BibSLEIGH — recommendation tag

recommendation

recommendation

Tag #recommendation

1554 papers:

EDM-2019-AiCGZWFW #concept #learning #online: Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System (FA, YC, YG, YZ, ZW, GF, GW).
EDM-2019-JacobsenS #exclamation #student: It's a Match! Reciprocal Recommender System for Graduating Students and Jobs (AJ, GS).
EDM-2019-KraussMA #learning #modelling: Smart Learning Object Recommendations based on Time-Dependent Learning Need Models (CK, AM, SA).
EDM-2019-Ma #design: Design of an Elective Course Recommendation System for University Environment (BM).
EDM-2019-MorsommeA #education: Content-based Course Recommender System for Liberal Arts Education (RM, SVA).
EDM-2019-PolyzouNK #framework #markov: Scholars Walk: A Markov Chain Framework for Course Recommendation (AP, ANN, GK).
ICPC-2019-0002KJCZ #contract: Recommending differentiated code to support smart contract update (YH0, QK, NJ, XC, ZZ), pp. 260–270.
ICPC-2019-SilvaR0SPM #mining #programming: Recommending comprehensive solutions for programming tasks by mining crowd knowledge (RFGdS, CKR, MMR0, KAS, KVRP, MdAM), pp. 358–368.
ICSME-2019-AnuCSH0Q #approach: An Approach to Recommendation of Verbosity Log Levels Based on Logging Intention (HA, JC, WS, JH, BL0, BQ), pp. 125–134.
ICSME-2019-GalsterTB #architecture #maintenance: Supporting Software Architecture Maintenance by Providing Task-Specific Recommendations (MG, CT, KB), pp. 370–372.
ICSME-2019-NguyenVN #personalisation: Personalized Code Recommendation (TN, PV, TN), pp. 313–317.
ICSME-2019-NguyenVN19a #exception: Recommending Exception Handling Code (TN, PV, TN), pp. 390–393.
MSR-2019-OliveiraOCF0 #energy #java: Recommending energy-efficient Java collections (WO, RO, FC, BF, GP0), pp. 160–170.
SANER-2019-KirinukiTN #named #testing #using #web: COLOR: Correct Locator Recommender for Broken Test Scripts using Various Clues in Web Application (HK, HT, KN), pp. 310–320.
SANER-2019-PachecoBMNMR #framework #mining #scala: Mining Scala Framework Extensions for Recommendation Patterns (YP, JDB, TM, DDN, WDM, CDR), pp. 514–523.
AIIDE-2019-MachadoGWNNT #design #evaluation #game studies: Evaluation of a Recommender System for Assisting Novice Game Designers (TM, DG, AW, ON, AN, JT), pp. 167–173.
CHI-PLAY-2019-HonauerMH #design #game studies #interactive: Interactive Soft Toys for Infants and Toddlers - Design Recommendations for Age-appropriate Play (MH, PM, EH), pp. 265–276.
CoG-2019-MachadoGNT #design #game studies: Pitako - Recommending Game Design Elements in Cicero (TM, DG, AN, JT), pp. 1–8.
FDG-2019-PirkerPK #game studies #interactive #social: Social interactions in game jams: a jammer recommender tool (JP, AP, JK), p. 4.
CIKM-2019-0001SKJ #named: Rehab-Path: Recommending Alcohol and Drug-free Routes (YZ0, PS, YK, AJ), pp. 2929–2932.
CIKM-2019-AnelliN: 2nd Workshop on Knowledge-aware and Conversational Recommender Systems - KaRS (VWA, TDN), pp. 3001–3002.
CIKM-2019-ChelliahZS #mining #multi #overview: Recommendation for Multi-stakeholders and through Neural Review Mining (MC, YZ, SS), pp. 2979–2981.
CIKM-2019-ChenAJC #bias: Correcting for Recency Bias in Job Recommendation (RCC, QA, GJ, WBC), pp. 2185–2188.
CIKM-2019-ChenCCR #network: A Dynamic Co-attention Network for Session-based Recommendation (WC, FC, HC, MdR), pp. 1461–1470.
CIKM-2019-ChenL: Data Poisoning Attacks on Cross-domain Recommendation (HC, JL), pp. 2177–2180.
CIKM-2019-Cohen-ShapiraRS #dataset #named #representation #visual notation: AutoGRD: Model Recommendation Through Graphical Dataset Representation (NCS, LR, BS, GK, RV), pp. 821–830.
CIKM-2019-GaoHWWWPC #named: STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints (JG, YH, YW, XW, JW, GP, XC), pp. 1903–1912.
CIKM-2019-HeWNC #self: A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists (YH, JW, WN, JC), pp. 1481–1490.
CIKM-2019-HuangLZYCGH #education #multi #online: Exploring Multi-Objective Exercise Recommendations in Online Education Systems (ZH, QL0, CZ, YY, EC, WG, GH), pp. 1261–1270.
CIKM-2019-JinCCHV #interactive #music #named: MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction (YJ, WC, LC, NNH, KV), pp. 951–960.
CIKM-2019-KangHLY #learning: Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users (SK, JH, DL, HY), pp. 1563–1572.
CIKM-2019-KangM #generative #scalability: Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation (WCK, JJM), pp. 1523–1532.
CIKM-2019-KangPKCC #modelling #topic #using: Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling (KK, JP, WK, HC, JC), pp. 1543–1552.
CIKM-2019-KrishnanCTS #approach #composition #social: A Modular Adversarial Approach to Social Recommendation (AK, HC, TC0, HS), pp. 1753–1762.
CIKM-2019-LaiSYHLY #multi: Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization (HCL, HHS, DNY, JLH, WCL, PSY), pp. 1773–1782.
CIKM-2019-LiGLCYN #graph #hashtag #network: Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network (ML, TG, ML, ZC, JY, LN), pp. 509–518.
CIKM-2019-LiJC0W #approach #hybrid #named: HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity (XL, WJ, WC, JW0, GW0), pp. 1503–1512.
CIKM-2019-LiLWXZHKCLL #multi #network: Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (CL, ZL, MW, YX, HZ, PH, GK, QC, WL, DLL), pp. 2615–2623.
CIKM-2019-LiuWJYZZ #named: In2Rec: Influence-based Interpretable Recommendation (HL, JW, LJ, JY, XZ0, MZ), pp. 1803–1812.
CIKM-2019-LvJYSLYN #named #online #scalability: SDM: Sequential Deep Matching Model for Online Large-scale Recommender System (FL, TJ, CY, FS, QL, KY, WN), pp. 2635–2643.
CIKM-2019-MaWZLLCYT0 #named: DBRec: Dual-Bridging Recommendation via Discovering Latent Groups (JM, JW, MZ, LL, CL, WC, YY, HT, XL0), pp. 1513–1522.
CIKM-2019-QiuLHY #graph #network #order: Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (RQ, JL, ZH, HY), pp. 579–588.
CIKM-2019-RaoSPJCTGK #evolution #learning: Learning to be Relevant: Evolution of a Course Recommendation System (SR, KS, GP, MJ, SC, VT, JG, DK), pp. 2625–2633.
CIKM-2019-ShiZYZHLM #adaptation: Adaptive Feature Sampling for Recommendation with Missing Content Feature Values (SS, MZ0, XY, YZ, BH, YL, SM), pp. 1451–1460.
CIKM-2019-SivrikayaAL #parametricity: International Workshop on Model Selection and Parameter Tuning in Recommender Systems (FS, SA, DL), pp. 2999–3000.
CIKM-2019-SongCZX #memory management #network: Session-based Recommendation with Hierarchical Memory Networks (BS, YC, WZ, CX), pp. 2181–2184.
CIKM-2019-SunLWPLOJ #bidirectional #named: BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (FS, JL, JW, CP, XL, WO, PJ), pp. 1441–1450.
CIKM-2019-Wang0C #graph #learning #reasoning: Learning and Reasoning on Graph for Recommendation (XW, XH0, TSC), pp. 2971–2972.
CIKM-2019-WangLZHG #e-commerce #interactive #named #network: QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation (PW, ZL, YZ, YH, LG), pp. 2329–2332.
CIKM-2019-WuLZQ #learning: Long- and Short-term Preference Learning for Next POI Recommendation (YW, KL, GZ, XQ), pp. 2301–2304.
CIKM-2019-WuWZJ #effectiveness #learning #performance: Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation (NW, JW, WXZ, YJ), pp. 1923–1932.
CIKM-2019-XiaWDZCC: Leveraging Ratings and Reviews with Gating Mechanism for Recommendation (HX, ZW, BD, LZ, SC, GC), pp. 1573–1582.
CIKM-2019-XueJLWZT #on-demand: A Spatio-temporal Recommender System for On-demand Cinemas (TX, BJ, BL, WW0, QZ, ST), pp. 1553–1562.
CIKM-2019-XuLHLX0 #e-commerce #graph #network #social: Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation (FX, JL, ZH, YL0, YX, XX0), pp. 529–538.
CIKM-2019-YanCKWM #2d #named #network: CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (AY, SC, WCK, MW, JJM), pp. 2173–2176.
CIKM-2019-YouVLL #multi #network: Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation (DY, NV, KL, QL), pp. 1471–1480.
CIKM-2019-ZhangC #visual notation: Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence (YZ, JC), pp. 1583–1592.
CIKM-2019-ZhangSTZLAZW0Y #on-demand #platform: Recommendation-based Team Formation for On-demand Taxi-calling Platforms (LZ, TS, YT, ZZ, DL, WA, LZ, GW, YL0, JY), pp. 59–68.
CIKM-2019-ZhaoLF: Cross-Domain Recommendation via Preference Propagation GraphNet (CZ, CL, CF), pp. 2165–2168.
CIKM-2019-ZhaoZSL #network: Motif Enhanced Recommendation over Heterogeneous Information Network (HZ, YZ, YS, DLL), pp. 2189–2192.
CIKM-2019-ZhuC0LZ #framework #named: DTCDR: A Framework for Dual-Target Cross-Domain Recommendation (FZ, CC, YW0, GL, XZ), pp. 1533–1542.
CIKM-2019-ZhuGLMOZWGC #adaptation #interactive: Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU (YZ, YG, QL, YM, WO, JZ, BW, ZG, DC), pp. 2585–2593.
ECIR-p1-2019-AnelliNSRT: Local Popularity and Time in top-N Recommendation (VWA, TDN, EDS, AR, JT), pp. 861–868.
ECIR-p1-2019-BorattoFM #algorithm #bias #online: The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses (LB, GF, MM), pp. 457–472.
ECIR-p1-2019-LandinVPB #modelling #named #probability: PRIN: A Probabilistic Recommender with Item Priors and Neural Models (AL, DV, JP, ÁB), pp. 133–147.
ECIR-p1-2019-ManotumruksaRMO #on the: On Cross-Domain Transfer in Venue Recommendation (JM, DR, CM, IO), pp. 443–456.
ECIR-p1-2019-OscheCB #multi #named: AntRS: Recommending Lists Through a Multi-objective Ant Colony System (PEO, SC, AB), pp. 229–243.
ECIR-p1-2019-Sanz-CruzadoC #information retrieval #modelling #network #social: Information Retrieval Models for Contact Recommendation in Social Networks (JSC, PC), pp. 148–163.
ECIR-p1-2019-WangOM #analysis #comparison #sentiment: Comparison of Sentiment Analysis and User Ratings in Venue Recommendation (XW, IO, CM), pp. 215–228.
ECIR-p2-2019-BeelCKDK #online: Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations (JB, AC0, OK, LWD, PK), pp. 213–219.
ECIR-p2-2019-KanagawaKSTS #adaptation: Cross-Domain Recommendation via Deep Domain Adaptation (HK, HK, NS, YT, TS), pp. 20–29.
ECIR-p2-2019-Landin #learning: Learning User and Item Representations for Recommender Systems (AL), pp. 337–342.
ECIR-p2-2019-MaityPGBGM #framework #named #stack overflow: DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow (SKM, AP, SG0, AB, PG, AM0), pp. 125–131.
ECIR-p2-2019-VermaGMC: Heterogeneous Edge Embedding for Friend Recommendation (JV, SG, DM, TC0), pp. 172–179.
ICML-2019-Chen0LJQS #generative #learning: Generative Adversarial User Model for Reinforcement Learning Based Recommendation System (XC, SL0, HL, SJ, YQ, LS), pp. 1052–1061.
ICML-2019-MannGGHJLS #learning: Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems (TAM, SG, AG, HH, RJ, BL, PS), pp. 4324–4332.
ICML-2019-WangZ0Q #learning #random #robust: Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random (XW, RZ0, YS0, JQ0), pp. 6638–6647.
KDD-2019-0009ZGZNQH #framework #graph #named #scalability: IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation (JZ0, ZZ, ZG, WZ0, WN, GQ, XH), pp. 2347–2357.
KDD-2019-AmelkinS #network #social: Fighting Opinion Control in Social Networks via Link Recommendation (VA, AKS), pp. 677–685.
KDD-2019-BeutelCDQWWHZHC #ranking: Fairness in Recommendation Ranking through Pairwise Comparisons (AB, JC, TD, HQ, LW, YW, LH, ZZ, LH, EHC, CG), pp. 2212–2220.
KDD-2019-ChenC0GLLW #modelling #named: λOpt: Learn to Regularize Recommender Models in Finer Levels (YC, BC, XH0, CG, YL0, JGL, YW), pp. 978–986.
KDD-2019-ChenHXGGSLPZZ #generative #named #personalisation: POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion (WC, PH, JX, XG, CG, FS, CL, AP, HZ, BZ), pp. 2662–2670.
KDD-2019-ChenSJ0Z0 #behaviour #effectiveness #performance #reuse #using: Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation (LC, SS, CSJ, BY0, ZZ, LS0), pp. 488–498.
KDD-2019-Ding0LXZSJS #realtime: Infer Implicit Contexts in Real-time Online-to-Offline Recommendation (XD, JT0, TXL, CX, YZ, FS, QJ, DS), pp. 2336–2346.
KDD-2019-DuWYZT #online: Sequential Scenario-Specific Meta Learner for Online Recommendation (ZD, XW, HY, JZ, JT0), pp. 2895–2904.
KDD-2019-FanZHSHML #graph #network: Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (SF, JZ, XH, CS, LH, BM, YL), pp. 2478–2486.
KDD-2019-GeyikAK #ranking: Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search (SCG, SA, KK), pp. 2221–2231.
KDD-2019-GongZDLGSOZ #clique #optimisation: Exact-K Recommendation via Maximal Clique Optimization (YG, YZ, LD, QL, ZG, FS, WO, KQZ), pp. 617–626.
KDD-2019-GuoGSLZCA #natural language: Deep Natural Language Processing for Search and Recommender Systems (WG, HG, JS, BL, LZ, BCC, DA), pp. 3199–3200.
KDD-2019-GuoYWCZH #streaming: Streaming Session-based Recommendation (LG0, HY, QW, TC, AZ, NQVH), pp. 1569–1577.
KDD-2019-HanNCLHX #approach #generative: A Deep Generative Approach to Search Extrapolation and Recommendation (FXH, DN, HC, KL, YH, YX), pp. 1771–1779.
KDD-2019-LeeIJCC #named: MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (HL, JI, SJ, HC, SC), pp. 1073–1082.
KDD-2019-LiuGZL #realtime: Real-time Attention Based Look-alike Model for Recommender System (YL, KG, XZ, LL), pp. 2765–2773.
KDD-2019-LiuLDCG #learning #named: DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDD-2019-LiuTZLDX #multi #named #personalisation: Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System (HL, YT, PZ, XL, JD, HX), pp. 2314–2324.
KDD-2019-MaKL #network: Hierarchical Gating Networks for Sequential Recommendation (CM, PK, XL), pp. 825–833.
KDD-2019-OchiaiSYTF #realtime #smarttech: Real-time On-Device Troubleshooting Recommendation for Smartphones (KO, KS, NY, YT, YF), pp. 2783–2791.
KDD-2019-QinZZXZMZX #named #personalisation #perspective: DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview (CQ, HZ, CZ, TX, FZ, CM, JZ, HX), pp. 2165–2173.
KDD-2019-ShangYLQMY #learning #re-engineering: Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDD-2019-TangWYS #named: AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation (XT, TW, HY, HS), pp. 1891–1899.
KDD-2019-Wang00LC #graph #named #network: KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
KDD-2019-Wang0L #social: Social Recommendation with Optimal Limited Attention (XW0, WZ0, CL), pp. 1518–1527.
KDD-2019-WangWZPL #algorithm #network #personalisation: Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation (JW, NW, WXZ, FP, XL), pp. 539–547.
KDD-2019-WangZZLZLW #graph #network: Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (HW, FZ, MZ, JL, MZ, WL, ZW), pp. 968–977.
KDD-2019-WarlopMG #process: Tensorized Determinantal Point Processes for Recommendation (RW, JM, MG), pp. 1605–1615.
KDD-2019-WuGGWC #modelling #predict: Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination (QW, YG, XG, PW, GC), pp. 447–457.
KDD-2019-WuWAHHX #named #personalisation: NPA: Neural News Recommendation with Personalized Attention (CW, FW, MA, JH, YH, XX0), pp. 2576–2584.
KDD-2019-XiaoZPSZ0 #personalisation #similarity #social: Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction (WX, HZ, HP, YS, VWZ, QY0), pp. 235–245.
KDD-2019-YuSJ #interactive #visual notation: A Visual Dialog Augmented Interactive Recommender System (TY, YS, HJ), pp. 157–165.
KDD-2019-ZhouMZ #memory management #network #personalisation #topic: Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation (XZ, CM, ZZ), pp. 3018–3028.
KDD-2019-ZouXDS0Y #learning: Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
OOPSLA-2019-LuanYBS0 #code search #named: Aroma: code recommendation via structural code search (SL, DY, CB, KS, SC0), p. 28.
ASE-2019-JiangLJ #how #machine learning: Machine Learning Based Recommendation of Method Names: How Far are We (LJ, HL, HJ), pp. 602–614.
ESEC-FSE-2019-Abid #api: Recommending related functions from API usage-based function clone structures (SA), pp. 1193–1195.
ESEC-FSE-2019-CaiWH0X0 #api #named: BIKER: a tool for Bi-information source based API method recommendation (LC, HW, QH, XX0, ZX, DL0), pp. 1075–1079.
ICSE-2019-NguyenRRODP #api #mining #named: FOCUS: a recommender system for mining API function calls and usage patterns (PTN, JDR, DDR, LO, TD, MDP), pp. 1050–1060.
ICSA-2018-BhatSKHBM #design #question: An Expert Recommendation System for Design Decision Making: Who Should be Involved in Making a Design Decision? (MB, KS, KK, UH, AB, FM), pp. 85–94.
JCDL-2018-KobayashiS0 #distributed #representation #using: Citation Recommendation Using Distributed Representation of Discourse Facets in Scientific Articles (YK, MS, YM0), pp. 243–251.
EDM-2018-EstebanGR #algorithm #approach #hybrid #multi #search-based #student #using: A Hybrid Multi-Criteria approach using a Genetic Algorithm for Recommending Courses to University Students (AE, AZG, CR).
ICPC-2018-PantiuchinaBTP #refactoring #towards: Towards just-in-time refactoring recommenders (JP, GB, MT, DP), pp. 312–315.
ICPC-2018-ParraEH #automation #development #video: Automatic tag recommendation for software development video tutorials (EP, JEA, SH), pp. 222–232.
ICPC-2018-ZhangLXJS #debugging: Recommending frequently encountered bugs (YZ0, DL0, XX0, JJ0, JS), pp. 120–131.
ICSME-2018-YueGMXWM #automation #refactoring: Automatic Clone Recommendation for Refactoring Based on the Present and the Past (RY, ZG, NM, YX, XW, JDM), pp. 115–126.
MSR-2018-JinS #code completion #comprehension #performance: The hidden cost of code completion: understanding the impact of the recommendation-list length on its efficiency (XJ, FS), pp. 70–73.
SCAM-2018-PughBM #adaptation #research: [Research Paper] The Case for Adaptive Change Recommendation (SP, DWB, LM), pp. 129–138.
FM-2018-NellenRWAK #challenge #empirical #evaluation #modelling #verification: Formal Verification of Automotive Simulink Controller Models: Empirical Technical Challenges, Evaluation and Recommendations (JN, TR, MTBW, EÁ, JPK), pp. 382–398.
AIIDE-2018-SifaYRB #architecture #bottom-up #comparative #evaluation #game studies #matrix #online: Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation (RS, RY, RR, CB), pp. 102–108.
CIG-2018-BertensGCP #game studies #video: A Machine-Learning Item Recommendation System for Video Games (PB, AG, PPC, AP), pp. 1–4.
CIG-2018-ChenANCSE #game studies #named #performance: Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games (ZC, CA, THDN, SC, YS, MSEN), pp. 1–8.
CIKM-2018-ChenFEZC0 #modelling #social: Modeling Users' Exposure with Social Knowledge Influence and Consumption Influence for Recommendation (JC, YF, ME, SZ, CC0, CW0), pp. 953–962.
CIKM-2018-ChinZJC #named: ANR: Aspect-based Neural Recommender (JYC, KZ0, SRJ, GC), pp. 147–156.
CIKM-2018-DaveZHAK #approach #learning #representation: A Combined Representation Learning Approach for Better Job and Skill Recommendation (VSD, BZ, MAH, KA, MK), pp. 1997–2005.
CIKM-2018-HidasiK #network: Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (BH, AK), pp. 843–852.
CIKM-2018-HuSZY #network: Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network (BH, CS, WXZ, TY), pp. 1683–1686.
CIKM-2018-HuZY #collaboration #named #network: CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (GH, YZ, QY), pp. 667–676.
CIKM-2018-KangWM: Recommendation Through Mixtures of Heterogeneous Item Relationships (WCK, MW, JJM), pp. 1143–1152.
CIKM-2018-KhattarKV0 #hybrid #named: HRAM: A Hybrid Recurrent Attention Machine for News Recommendation (DK, VK, VV, MG0), pp. 1619–1622.
CIKM-2018-KhattarKV018a #3d #network #word: Weave&Rec: A Word Embedding based 3-D Convolutional Network for News Recommendation (DK, VK, VV, MG0), pp. 1855–1858.
CIKM-2018-LinGL #social: Recommender Systems with Characterized Social Regularization (THL, CG, YL0), pp. 1767–1770.
CIKM-2018-MafrurSK #data analysis #named #visual notation: DiVE: Diversifying View Recommendation for Visual Data Exploration (RM, MAS, HAK), pp. 1123–1132.
CIKM-2018-MaZWL #self: Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence (CM, YZ, QW, XL), pp. 697–706.
CIKM-2018-MehrotraMBL0 #evaluation #towards #trade-off: Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems (RM, JM, HB, ML, FD0), pp. 2243–2251.
CIKM-2018-MeiRCNMN #interactive #network: An Attentive Interaction Network for Context-aware Recommendations (LM, PR, ZC, LN, JM0, JYN), pp. 157–166.
CIKM-2018-PandeyKS #learning #using: Recommending Serendipitous Items using Transfer Learning (GP0, DK, AS), pp. 1771–1774.
CIKM-2018-Shi0LM #adaptation: Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation (SS, MZ0, YL, SM), pp. 127–136.
CIKM-2018-TranLL0 #matrix: Regularizing Matrix Factorization with User and Item Embeddings for Recommendation (TT, KL, YL, DL0), pp. 687–696.
CIKM-2018-VainshteinGKSR #approach #automation #hybrid: A Hybrid Approach for Automatic Model Recommendation (RV, AGM, GK, BS, LR), pp. 1623–1626.
CIKM-2018-Wang0C #modelling #overview: Word-Driven and Context-Aware Review Modeling for Recommendation (QW, SL0, GC), pp. 1859–1862.
CIKM-2018-WangCZLL: Variational Recurrent Model for Session-based Recommendation (ZW, CC, KZ, YL, WL), pp. 1839–1842.
CIKM-2018-WangGEGSZBC #internet: Device-Aware Rule Recommendation for the Internet of Things (BW, XG0, ME, ZG, BS, YZ, JB, DC), pp. 2037–2045.
CIKM-2018-WangZWZLXG #graph #named: RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (HW, FZ, JW, MZ, WL, XX, MG), pp. 417–426.
CIKM-2018-WanWLBM #representation: Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty (MW, DW, JL0, PB, JJM), pp. 1133–1142.
CIKM-2018-WilhelmRBJCG #process: Practical Diversified Recommendations on YouTube with Determinantal Point Processes (MW, AR, AB, SJ, EHC, JG), pp. 2165–2173.
CIKM-2018-XiaJSZWS #learning #modelling #multi: Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning (QX, PJ, FS, YZ, XW, ZS), pp. 1703–1706.
CIKM-2018-Yu0LYL #adaptation #identification #network #social: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (JY, MG0, JL, HY, HL0), pp. 357–366.
CIKM-2018-ZhangCA0C #towards: Towards Conversational Search and Recommendation: System Ask, User Respond (YZ, XC, QA, LY0, WBC), pp. 177–186.
CIKM-2018-ZhuHC: Fairness-Aware Tensor-Based Recommendation (ZZ, XH, JC), pp. 1153–1162.
ECIR-2018-0001TJ18a #named: CITEWERTs: A System Combining Cite-Worthiness with Citation Recommendation (MF0, AT, AJ), pp. 815–819.
ECIR-2018-GuptaSP #modelling #music: Explicit Modelling of the Implicit Short Term User Preferences for Music Recommendation (KG, NS, VP), pp. 333–344.
ECIR-2018-JiaS #approach #performance: Local Is Good: A Fast Citation Recommendation Approach (HJ, ES), pp. 758–764.
ECIR-2018-SanchezB #metric: Time-Aware Novelty Metrics for Recommender Systems (PS0, AB), pp. 357–370.
ECIR-2018-SinghM #symmetry #using: Benefits of Using Symmetric Loss in Recommender Systems (GS, SM), pp. 345–356.
ICPR-2018-GarciaLSH #classification #complexity #metric #using: Classifier Recommendation Using Data Complexity Measures (LPFG, ACL, MCPdS, TKH), pp. 874–879.
KDD-2018-0001C #performance #probability: Efficient Attribute Recommendation with Probabilistic Guarantee (CW0, KC), pp. 2387–2396.
KDD-2018-BhagatMLV #modelling: Buy It Again: Modeling Repeat Purchase Recommendations (RB, SM, AL, SV), pp. 62–70.
KDD-2018-CardosoDV #learning #personalisation #semistructured data #towards: Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products (ÂC, FD, SV), pp. 80–89.
KDD-2018-Chen0DTHT #learning #online: Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation (SYC, YY0, QD, JT, HKH, HHT), pp. 1187–1196.
KDD-2018-Christakopoulou #approach #interactive #towards: Q&R: A Two-Stage Approach toward Interactive Recommendation (KC, AB, RL, SJ, EHC), pp. 139–148.
KDD-2018-Christakopoulou18a #modelling: Local Latent Space Models for Top-N Recommendation (EC, GK), pp. 1235–1243.
KDD-2018-GargR #exclamation: Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer! (NG, SR), pp. 1425–1434.
KDD-2018-HuSZY: Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model (BH, CS, WXZ, PSY), pp. 1531–1540.
KDD-2018-Kokkodis #online: Dynamic Recommendations for Sequential Hiring Decisions in Online Labor Markets (MK), pp. 453–461.
KDD-2018-LianZZCXS #feature model #interactive #named: xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (JL, XZ, FZ, ZC, XX0, GS), pp. 1754–1763.
KDD-2018-LiuXC: Context-aware Academic Collaborator Recommendation (ZL0, XX, LC0), pp. 1870–1879.
KDD-2018-LiuZMZ #memory management #named: STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation (QL, YZ, RM, HZ), pp. 1831–1839.
KDD-2018-LiZLHMC #behaviour #learning: Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors (ZL, HZ, QL0, ZH, TM, EC), pp. 1734–1743.
KDD-2018-PeakeW #mining #modelling: Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (GP, JW), pp. 2060–2069.
KDD-2018-Raghavan #community #realtime: Building Near Realtime Contextual Recommendations for Active Communities on LinkedIn (HR), p. 2874.
KDD-2018-TangW #learning #modelling #performance #ranking: Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System (JT, KW), pp. 2289–2298.
KDD-2018-TayLH #multi #network: Multi-Pointer Co-Attention Networks for Recommendation (YT, ATL, SCH), pp. 2309–2318.
KDD-2018-WangHZZZL #e-commerce: Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (JW, PH, HZ, ZZ, BZ, DLL), pp. 839–848.
KDD-2018-WangYHLWH #memory management #network #streaming: Neural Memory Streaming Recommender Networks with Adversarial Training (QW, HY, ZH, DL, HW, ZH), pp. 2467–2475.
KDD-2018-WangZHZ #learning #network: Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (LW, WZ0, XH, HZ), pp. 2447–2456.
KDD-2018-YeZXZGD #mobile #paradigm #parallel #performance: Multi-User Mobile Sequential Recommendation: An Efficient Parallel Computing Paradigm (ZY, LZ, KX, WZ, YG, YD), pp. 2624–2633.
KDD-2018-YingHCEHL #graph #network: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (RY, RH, KC, PE, WLH, JL), pp. 974–983.
KDD-2018-ZhaoZDXTY #feedback #learning: Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning (XZ, LZ, ZD, LX, JT, DY), pp. 1040–1048.
KDD-2018-ZhuLZLHLG #learning: Learning Tree-based Deep Model for Recommender Systems (HZ, XL, PZ, GL, JH, HL, KG), pp. 1079–1088.
MoDELS-2018-MokaddemSS #refactoring: Recommending Model Refactoring Rules from Refactoring Examples (CeM, HAS, ES), pp. 257–266.
ASE-2018-HassanRW #analysis #named: RUDSEA: recommending updates of Dockerfiles via software environment analysis (FH, RR, XW), pp. 796–801.
ASE-2018-HuangXXLW #api: API method recommendation without worrying about the task-API knowledge gap (QH, XX0, ZX, DL0, XW0), pp. 293–304.
ASE-2018-LiuHN #api #effectiveness #repository: Effective API recommendation without historical software repositories (XL, LH, VN), pp. 282–292.
ASE-2018-NagashimaH #higher-order #named #proving: PaMpeR: proof method recommendation system for Isabelle/HOL (YN, YH), pp. 362–372.
ASE-2018-Ye0WW #crowdsourcing #developer #personalisation: Personalized teammate recommendation for crowdsourced software developers (LY, HS0, XW, JW), pp. 808–813.
ICSE-2018-BarbosaG #exception: Global-aware recommendations for repairing violations in exception handling (EAB, AG), p. 858.
CASE-2018-LuoZ #matrix: Unconstrained Non-negative Factorization of High-dimensional and Sparse Matrices in Recommender Systems (XL0, MZ), pp. 1406–1413.
CSEET-2017-BollinRSSS #case study #experience #maturity #re-engineering: Applying a Maturity Model during a Software Engineering Course - Experiences and Recommendations (AB, ER, CS, VS, RS), pp. 9–18.
EDM-2017-KhosraviCK #named: RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests (HK, KMLC, KK).
EDM-2017-MiF #adaptation #using: Adaptive Sequential Recommendation for Discussion Forums on MOOCs using Context Trees (FM, BF).
ICPC-2017-ZhangCJLX #automation #debugging: Bug report enrichment with application of automated fixer recommendation (TZ0, JC, HJ, XL, XX0), pp. 230–240.
ICSME-2017-AsaduzzamanRSH #framework: Recommending Framework Extension Examples (MA, CKR, KAS, DH), pp. 456–466.
ICSME-2017-XuSHL #git #named #personalisation: REPERSP: Recommending Personalized Software Projects on GitHub (WX, XS, JH, BL0), pp. 648–652.
ICSME-2017-ZampettiNAKP #design #self #technical debt: Recommending when Design Technical Debt Should be Self-Admitted (FZ, CN, GA, FK, MDP), pp. 216–226.
SANER-2017-GhafariM #framework #source code: A framework for classifying and comparing source code recommendation systems (MG, HM), pp. 555–556.
SANER-2017-SantosPAEMD #source code: Recommending source code locations for system specific transformations (GS, KVRP, NA, AE, MdAM, SD), pp. 160–170.
SANER-2017-ZhouLYZ #scalability: Scalable tag recommendation for software information sites (PZ, JL0, ZY, GZ), pp. 272–282.
SCAM-2017-WangPXFZ #feature model #interactive #process: Contextual Recommendation of Relevant Program Elements in an Interactive Feature Location Process (JW, XP0, ZX, KF, WZ), pp. 61–70.
IFM-2017-RahmanB #health #verification: Formal Verification of CNL Health Recommendations (FR, JKFB), pp. 357–371.
AIIDE-2017-HankeC #game studies: A Recommender System for Hero Line-Ups in MOBA Games (LH, LC), pp. 43–49.
AIIDE-2017-PadovaniFL #game studies #music #named: Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games (RRP, LNF, LHSL), pp. 214–220.
CIKM-2017-Christakopoulou #capacity #constraints: Recommendation with Capacity Constraints (KC, JK, AB), pp. 1439–1448.
CIKM-2017-DingLHM #fault tolerance #performance #using: Efficient Fault-Tolerant Group Recommendation Using alpha-beta-core (DD, HL, ZH0, NM), pp. 2047–2050.
CIKM-2017-DingZLTCZ #named #network #personalisation #ranking: BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network (DD, MZ0, SYL, JT0, XC, ZHZ), pp. 1479–1488.
CIKM-2017-GyselMVRKGC #email: Reply With: Proactive Recommendation of Email Attachments (CVG, BM, MV, RR, GK, PG, NC), pp. 327–336.
CIKM-2017-HalderKS #concurrent #health #thread #topic #using: Health Forum Thread Recommendation Using an Interest Aware Topic Model (KH, MYK, KS), pp. 1589–1598.
CIKM-2017-LeL #performance #personalisation #ranking: Indexable Bayesian Personalized Ranking for Efficient Top-k Recommendation (DDL, HWL), pp. 1389–1398.
CIKM-2017-LiCY #graph #learning: Learning Graph-based Embedding For Time-Aware Product Recommendation (YL, WC, HY), pp. 2163–2166.
CIKM-2017-LiRCRLM: Neural Attentive Session-based Recommendation (JL, PR, ZC, ZR, TL, JM0), pp. 1419–1428.
CIKM-2017-LiTZYW #learning #representation: Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation (YL, GT, WZ0, YY0, JW0), pp. 1657–1665.
CIKM-2017-ManotumruksaMO #collaboration #framework: A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation (JM, CM, IO), pp. 1429–1438.
CIKM-2017-ManotumruksaMO17a #framework #multi #personalisation #ranking: A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation (JM, CM, IO), pp. 1469–1478.
CIKM-2017-NiuZ #collaboration #predict #sequence: Collaborative Sequence Prediction for Sequential Recommender (SN, RZ), pp. 2239–2242.
CIKM-2017-ParkLC #network: Deep Neural Networks for News Recommendations (KP, JL, JC), pp. 2255–2258.
CIKM-2017-PeiYSZBT #network: Interacting Attention-gated Recurrent Networks for Recommendation (WP, JY0, ZS, JZ0, AB, DMJT), pp. 1459–1468.
CIKM-2017-RafailidisC #collaboration #ranking: A Collaborative Ranking Model for Cross-Domain Recommendations (DR, FC), pp. 2263–2266.
CIKM-2017-Roy: An Improved Test Collection and Baselines for Bibliographic Citation Recommendation (DR), pp. 2271–2274.
CIKM-2017-WangFTH #behaviour #modelling #topic #visual notation: Improving the Gain of Visual Perceptual Behaviour on Topic Modeling for Text Recommendation (CW, YF, ZT, YH), pp. 2339–2342.
CIKM-2017-WangHLE #interactive #social: Interactive Social Recommendation (XW0, SCHH, CL, ME), pp. 357–366.
CIKM-2017-WangWZCG #online #semantics #social #topic: Joint Topic-Semantic-aware Social Recommendation for Online Voting (HW0, JW, MZ, JC, MG), pp. 347–356.
CIKM-2017-WuWHS #optimisation: Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems (QW, HW, LH, YS), pp. 1927–1936.
CIKM-2017-WuY #e-commerce: Session-aware Information Embedding for E-commerce Product Recommendation (CW, MY), pp. 2379–2382.
CIKM-2017-XiaoMZLM #learning #personalisation #social: Learning and Transferring Social and Item Visibilities for Personalized Recommendation (XL0, MZ0, YZ, YL, SM), pp. 337–346.
CIKM-2017-YuCY #algebra #finite #matrix #rank: Low-Rank Matrix Completion over Finite Abelian Group Algebras for Context-Aware Recommendation (CAY, TSC, YHY), pp. 2415–2418.
CIKM-2017-ZhangACC #learning #representation: Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (YZ, QA, XC, WBC), pp. 1449–1458.
CIKM-2017-ZhuZHWZZY #collaboration #learning #multi: Broad Learning based Multi-Source Collaborative Recommendation (JZ, JZ, LH0, QW, BZ0, CZ, PSY), pp. 1409–1418.
ECIR-2017-Anand0D #named: FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation (AA, TC0, AD), pp. 753–757.
ECIR-2017-FeyerSGAB #integration: Integration of the Scientific Recommender System Mr. DLib into the Reference Manager JabRef (SF, SS, BG, AA, JB), pp. 770–774.
ECIR-2017-Recalde #framework #set #social: A Social Framework for Set Recommendation in Group Recommender Systems (LR), pp. 735–743.
ECIR-2017-ZagheliAS #feedback #framework #modelling: Negative Feedback in the Language Modeling Framework for Text Recommendation (HRZ, MA, AS), pp. 662–668.
KDD-2017-Antikacioglu0: Post Processing Recommender Systems for Diversity (AA, RR0), pp. 707–716.
KDD-2017-Bauman0T #aspect-oriented: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews (KB, BL0, AT), pp. 717–725.
KDD-2017-FuLTA #integer #programming: Unsupervised P2P Rental Recommendations via Integer Programming (YF, GL, MT, CCA), pp. 165–173.
KDD-2017-HosseiniAKAFZR: Recurrent Poisson Factorization for Temporal Recommendation (SAH, KA, AK, AA, MF, HZ, HRR), pp. 847–855.
KDD-2017-LiS #collaboration: Collaborative Variational Autoencoder for Recommender Systems (XL, JS), pp. 305–314.
KDD-2017-OkuraTOT: Embedding-based News Recommendation for Millions of Users (SO, YT, SO, AT), pp. 1933–1942.
KDD-2017-WangFWYDX #sentiment: A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users (HW0, YF, QW, HY, CD, HX), pp. 1135–1143.
KDD-2017-WangYRTZYW #editing #learning: Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (XW, LY, KR, GT, WZ0, YY0, JW0), pp. 2051–2059.
KDD-2017-Xu0TTL #distance #higher-order #named #optimisation #rating: HoORaYs: High-order Optimization of Rating Distance for Recommender Systems (JX0, YY0, HT, XT, JL0), pp. 525–534.
KDD-2017-YangBZY0 #approach #collaboration #learning: Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (CY, LB, CZ0, QY0, JH0), pp. 1245–1254.
KDD-2017-YangDSZFXBM #data-driven #framework #process: A Data-driven Process Recommender Framework (SY, XD, LS, YZ, RAF, HX, RSB, IM), pp. 2111–2120.
KDD-2017-ZhaoYLSL #network: Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (HZ, QY, JL, YS, DLL), pp. 635–644.
MoDELS-2017-BatotKSF #heuristic #metamodelling #ocl: Heuristic-Based Recommendation for Metamodel — OCL Coevolution (EB, WK, HAS, MF), pp. 210–220.
ASE-2017-AsaduzzamanRSH #framework #named: FEMIR: a tool for recommending framework extension examples (MA, CKR, KAS, DH), pp. 967–972.
ASE-2017-GasparicG0 #development #ide: Context-aware integrated development environment command recommender systems (MG, TG, FR0), pp. 688–693.
ASE-2017-RolfsnesMB #predict: Predicting relevance of change recommendations (TR, LM, DWB), pp. 694–705.
ASE-2017-WangSFY #crowdsourcing #developer: Recommending crowdsourced software developers in consideration of skill improvement (ZW, HS0, YF, LY), pp. 717–722.
ESEC-FSE-2017-DotzlerKKP: More accurate recommendations for method-level changes (GD, MK, PK, MP), pp. 798–808.
ESEC-FSE-2017-Kogel #development #modelling: Recommender system for model driven software development (SK), pp. 1026–1029.
ICSE-2017-PalombaSCPGFL #mobile: Recommending and localizing change requests for mobile apps based on user reviews (FP, PS, AC, SP, HCG, FF, ADL), pp. 106–117.
ICSE-2017-PonzanelliSBMOP #developer: Supporting software developers with a holistic recommender system (LP, SS, GB, AM, RO, MDP, ML), pp. 94–105.
JCDL-2016-NishiokaS #matter #profiling #question #twitter #what: Profiling vs. Time vs. Content: What does Matter for Top-k Publication Recommendation based on Twitter Profiles? (CN, AS), pp. 171–180.
JCDL-2016-SchwarzerSMBMG #wiki: Evaluating Link-based Recommendations for Wikipedia (MS, MS, NM, CB, VM, BG), pp. 191–200.
EDM-2016-Bydzovska16a: Course Enrollment Recommender System (HB), pp. 312–317.
EDM-2016-DaiAY #analysis #learning #towards: Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners (YD, YA, MY), pp. 347–352.
EDM-2016-LabartheBBY #question #student: Does a Peer Recommender Foster Students' Engagement in MOOCs? (HL, FB, RB, KY), pp. 418–423.
EDM-2016-SweeneyLRJ #approach #performance #predict #student: Next-Term Student Performance Prediction: A Recommender Systems Approach (MS, JL, HR, AJ), p. 7.
ICPC-2016-TianWLG #debugging #learning #rank: Learning to rank for bug report assignee recommendation (YT0, DW, DL0, CLG), pp. 1–10.
ICSME-2016-0001KI #code review #overview #perspective #search-based: Search-Based Peer Reviewers Recommendation in Modern Code Review (AO0, RGK, KI), pp. 367–377.
ICSME-2016-AlghmadiSSH #approach #automation #performance #testing: An Automated Approach for Recommending When to Stop Performance Tests (HMA, MDS, WS, AEH), pp. 279–289.
ICSME-2016-ThungLLL #automation #linux: Recommending Code Changes for Automatic Backporting of Linux Device Drivers (FT, XBDL, DL0, JLL), pp. 222–232.
MSR-2016-DiamantopoulosT #component #named #source code #usability: QualBoa: reusability-aware recommendations of source code components (TGD, KT, ALS), pp. 488–491.
MSR-2016-RolfsnesMABB #using: Improving change recommendation using aggregated association rules (TR, LM, SDA, RB, DWB), pp. 73–84.
SANER-2016-KarimKP #android #mining: Mining Android Apps to Recommend Permissions (MYK, HHK, MDP), pp. 427–437.
SANER-2016-RahmanRL #api #automation #crowdsourcing #named #using: RACK: Automatic API Recommendation Using Crowdsourced Knowledge (MMR0, CKR, DL0), pp. 349–359.
SANER-2016-RochaVMM #debugging #empirical: An Empirical Study on Recommendations of Similar Bugs (HR, MTV, HMN, GCM), pp. 46–56.
SCAM-2016-DSouzaYL #api #python: Collective Intelligence for Smarter API Recommendations in Python (ARD, DY, CVL), pp. 51–60.
CIKM-2016-ChenDWST #data mining #mining: From Recommendation to Profile Inference (Rec2PI): A Value-added Service to Wi-Fi Data Mining (CC0, FD, KW0, VS0, AT), pp. 1503–1512.
CIKM-2016-ChenNLXA #feedback #modelling #scalability: Separating-Plane Factorization Models: Scalable Recommendation from One-Class Implicit Feedback (HC, DN, KL, YX, MA), pp. 669–678.
CIKM-2016-ChenOX #learning: Learning Points and Routes to Recommend Trajectories (DC, CSO, LX), pp. 2227–2232.
CIKM-2016-Fernandez-Tobias #web: Memory-based Recommendations of Entities for Web Search Users (IFT, RB), pp. 35–44.
CIKM-2016-GaoWQZYH #collaboration #social: Collaborative Social Group Influence for Event Recommendation (LG, JW0, ZQ, CZ0, HY, YH0), pp. 1941–1944.
CIKM-2016-HuangCCZ #query: KB-Enabled Query Recommendation for Long-Tail Queries (ZH0, BC, RC, YZ), pp. 2107–2112.
CIKM-2016-KangPYC #graph: Top-N Recommendation on Graphs (ZK, CP, MY, QC), pp. 2101–2106.
CIKM-2016-LiSNLF #hashtag #learning #rank #topic #twitter: Hashtag Recommendation Based on Topic Enhanced Embedding, Tweet Entity Data and Learning to Rank (QL, SS, AN, XL, RF), pp. 2085–2088.
CIKM-2016-LuCL #mining #topic #video: Scarce Feature Topic Mining for Video Recommendation (WL0, KFLC, KL), pp. 1993–1996.
CIKM-2016-LuQZHG #approach #network #social: Location-aware Friend Recommendation in Event-based Social Networks: A Bayesian Latent Factor Approach (YL, ZQ, CZ0, YH0, LG0), pp. 1957–1960.
CIKM-2016-MahajanKBPSKG #enterprise #hashtag: Hashtag Recommendation for Enterprise Applications (DM, VK, CB, SP, SS, SSK, JG), pp. 893–902.
CIKM-2016-ManotumruksaMO #modelling #using: Regularising Factorised Models for Venue Recommendation using Friends and their Comments (JM, CM, IO), pp. 1981–1984.
CIKM-2016-PiaoB #concept #modelling #personalisation #twitter: User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations (GP, JGB), pp. 2057–2060.
CIKM-2016-RafailidisC #collaboration #ranking #social: Joint Collaborative Ranking with Social Relationships in Top-N Recommendation (DR, FC), pp. 1393–1402.
CIKM-2016-SoulierTN #collaboration #social #twitter: Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration (LS, LT, GHN), pp. 267–276.
CIKM-2016-SubbianAH #streaming: Recommendations For Streaming Data (KS, CCA, KH), pp. 2185–2190.
CIKM-2016-TanWX #approach #network: A Neural Network Approach to Quote Recommendation in Writings (JT, XW0, JX), pp. 65–74.
CIKM-2016-WangLCLV #personalisation: Improving Personalized Trip Recommendation by Avoiding Crowds (XW, CL, JC, KHL0, TV), pp. 25–34.
CIKM-2016-WangLEWC #social: Social Recommendation with Strong and Weak Ties (XW0, WL, ME, CW0, CC0), pp. 5–14.
CIKM-2016-WangLL: Improving Advertisement Recommendation by Enriching User Browser Cookie Attributes (LW, KcL, QL), pp. 2401–2404.
CIKM-2016-Wu0XTL #named #using #word: Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation (YW, YY0, FX0, HT, JL0), pp. 2287–2292.
CIKM-2016-XieYWXCW #graph #learning: Learning Graph-based POI Embedding for Location-based Recommendation (MX, HY, HW, FX, WC, SW), pp. 15–24.
CIKM-2016-XuCLMM #personalisation #semantics #similarity #using: Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling (ZX, CC0, TL, YM, XM), pp. 1921–1924.
CIKM-2016-ZhangWYLLZ #social: Global and Local Influence-based Social Recommendation (QZ, JW0, HY, WL, GL, CZ), pp. 1917–1920.
ECIR-2016-Boratto #aspect-oriented #challenge #research #state of the art: Group Recommender Systems: State of the Art, Emerging Aspects and Techniques, and Research Challenges (LB), pp. 889–892.
ECIR-2016-LinOLVVKP #facebook: A Business Zone Recommender System Based on Facebook and Urban Planning Data (JL, RJO, EPL, CV, AV, ATK, PKP), pp. 641–647.
ECIR-2016-MustoSGL #learning #wiki #word: Learning Word Embeddings from Wikipedia for Content-Based Recommender Systems (CM, GS, MdG, PL), pp. 729–734.
ECIR-2016-ValcarcePB #collaboration #feedback #performance #pseudo: Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation (DV, JP, AB), pp. 602–613.
ECIR-2016-WangJLSZL #cumulative #knowledge base: Cold Start Cumulative Citation Recommendation for Knowledge Base Acceleration (JW, JJ, LL, DS, ZZ, CYL), pp. 748–753.
ICML-2016-HoilesS #bound #design #education #evaluation: Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design (WH, MvdS), pp. 1596–1604.
ICML-2016-SchnabelSSCJ #evaluation #learning: Recommendations as Treatments: Debiasing Learning and Evaluation (TS, AS, AS, NC, TJ), pp. 1670–1679.
KDD-2016-AckermannRHKBKG #design #policy: Designing Policy Recommendations to Reduce Home Abandonment in Mexico (KA, EBR, SH, TAK, PvdB, RK, RG, JCG), pp. 13–20.
KDD-2016-Bressan0PRT #social: The Limits of Popularity-Based Recommendations, and the Role of Social Ties (MB0, SL0, AP, PR, ET), pp. 745–754.
KDD-2016-Christakopoulou #towards: Towards Conversational Recommender Systems (KC, FR, KH), pp. 815–824.
KDD-2016-JainPV #multi #ranking: Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (HJ, YP, MV), pp. 935–944.
KDD-2016-LiGHZ #learning: Point-of-Interest Recommendations: Learning Potential Check-ins from Friends (HL, YG, RH, HZ), pp. 975–984.
KDD-2016-LiuLLQX #assessment: Unified Point-of-Interest Recommendation with Temporal Interval Assessment (YL, CL, BL0, MQ, HX), pp. 1015–1024.
KDD-2016-PerozziSST #network: When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks (BP, MS, JS, MT), pp. 569–578.
KDD-2016-SunLGXX #automation #data-driven #development: Data-driven Automatic Treatment Regimen Development and Recommendation (LS, CL, CG, HX, YX), pp. 1865–1874.
KDD-2016-TaghaviLK #machine learning #memory management #using: Compute Job Memory Recommender System Using Machine Learning (TT, ML, YK), pp. 609–616.
KDD-2016-TangLCA #empirical #feedback #multi: An Empirical Study on Recommendation with Multiple Types of Feedback (LT, BL, BCC, DA), pp. 283–292.
KDD-2016-WangELBZGC #challenge #email: The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation (BW, ME, YL, JB, YZ, ZG, DC), pp. 1895–1904.
KDD-2016-ZengWML #multi #online: Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit (CZ, QW, SM, TL0), pp. 2025–2034.
KDD-2016-ZhangYLXM #collaboration #knowledge base: Collaborative Knowledge Base Embedding for Recommender Systems (FZ, NJY, DL, XX0, WYM), pp. 353–362.
ASE-2016-AlmhanaMK0 #debugging #multi #using: Recommending relevant classes for bug reports using multi-objective search (RA, WM, MK, AO), pp. 286–295.
ASE-2016-ChenX #automation #library #named #programming language: SimilarTech: automatically recommend analogical libraries across different programming languages (CC, ZX), pp. 834–839.
ASE-2016-HannebauerPSG #automation #code review #comparison #empirical: Automatically recommending code reviewers based on their expertise: an empirical comparison (CH, MP, SS, VG), pp. 99–110.
ASE-2016-MoonenABR #guidelines #mining #using: Practical guidelines for change recommendation using association rule mining (LM, SDA, DB, TR), pp. 732–743.
ASE-2016-ProkschANM: Evaluating the evaluations of code recommender systems: a reality check (SP, SA, SN, MM), pp. 111–121.
ASE-2016-RahmanRRC #code review #git #named: CORRECT: code reviewer recommendation at GitHub for Vendasta technologies (MMR, CKR, JR, JAC), pp. 792–797.
ASE-2016-RazaF #analysis #automation #development #named #performance: ProcessPAIR: a tool for automated performance analysis and improvement recommendation in software development (MR, JPF), pp. 798–803.
ASE-2016-Thung #api #development: API recommendation system for software development (FT), pp. 896–899.
FSE-2016-CostaFMS #branch #named: TIPMerge: recommending experts for integrating changes across branches (CC, JF, LM, AS), pp. 523–534.
FSE-2016-CostaFSM #branch #developer #named: TIPMerge: recommending developers for merging branches (CC, JF, AS, LM), pp. 998–1002.
FSE-2016-LinPCDZZ #architecture #interactive #refactoring #search-based: Interactive and guided architectural refactoring with search-based recommendation (YL0, XP0, YC, DD, DZ, WZ), pp. 535–546.
FSE-2016-NguyenHCNMRND #api #fine-grained #learning #statistics #using: API code recommendation using statistical learning from fine-grained changes (ATN0, MH, MC, HAN, LM, ER, TNN, DD), pp. 511–522.
FSE-2016-SorboPASVCG #what: What would users change in my app? summarizing app reviews for recommending software changes (ADS, SP, CVA, JS, CAV, GC, HCG), pp. 499–510.
FSE-2016-ZhangJKKGH #developer: Bing developer assistant: improving developer productivity by recommending sample code (HZ0, AJ, GK, CK, SG, WH), pp. 956–961.
GPCE-2016-PereiraMKSS #personalisation #product line: A feature-based personalized recommender system for product-line configuration (JAP, PM, SK, MS, GS), pp. 120–131.
ICST-2016-LuYAZ #product line: Nonconformity Resolving Recommendations for Product Line Configuration (HL0, TY0, SA0, LZ0), pp. 57–68.
HT-2015-HaKK #on the: On Recommending Newly Published Academic Papers (JH, SHK, SWK), pp. 329–330.
HT-2015-LeeHKGH #on the: On Recommending Job Openings (YCL, JH, SWK, SG, JYH), pp. 331–332.
HT-2015-Orellana-Rodriguez #mining: Mining Affective Context in Short Films for Emotion-Aware Recommendation (COR, EDA, WN), pp. 185–194.
HT-2015-PeraN: Analyzing Book-Related Features to Recommend Books for Emergent Readers (MSP, YKN), pp. 221–230.
SIGMOD-2015-HuangCZJX #named #realtime: TencentRec: Real-time Stream Recommendation in Practice (YH, BC, WZ, JJ, YX), pp. 227–238.
SIGMOD-2015-RoyLL: From Group Recommendations to Group Formation (SBR, LVSL, RL), pp. 1603–1616.
SIGMOD-2015-ZhouCZCHW #community #online #video: Online Video Recommendation in Sharing Community (XZ, LC, YZ, LC, GH, CW), pp. 1645–1656.
TPDL-2015-BeelL #comparison #online #user study: A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems (JB, SL), pp. 153–168.
VLDB-2015-ChenGXJC #distributed #image #named #retrieval: I2RS: A Distributed Geo-Textual Image Retrieval and Recommendation System (LC, YG, ZX, CSJ, GC), pp. 1884–1895.
VLDB-2015-GuerraouiKPT #difference #named #privacy: D2P: Distance-Based Differential Privacy in Recommenders (RG, AMK, RP, MT), pp. 862–873.
VLDB-2015-VartakRMPP #data-driven #named #performance #visual notation #visualisation: SEEDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics (MV, SR, SM, AGP, NP), pp. 2182–2193.
EDM-2015-AgrawalVLP #named #video: YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips (AA, JV, SL, AP), pp. 297–304.
EDM-2015-MihaescuPI #education #online: Intelligent Tutor Recommender System for On-Line Educational Environments (MCM, PSP, CMI), pp. 516–519.
EDM-2015-WangZ #education #process: Discovering Process in Curriculum Data to Provide Recommendation (RW, ORZ), pp. 580–581.
ICPC-2015-AmintabarHG #development #exception #ide #named: ExceptionTracer: a solution recommender for exceptions in an integrated development environment (VA, AH, MG), pp. 299–302.
ICSME-2015-AsaduzzamanRMS #api #parametricity: Exploring API method parameter recommendations (MA, CKR, SM, KAS), pp. 271–280.
ICSME-2015-AsaduzzamanRS #api #named #parametricity: PARC: Recommending API methods parameters (MA, CKR, KAS), pp. 330–332.
ICSME-2015-XiaLWY #analysis #overview: Who should review this change?: Putting text and file location analyses together for more accurate recommendations (XX, DL, XW, XY), pp. 261–270.
MSR-2015-WangMG #api #developer: Recommending Posts concerning API Issues in Developer Q&A Sites (WW, HM, MWG), pp. 224–234.
SANER-2015-ThongtanunamTKY #approach #code review #overview #perspective: Who should review my code? A file location-based code-reviewer recommendation approach for Modern Code Review (PT, CT, RGK, NY, HI, KiM), pp. 141–150.
SCAM-2015-RahmanRK #crowdsourcing #source code #using: Recommending insightful comments for source code using crowdsourced knowledge (MMR, CKR, IK), pp. 81–90.
FDG-2015-KaltmanWLC #development #game studies: Methods and Recommendations for Archival Records of Game Development: The Case of Academic Games (EK, NWF, HL, CC).
CHI-2015-LoeppH0 #algorithm #information management #interactive: Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques (BL, KH, JZ), pp. 975–984.
CSCW-2015-ChangHT #using: Using Groups of Items to Bootstrap New Users in Recommender Systems (SC, FMH, LGT), pp. 1258–1269.
DUXU-IXD-2015-WalterKWAB #adaptation #question #what: What Are the Expectations of Users of an Adaptive Recommendation Service Which Aims to Reduce Driver Distraction? (NW, BK, CW, TA, KB), pp. 517–528.
HIMI-IKC-2015-BrunsVGZS #personalisation #visual notation #what: What Should I Read Next? A Personalized Visual Publication Recommender System (SB, ACV, CG, MZ, US), pp. 89–100.
HIMI-IKC-2015-Kaewkiriya #design #framework #student: Design of Framework for Students Recommendation System in Information Technology Skills (TK), pp. 109–117.
HIMI-IKC-2015-VerstocktSB: Map-Based Linking of Geographic User and Content Profiles for Hyperlocal Content Recommendation (SV, VS, KB), pp. 53–63.
LCT-2015-Iitaka #online: Recommendation Engine for an Online Drill System (TI), pp. 238–248.
LCT-2015-RodriguezOD #hybrid #learning #repository #student: A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories (PAR, DAO, NDD), pp. 291–300.
LCT-2015-Sirisaengtaksin #approach #design #education #mobile #online #research: A Notification and Recommender Mobile App for Educational Online Discussion: A Design Research Approach (KS, LO, NA), pp. 325–336.
ICEIS-v1-2015-Kozmina #empirical: An Empirical Study of Recommendations in OLAP Reporting Tool (NK), pp. 303–312.
ICEIS-v2-2015-CeredaN #adaptation #automaton: A Recommendation Engine based on Adaptive Automata (PRMC, JJN), pp. 594–601.
ICEIS-v2-2015-SmirnovP #architecture #hybrid #network #peer-to-peer #privacy: Privacy-preserving Hybrid Peer-to-Peer Recommendation System Architecture — Locality-Sensitive Hashing in Structured Overlay Network (AVS, AP), pp. 532–542.
CIKM-2015-DuanZ #coordination #mining #representation: Mining Coordinated Intent Representation for Entity Search and Recommendation (HD, CZ), pp. 333–342.
CIKM-2015-HeCKC #aspect-oriented #modelling #named: TriRank: Review-aware Explainable Recommendation by Modeling Aspects (XH0, TC0, MYK, XC0), pp. 1661–1670.
CIKM-2015-JiangLG: Chronological Citation Recommendation with Information-Need Shifting (ZJ, XL, LG), pp. 1291–1300.
CIKM-2015-LiangB #personalisation #probability #rating: A Probabilistic Rating Auto-encoder for Personalized Recommender Systems (HL, TB), pp. 1863–1866.
CIKM-2015-ManSHC #adaptation #matrix #multi: Context-Adaptive Matrix Factorization for Multi-Context Recommendation (TM, HS, JH, XC), pp. 901–910.
CIKM-2015-PerezSLK #approach: Category-Driven Approach for Local Related Business Recommendations (YP, MS, ML, SK), pp. 73–82.
CIKM-2015-QianHSR: Structured Sparse Regression for Recommender Systems (MQ, LH, YS, SR), pp. 1895–1898.
CIKM-2015-ShinCLD #induction #matrix: Tumblr Blog Recommendation with Boosted Inductive Matrix Completion (DS, SC, KCL, ISD), pp. 203–212.
CIKM-2015-ShiZLYYW #network #personalisation #semantics: Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks (CS, ZZ, PL, PSY, YY, BW0), pp. 453–462.
CIKM-2015-SongCYL #personalisation: Personalized Recommendation Meets Your Next Favorite (QS, JC0, TY, HL), pp. 1775–1778.
CIKM-2015-SwezeyC: Recommending Short-lived Dynamic Packages for Golf Booking Services (RS, YjC), pp. 1779–1782.
CIKM-2015-VahabiKGH #named: DifRec: A Social-Diffusion-Aware Recommender System (HV, IK, FG, MH), pp. 1481–1490.
CIKM-2015-WangTL #towards: Toward Dual Roles of Users in Recommender Systems (SW, JT, HL0), pp. 1651–1660.
CIKM-2015-YangHQXW #graph: A Graph-based Recommendation across Heterogeneous Domains (DY, JH, HQ, YX, WW0), pp. 463–472.
CIKM-2015-YinZSWS #behaviour #modelling: Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation (HY, XZ0, YS, HW0, SWS), pp. 1631–1640.
CIKM-2015-ZhangCZ #framework #named: ORec: An Opinion-Based Point-of-Interest Recommendation Framework (JDZ, CYC, YZ), pp. 1641–1650.
CIKM-2015-ZhangLWS #nondeterminism #personalisation: Personalized Trip Recommendation with POI Availability and Uncertain Traveling Time (CZ, HL, KW0, JS), pp. 911–920.
CIKM-2015-ZhangLY #enterprise #social: Enterprise Social Link Recommendation (JZ, YL, PSY), pp. 841–850.
CIKM-2015-ZhangW #collaboration #retrieval #social: Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest Recommendation (WZ0, JW), pp. 1221–1230.
CIKM-2015-ZhaoMK #modelling #personalisation: Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation (TZ, JJM, IK), pp. 821–830.
CIKM-2015-ZhaoZK #analysis #game studies #network #social: Exploiting Game Theoretic Analysis for Link Recommendation in Social Networks (TZ, HVZ, IK), pp. 851–860.
ECIR-2015-HopfgartnerB #realtime: Join the Living Lab: Evaluating News Recommendations in Real-Time (FH, TB), pp. 826–829.
ECIR-2015-SchedlHFT #algorithm #music #on the: On the Influence of User Characteristics on Music Recommendation Algorithms (MS, DH, KF, MT), pp. 339–345.
ECIR-2015-ValcarcePB #case study #modelling: A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems (DV, JP, AB), pp. 346–351.
ECIR-2015-WangHS0W0 #network #problem #social #towards: Toward the New Item Problem: Context-Enhanced Event Recommendation in Event-Based Social Networks (ZW, PH, LS, KC, SW, GC), pp. 333–338.
KDD-2015-BerkovskyF #personalisation #web: Web Personalization and Recommender Systems (SB, JF), pp. 2307–2308.
KDD-2015-FrenoSJA #modelling #ranking: One-Pass Ranking Models for Low-Latency Product Recommendations (AF, MS, RJ, CA), pp. 1789–1798.
KDD-2015-GrbovicRDBSBS #e-commerce #scalability: E-commerce in Your Inbox: Product Recommendations at Scale (MG, VR, ND, NB, JS, VB, DS), pp. 1809–1818.
KDD-2015-HolleczekAYJAGL #agile #metric: Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT) (TH, DTA, SY, YJ, SA, HLG, SL, ASN), pp. 1859–1868.
KDD-2015-HsiehLZ #big data #quality: Inferring Air Quality for Station Location Recommendation Based on Urban Big Data (HPH, SDL, YZ), pp. 437–446.
KDD-2015-JiangZZY #e-commerce #predict: Life-stage Prediction for Product Recommendation in E-commerce (PJ, YZ, YZ, QY), pp. 1879–1888.
KDD-2015-Kawamae #realtime: Real Time Recommendations from Connoisseurs (NK), pp. 537–546.
KDD-2015-QianCMSL #named: SCRAM: A Sharing Considered Route Assignment Mechanism for Fair Taxi Route Recommendations (SQ, JC, FLM, IS, ML), pp. 955–964.
KDD-2015-SongMT #network #performance: Efficient Latent Link Recommendation in Signed Networks (DS, DAM, DT), pp. 1105–1114.
KDD-2015-WangWY #collaboration #learning: Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDD-2015-WangYCSSZ #generative #named: Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation (WW, HY, LC, YS, SWS, XZ), pp. 1255–1264.
KDD-2015-ZhangW: A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation (WZ, JW), pp. 1455–1464.
KDD-2015-ZhongJYYZL: Stock Constrained Recommendation in Tmall (WZ, RJ, CY, XY, QZ, QL), pp. 2287–2296.
KDD-2015-ZhongLSR #scalability: Building Discriminative User Profiles for Large-scale Content Recommendation (EZ, NL, YS, SR), pp. 2277–2286.
RecSys-2015-Abel: We Know Where You Should Work Next Summer: Job Recommendations (FA), p. 230.
RecSys-2015-AghdamHMB #adaptation #markov #modelling #using: Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models (MHA, NH, BM, RDB), pp. 241–244.
RecSys-2015-AharonAADGS #named: ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations (MA, OA, NAE, DDC, SG, OS), pp. 83–90.
RecSys-2015-BanksRS #game studies #using: The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data (SB, RR, BS), pp. 305–308.
RecSys-2015-BansalDB #profiling: Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (TB, MKD, CB), pp. 195–202.
RecSys-2015-BarjastehFMER: Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.
RecSys-2015-BetzalelSR #exclamation #quote: “Please, Not Now!”: A Model for Timing Recommendations (NDB, BS, LR), pp. 297–300.
RecSys-2015-BistaffaFCR #scalability #social: Recommending Fair Payments for Large-Scale Social Ridesharing (FB, AF, GC, SDR), pp. 139–146.
RecSys-2015-Bourke #multi: The Application of Recommender Systems in a Multi Site, Multi Domain Environment (SB), p. 229.
RecSys-2015-ChaneyBE #network #personalisation #probability #social #using: A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.
RecSys-2015-ChristoffelPNB #random #scalability: Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks (FC, BP, CN, AB), pp. 163–170.
RecSys-2015-DalyBS: Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates (ED, MB, FS), pp. 253–256.
RecSys-2015-Das: Making Meaningful Restaurant Recommendations At OpenTable (SD), p. 235.
RecSys-2015-EkstrandKHK #algorithm #case study: Letting Users Choose Recommender Algorithms: An Experimental Study (MDE, DK, FMH, JAK), pp. 11–18.
RecSys-2015-ElsweilerH #automation #towards: Towards Automatic Meal Plan Recommendations for Balanced Nutrition (DE, MH), pp. 313–316.
RecSys-2015-ForsatiBMER #algorithm #named #performance #trust: PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations (RF, IB, FM, AHE, HR), pp. 51–58.
RecSys-2015-GeRM: Health-aware Food Recommender System (MG, FR, DM), pp. 333–334.
RecSys-2015-Geuens #behaviour #hybrid: Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data (SG), pp. 379–382.
RecSys-2015-GriesnerAN #matrix #towards: POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences (JBG, TA, HN), pp. 301–304.
RecSys-2015-Guardia-Sebaoun #modelling #performance: Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems (ÉGS, VG, PG), pp. 281–284.
RecSys-2015-Guy #personalisation: The Role of User Location in Personalized Search and Recommendation (IG), p. 236.
RecSys-2015-HarperXKCCT: Putting Users in Control of their Recommendations (FMH, FX, HK, KC, SC, LGT), pp. 3–10.
RecSys-2015-HarveyE #automation #personalisation: Automated Recommendation of Healthy, Personalised Meal Plans (MH, DE), pp. 327–328.
RecSys-2015-HopfgartnerKHT #realtime: Real-time Recommendation of Streamed Data (FH, BK, TH, RT), pp. 361–362.
RecSys-2015-HuD #machine learning #scalability: Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
RecSys-2015-JannachLJ #adaptation #evaluation: Adaptation and Evaluation of Recommendations for Short-term Shopping Goals (DJ, LL, MJ), pp. 211–218.
RecSys-2015-KangDS: Elsevier Journal Finder: Recommending Journals for your Paper (NK, MAD, RJAS), pp. 261–264.
RecSys-2015-KaragiannakisGS #automation #category theory: OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap (NK, GG, DS, SA), pp. 337–338.
RecSys-2015-KoukiFFEG #flexibility #framework #hybrid #named #probability: HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems (PK, SF, JRF, ME, LG), pp. 99–106.
RecSys-2015-KowaldL #algorithm #case study #comparative #folksonomy: Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study (DK, EL), pp. 265–268.
RecSys-2015-LerallutGR #realtime #scalability: Large-Scale Real-Time Product Recommendation at Criteo (RL, DG, NLR), p. 232.
RecSys-2015-LimML #feedback: Top-N Recommendation with Missing Implicit Feedback (DL, JM, GRGL), pp. 309–312.
RecSys-2015-LiuK #named: Kibitz: End-to-End Recommendation System Builder (QL, DRK), pp. 335–336.
RecSys-2015-LiWTM #community #predict #rating #social: Overlapping Community Regularization for Rating Prediction in Social Recommender Systems (HL, DW, WT, NM), pp. 27–34.
RecSys-2015-LuC #personalisation: Exploiting Geo-Spatial Preference for Personalized Expert Recommendation (HL, JC), pp. 67–74.
RecSys-2015-Ludmann #data type #online: Online Recommender Systems based on Data Stream Management Systems (CAL), pp. 391–394.
RecSys-2015-MacedoMS #network #social: Context-Aware Event Recommendation in Event-based Social Networks (AQdM, LBM, RLTS), pp. 123–130.
RecSys-2015-MagnusonDM #process #twitter #using: Event Recommendation using Twitter Activity (AM, VD, DM), pp. 331–332.
RecSys-2015-MaksaiGF #evaluation #metric #online #performance #predict: Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics (AM, FG, BF), pp. 179–186.
RecSys-2015-MarinhoTP #algorithm #question: Are Real-World Place Recommender Algorithms Useful in Virtual World Environments? (LBM, CT, DP), pp. 245–248.
RecSys-2015-MojsilovicV #enterprise #perspective: Assessing Expertise in the Enterprise: The Recommender Point of View (AM, KRV), p. 231.
RecSys-2015-Nemeth #scalability: Scaling Up Recommendation Services in Many Dimensions (BN), p. 233.
RecSys-2015-NeumannS: Recommendations for Live TV (JN, HS), p. 228.
RecSys-2015-NovA #social #symmetry: Asymmetric Recommendations: The Interacting Effects of Social Ratings? Direction and Strength on Users’ Ratings (ON, OA), pp. 249–252.
RecSys-2015-SaidB #evaluation: Replicable Evaluation of Recommender Systems (AS, AB), pp. 363–364.
RecSys-2015-Salehi-AbariB #network #social: Preference-oriented Social Networks: Group Recommendation and Inference (ASA, CB), pp. 35–42.
RecSys-2015-Santos #hybrid: A Hybrid Recommendation System Based on Human Curiosity (AMdS), pp. 367–370.
RecSys-2015-SeminarioW #collaboration: Nuke ’Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders (CES, DCW), pp. 293–296.
RecSys-2015-ShalomBRZA #matter #quality: Data Quality Matters in Recommender Systems (OSS, SB, RR, EZ, AA), pp. 257–260.
RecSys-2015-SongCL #incremental #matrix: Incremental Matrix Factorization via Feature Space Re-learning for Recommender System (QS, JC, HL), pp. 277–280.
RecSys-2015-SousaDBM #analysis #named #network: CNARe: Co-authorship Networks Analysis and Recommendations (GAdS, MAD, MAB, MMM), pp. 329–330.
RecSys-2015-SteckZJ #interactive #tutorial: Interactive Recommender Systems: Tutorial (HS, RvZ, CJ), pp. 359–360.
RecSys-2015-Unger: Latent Context-Aware Recommender Systems (MU), pp. 383–386.
RecSys-2015-Valcarce #modelling #statistics: Exploring Statistical Language Models for Recommender Systems (DV), pp. 375–378.
RecSys-2015-ValcarcePB #case study #modelling: A Study of Priors for Relevance-Based Language Modelling of Recommender Systems (DV, JP, AB), pp. 237–240.
RecSys-2015-VerstrepenG: Top-N Recommendation for Shared Accounts (KV, BG), pp. 59–66.
RecSys-2015-ZhaoZ0: Risk-Hedged Venture Capital Investment Recommendation (XZ, WZ, JW), pp. 75–82.
RecSys-2015-ZhaoZFT #e-commerce #personalisation: E-commerce Recommendation with Personalized Promotion (QZ, YZ, DF, FT), pp. 219–226.
RecSys-2015-Zoeter: Recommendations in Travel (OZ), p. 234.
SEKE-2015-Colace0LLYC #adaptation #perspective: An Adaptive Contextual Recommender System: a Slow Intelligence Perspective (FC, LG, SL, ML, DY, SKC), pp. 64–71.
SEKE-2015-LiuXC #learning: Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKE-2015-RamosCRSAP #analysis #architecture: Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis (FBAR, AAMC, RRdS, GS, HOdA, AP), pp. 99–104.
SEKE-2015-ZhaoSCZ #crowdsourcing #developer #effectiveness #towards: Towards Effective Developer Recommendation in Software Crowdsourcing (SZ, BS, YC, HZ), pp. 326–329.
SIGIR-2015-ChengS #music #named: VenueMusic: A Venue-Aware Music Recommender System (ZC, JS), pp. 1029–1030.
SIGIR-2015-ChenLZLS #approximate #matrix #named #scalability: WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation (CC, DL, YZ, QL, LS), pp. 303–312.
SIGIR-2015-GuoL #automation #generative #graph #music: Automatic Feature Generation on Heterogeneous Graph for Music Recommendation (CG, XL), pp. 807–810.
SIGIR-2015-GuyLDB #case study #enterprise #social: Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream (IG, RL, TD, EB), pp. 665–674.
SIGIR-2015-HaraSKF #using: Reducing Hubness: A Cause of Vulnerability in Recommender Systems (KH, IS, KK, KF), pp. 815–818.
SIGIR-2015-KneesS #music #overview #perspective #retrieval #tutorial: Music Retrieval and Recommendation: A Tutorial Overview (PK, MS), pp. 1133–1136.
SIGIR-2015-LiCLPK #named #ranking: Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (XL, GC, XL, TANP, SK), pp. 433–442.
SIGIR-2015-LuZZW #personalisation: Exploiting User and Business Attributes for Personalized Business Recommendation (KL, YZ, LZ, SW), pp. 891–894.
SIGIR-2015-McAuleyTSH: Image-Based Recommendations on Styles and Substitutes (JJM, CT, QS, AvdH), pp. 43–52.
SIGIR-2015-ReinandaMR #aspect-oriented #mining #ranking: Mining, Ranking and Recommending Entity Aspects (RR, EM, MdR), pp. 263–272.
SIGIR-2015-SchedlH #music: Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty (MS, DH), pp. 947–950.
SIGIR-2015-ShokouhiG #query #ranking: From Queries to Cards: Re-ranking Proactive Card Recommendations Based on Reactive Search History (MS, QG), pp. 695–704.
SIGIR-2015-SunXZLGX #multi #personalisation: Multi-source Information Fusion for Personalized Restaurant Recommendation (JS, YX, YZ, JL, CG, HX), pp. 983–986.
SIGIR-2015-TangJLZL #personalisation: Personalized Recommendation via Parameter-Free Contextual Bandits (LT, YJ, LL, CZ, TL), pp. 323–332.
SIGIR-2015-VolkovsY #effectiveness #feedback #modelling: Effective Latent Models for Binary Feedback in Recommender Systems (MV, GWY), pp. 313–322.
SIGIR-2015-WangGLXWC #learning #representation: Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIR-2015-WangSWZSLL #cumulative: An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation (JW, DS, QW, ZZ, LS, LL, CYL), pp. 635–644.
SIGIR-2015-XuWW #collaboration #personalisation #ranking #semantics: Personalized Semantic Ranking for Collaborative Recommendation (SX, SW, LW), pp. 971–974.
SIGIR-2015-YaoSQWSH #social #using: Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization (LY, QZS, YQ, XW, AS, QH), pp. 1007–1010.
SIGIR-2015-ZhangC #category theory #correlation #named #social: GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (JDZ, CYC), pp. 443–452.
SIGIR-2015-ZhangCQZL #multi #personalisation #similarity: When Personalization Meets Conformity: Collective Similarity based Multi-Domain Recommendation (XZ, JC, SQ, ZZ, HL), pp. 1019–1022.
ASE-2015-NguyenPVN #api #markov #mobile: Recommending API Usages for Mobile Apps with Hidden Markov Model (TTN, HVP, PMV, TTN), pp. 795–800.
ASE-2015-ZimmermanR #automation #framework: An Automated Framework for Recommending Program Elements to Novices (N) (KZ, CRR), pp. 283–288.
ESEC-FSE-2015-LinPXZZ #interactive: Clone-based and interactive recommendation for modifying pasted code (YL, XP, ZX, DZ, WZ), pp. 520–531.
ESEC-FSE-2015-PhamSS #automation #developer: Automatically recommending test code examples to inexperienced developers (RP, YS, KS), pp. 890–893.
ICSE-v2-2015-Beyer #api #developer #mobile #named: DIETs: Recommender Systems for Mobile API Developers (SB), pp. 859–862.
SAC-2015-AissiGSS #evaluation #framework #personalisation #query: Personalized recommendation of SOLAP queries: theoretical framework and experimental evaluation (SA, MSG, TS, LBS), pp. 1008–1014.
SAC-2015-AliK #approach #effectiveness: An effective approach to group recommendation based on belief propagation (IA, SWK), pp. 1148–1153.
SAC-2015-BurityE #approach: A quantitative, evidence-based approach for recommending software modules (TB, GEdS), pp. 1449–1456.
SAC-2015-CamaraHJJ #graph #modelling #persuasion #social #using: Using graph-based models in a persuasive social recommendation system (JPC, SH, JJ, VJ), pp. 189–194.
SAC-2015-CapelleMHFV #hybrid #semantics: Bing-SF-IDF+: a hybrid semantics-driven news recommender (MC, MM, FH, FF, DV), pp. 732–739.
SAC-2015-Chaudhary #experience: Experience in item based recommender system (AC), pp. 1112–1114.
SAC-2015-DominguesSBMPR #metadata #multi #personalisation #ranking: Applying multi-view based metadata in personalized ranking for recommender systems (MAD, CVS, FMMB, MGM, MGCP, SOR), pp. 1105–1107.
SAC-2015-HsiehNKC #approximate #performance #query: Efficient approximate thompson sampling for search query recommendation (CCH, JN, TK, JC), pp. 740–746.
SAC-2015-LommatzschA #realtime: Real-time recommendations for user-item streams (AL, SA), pp. 1039–1046.
SAC-2015-MatuszykVSJG #incremental #matrix: Forgetting methods for incremental matrix factorization in recommender systems (PM, JV, MS, AMJ, JG), pp. 947–953.
SAC-2015-PaivaBSIJ #behaviour #learning #student: Improving pedagogical recommendations by classifying students according to their interactional behavior in a gamified learning environment (ROAP, IIB, APdS, SI, PAJ), pp. 233–238.
SAC-2015-RodriguesJD #using: Accelerating recommender systems using GPUs (AVR, AJ, ID), pp. 879–884.
SAC-2015-Zheng #algorithm: Improve general contextual slim recommendation algorithms by factorizing contexts (YZ), pp. 929–930.
ECSA-2014-HeroldM #architecture #consistency #refactoring: Recommending Refactorings to Re-establish Architectural Consistency (SH, MM), pp. 390–397.
HT-2014-KowaldLT #benchmark #framework #metric #named #standard #towards: TagRec: towards a standardized tag recommender benchmarking framework (DK, EL, CT), pp. 305–307.
HT-2014-LacicKT #named #online #scalability #social: SocRecM: a scalable social recommender engine for online marketplaces (EL, DK, CT), pp. 308–310.
HT-2014-QuerciaSA: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city (DQ, RS, LMA), pp. 116–125.
HT-2014-TrevisiolCB #personalisation: Buon appetito: recommending personalized menus (MT, LC, RABY), pp. 327–329.
JCDL-2014-AkbarSFF #deduction #education #library #network #social: Recommendation based on Deduced Social Networks in an educational digital library (MA, CAS, WF, EAF), pp. 29–38.
JCDL-2014-HuangWMG #named: RefSeer: A citation recommendation system (WH, ZW, PM, CLG), pp. 371–374.
JCDL-2014-LiuYGSG #approach #mining #network: Full-text based context-rich heterogeneous network mining approach for citation recommendation (XL, YY, CG, YS, LG), pp. 361–370.
JCDL-2014-OtegiAC #personalisation #rank: Personalised PageRank for making recommendations in digital cultural heritage collections (AO, EA, PDC), pp. 49–52.
VLDB-2014-DaiQJWW #personalisation: A Personalized Recommendation System for NetEase Dating Site (CD, FQ, WJ, ZW, ZW), pp. 1760–1765.
VLDB-2014-GuptaSGGZLL #detection #graph #online #realtime #scalability #twitter: Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs (PG, VS, AG, SG, VZ, QL, JL), pp. 1379–1380.
VLDB-2014-LuCLL: Show Me the Money: Dynamic Recommendations for Revenue Maximization (WL, SC, KL, LVSL), pp. 1785–1796.
VLDB-2014-WangLHCSWLT #named #realtime: R3: A Real-Time Route Recommendation System (HW, GL, HH, SC, BS, HW, WSL, KLT), pp. 1549–1552.
VLDB-2014-ZhangJSR #big data #using: Getting Your Big Data Priorities Straight: A Demonstration of Priority-based QoS using Social-network-driven Stock Recommendation (RZ, RJ, PS, LR), pp. 1665–1668.
EDM-2014-CarballoA #case study #composition #education: Singular Value Decomposition in Education: a case study on recommending courses (FC, CA), pp. 417–418.
EDM-2014-YangPHR #concurrent #online #thread: Forum Thread Recommendation for Massive Open Online Courses (DY, MP, IKH, CPR), pp. 257–260.
CSMR-WCRE-2014-GeSDM #how #query: How the Sando search tool recommends queries (XG, DCS, KD, ERMH), pp. 425–428.
CSMR-WCRE-2014-KashiwabaraOIHYI #mining #using: Recommending verbs for rename method using association rule mining (YK, YO, TI, YH, TY, KI), pp. 323–327.
CSMR-WCRE-2014-RahmanYR #exception #fault #ide #programming #towards: Towards a context-aware IDE-based meta search engine for recommendation about programming errors and exceptions (MMR, SY, CKR), pp. 194–203.
ICPC-2014-GhafariGMT #mining #testing: Mining unit tests for code recommendation (MG, CG, AM, GT), pp. 142–145.
ICPC-2014-SilvaTV #automation #refactoring: Recommending automated extract method refactorings (DS, RT, MTV), pp. 146–156.
ICPC-2014-SteidlE #fault #maintenance #refactoring: Prioritizing maintainability defects based on refactoring recommendations (DS, SE), pp. 168–176.
ICSME-2014-PonzanelliBPOL #named #self: Prompter: A Self-Confident Recommender System (LP, GB, MDP, RO, ML), pp. 577–580.
ICSME-2014-WangG #design #refactoring #using: Recommending Clones for Refactoring Using Design, Context, and History (WW, MWG), pp. 331–340.
ICSME-2014-WangLVS #named: EnTagRec: An Enhanced Tag Recommendation System for Software Information Sites (SW, DL, BV, AS), pp. 291–300.
ICSME-2014-YuWYL #git: Reviewer Recommender of Pull-Requests in GitHub (YY, HW, GY, CXL), pp. 609–612.
SCAM-2014-RahmanR14a #exception #on the #using: On the Use of Context in Recommending Exception Handling Code Examples (MMR, CKR), pp. 285–294.
CIG-2014-KimK #game studies #learning #realtime: Learning to recommend game contents for real-time strategy gamers (HTK, KJK), pp. 1–8.
CHI-2014-HongA #modelling #performance #predict #user interface #using: Novice use of a predictive human performance modeling tool to produce UI recommendations (KWH, RSA), pp. 2251–2254.
CHI-2014-LoeppHZ #collaboration #elicitation: Choice-based preference elicitation for collaborative filtering recommender systems (BL, TH, JZ), pp. 3085–3094.
CHI-2014-NorvalAH #network #social #what: What’s on your mind?: investigating recommendations for inclusive social networking and older adults (CN, JLA, VLH), pp. 3923–3932.
HCI-AIMT-2014-LackeyBM #communication #interactive #requirements: Recommended Considerations for Human-Robot Interaction Communication Requirements (SJL, DJB, SGM), pp. 663–674.
HCI-AS-2014-Lopez-OrnelasAZ: A Geo-collaborative Recommendation Tool to Help Urban Mobility (ÉLO, RAM, JSZH), pp. 466–472.
HCI-AS-2014-ZiesemerMS #exclamation #gamification: Just Rate It! Gamification as Part of Recommendation (AdCAZ, LM, MSS), pp. 786–796.
HCI-TMT-2014-SousaB #statistics: Recommender System to Support Chart Constructions with Statistical Data (TAFdS, SDJB), pp. 631–642.
HIMI-AS-2014-AsikisL #research: Operations Research and Recommender Systems (TA, GL), pp. 579–589.
HIMI-DE-2014-GombosK #dataset #query: SPARQL Query Writing with Recommendations Based on Datasets (GG, AK), pp. 310–319.
LCT-TRE-2014-BraunhoferEGR #learning #mobile: Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems (MB, ME, MG, FR), pp. 105–116.
ICEIS-v1-2014-TitoRSFTS #information management #named: RecRoute — A Bus Route Recommendation System Based on Users’ Contextual Information (AdOT, ARRR, LMdS, LAVF, PRT, ACS), pp. 357–366.
ICEIS-v2-2014-BorattoC #clustering #collaboration #using: Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System (LB, SC), pp. 564–572.
ICEIS-v2-2014-PanfilenkoEML #impact analysis #independence #model transformation #platform #requirements: Recommendations for Impact Analysis of Model Transformations — From the Requirements Model to the Platform-independent Model (DVP, AE, CM, PL), pp. 428–434.
CIKM-2014-AllahoL #latency #online: Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects (MYA, WCL), pp. 749–758.
CIKM-2014-DahimeneCM #named #network #social: RecLand: A Recommender System for Social Networks (RD, CC, CdM), pp. 2063–2065.
CIKM-2014-DeveaudAMMO #named #personalisation: SmartVenues: Recommending Popular and Personalised Venues in a City (RD, MDA, JM, CM, IO), pp. 2078–2080.
CIKM-2014-LiuWSM: Exploiting Geographical Neighborhood Characteristics for Location Recommendation (YL, WW, AS, CM), pp. 739–748.
CIKM-2014-LiuYGS #feedback #graph #pseudo #ranking: Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation (XL, YY, CG, YS), pp. 121–130.
CIKM-2014-MahdabiC #mining #network #retrieval: Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation (PM, FC), pp. 1659–1668.
CIKM-2014-MaLG #collaboration #community #trust: Improving Recommendation Accuracy by Combining Trust Communities and Collaborative Filtering (XM, HL, ZG), pp. 1951–1954.
CIKM-2014-NtoutsiSRK #clustering #difference #quote: “Strength Lies in Differences”: Diversifying Friends for Recommendations through Subspace Clustering (EN, KS, KR, HPK), pp. 729–738.
CIKM-2014-ShiKBLH #learning #named: CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKM-2014-VlachosFMKV #clustering #quality: Improving Co-Cluster Quality with Application to Product Recommendations (MV, FF, CM, ATK, VGV), pp. 679–688.
CIKM-2014-WangGL #modelling #personalisation #transaction: Modeling Retail Transaction Data for Personalized Shopping Recommendation (PW, JG, YL), pp. 1979–1982.
CIKM-2014-WangJDY #adaptation #social: User Interests Imbalance Exploration in Social Recommendation: A Fitness Adaptation (TW, XJ, XD, XY), pp. 281–290.
CIKM-2014-WangPX #collaboration #matrix #named: HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation (XW, WP, CX), pp. 769–778.
CIKM-2014-YangSR: Constrained Question Recommendation in MOOCs via Submodularity (DY, JS, CPR), pp. 1987–1990.
CIKM-2014-YuanCS #graph: Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences (QY, GC, AS), pp. 659–668.
CIKM-2014-ZhengMB: Deviation-Based Contextual SLIM Recommenders (YZ, BM, RDB), pp. 271–280.
CIKM-2014-ZhongPXYM #adaptation #collaboration #learning: Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
ECIR-2014-BreussT #interactive #learning #social #social media: Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIR-2014-BrilhanteMNPR #named: TripBuilder: A Tool for Recommending Sightseeing Tours (IRB, JAFdM, FMN, RP, CR), pp. 771–774.
ECIR-2014-HofmannSBR #bias #evaluation: Effects of Position Bias on Click-Based Recommender Evaluation (KH, AS, AB, MdR), pp. 624–630.
ECIR-2014-Lommatzsch #realtime #using: Real-Time News Recommendation Using Context-Aware Ensembles (AL), pp. 51–62.
ECIR-2014-RikitianskiiHC #personalisation: A Personalised Recommendation System for Context-Aware Suggestions (AR, MH, FC), pp. 63–74.
ECIR-2014-ZhangZWS #network: Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks (CZ, XZ, KW, JS), pp. 39–50.
ICPR-2014-DominguesMMSR #information management #topic #using: Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems (MAD, MGM, RMM, CVS, SOR), pp. 3606–3611.
ICPR-2014-ManzatoDMR #feedback #personalisation #ranking #topic: Improving Personalized Ranking in Recommender Systems with Topic Hierarchies and Implicit Feedback (MGM, MAD, RMM, SOR), pp. 3696–3701.
KDD-2014-AmatriainM #problem #tutorial: The recommender problem revisited: morning tutorial (XA, BM), p. 1971.
KDD-2014-DiaoQWSJW #aspect-oriented #modelling #sentiment: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (QD, MQ, CYW, AJS, JJ, CW), pp. 193–202.
KDD-2014-JagadeeshPBDS #image #scalability #visual notation: Large scale visual recommendations from street fashion images (VJ, RP, AB, WD, NS), pp. 1925–1934.
KDD-2014-LeeLTS #modelling #scalability: Modeling impression discounting in large-scale recommender systems (PL, LVSL, MT, SS), pp. 1837–1846.
KDD-2014-LianZXSCR #matrix #modelling #named: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (DL, CZ, XX, GS, EC, YR), pp. 831–840.
KDD-2014-LiL #quality: Matching users and items across domains to improve the recommendation quality (CYL, SDL), pp. 801–810.
KDD-2014-LuIBL #social: Optimal recommendations under attraction, aversion, and social influence (WL, SI, SB, LVSL), pp. 811–820.
KDD-2014-QuZLLX #effectiveness: A cost-effective recommender system for taxi drivers (MQ, HZ, JL, GL, HX), pp. 45–54.
KDD-2014-RenLYKGWH #clustering #effectiveness #named: ClusCite: effective citation recommendation by information network-based clustering (XR, JL, XY, UK, QG, LW, JH), pp. 821–830.
KDD-2014-TangTL #future of #social #social media: Recommendation in social media: recent advances and new frontiers (JT, JT, HL), p. 1977.
KDD-2014-YuanCL #generative #named: COM: a generative model for group recommendation (QY, GC, CYL), pp. 163–172.
KDD-2014-ZhaoGHJWL #microblog #what: We know what you want to buy: a demographic-based system for product recommendation on microblogs (WXZ, YG, YH, HJ, YW, XL), pp. 1935–1944.
KDD-2014-ZhuXGC #mobile #privacy #security: Mobile app recommendations with security and privacy awareness (HZ, HX, YG, EC), pp. 951–960.
KDIR-2014-FukumotoSSM #collaboration: Incorporating Guest Preferences into Collaborative Filtering for Hotel Recommendation (FF, HS, YS, SM), pp. 22–30.
KDIR-2014-HasnaMDP #sentiment: Sentiment Polarity Extension for Context-Sensitive Recommender Systems (OLH, FCM, MD, RP), pp. 126–137.
KDIR-2014-MeguebliKDP14a #personalisation: Stories Around You — A Two-Stage Personalized News Recommendation (YM, MK, BLD, FP), pp. 473–479.
KDIR-2014-SaiaBC #modelling #semantics: Semantic Coherence-based User Profile Modeling in the Recommender Systems Context (RS, LB, SC), pp. 154–161.
KDIR-2014-TasciC: A Media Tracking and News Recommendation System (ST, IC), pp. 53–60.
KDIR-2014-UtkuA #mobile: A Mobile Location-Aware Recommendation System (SU, CEA), pp. 176–183.
KEOD-2014-AliE #collaboration #semantics: Semantic-based Collaborative Filtering for Enhancing Recommendation (GA, AE), pp. 176–185.
KEOD-2014-TarakciC #using: Using Hypergraph-based User Profile in a Recommendation System (HT, NKC), pp. 27–35.
KMIS-2014-WangABN #semantics #towards: Towards a Recommender System from Semantic Traces for Decision Aid (NW, MHA, JPAB, EN), pp. 274–279.
MLDM-2014-DzubaB #mining #music: Mining Users Playbacks History for Music Recommendations (AD, DB), pp. 422–430.
RecSys-2014-AdamopoulosT14a #bias #collaboration #on the #probability: On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems (PA, AT), pp. 153–160.
RecSys-2014-Aiolli #feedback #optimisation: Convex AUC optimization for top-N recommendation with implicit feedback (FA), pp. 293–296.
RecSys-2014-Amatriain #problem #revisited: The recommender problem revisited (XA), pp. 397–398.
RecSys-2014-BachrachFGKKNP #using: Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (YB, YF, RGB, LK, NK, NN, UP), pp. 257–264.
RecSys-2014-BadenesBCGHMNPSSXYZ #automation #people #social #social media: System U: automatically deriving personality traits from social media for people recommendation (HB, MNB, JC, LG, EMH, JM, JWN, AP, JS, BAS, YX, HY, MXZ), pp. 373–374.
RecSys-2014-Ben-ShimonTFH #as a service #configuration management #monitoring: Configuring and monitoring recommender system as a service (DBS, AT, MF, JH), pp. 363–364.
RecSys-2014-BhagatWIT #learning #matrix #using: Recommending with an agenda: active learning of private attributes using matrix factorization (SB, UW, SI, NT), pp. 65–72.
RecSys-2014-Braunhofer: Hybridisation techniques for cold-starting context-aware recommender systems (MB), pp. 405–408.
RecSys-2014-BraunhoferCR #hybrid: Switching hybrid for cold-starting context-aware recommender systems (MB, VC, FR), pp. 349–352.
RecSys-2014-CantadorC #tutorial: Tutorial on cross-domain recommender systems (IC, PC), pp. 401–402.
RecSys-2014-Christakopoulou #independence: Moving beyond linearity and independence in top-N recommender systems (EC), pp. 409–412.
RecSys-2014-CremonesiQ #question: Cross-domain recommendations without overlapping data: myth or reality? (PC, MQ), pp. 297–300.
RecSys-2014-DalyBKM #multi: Multi-criteria journey aware housing recommender system (EMD, AB, AK, RM), pp. 325–328.
RecSys-2014-DeryKRS #elicitation: Preference elicitation for narrowing the recommended list for groups (LND, MK, LR, BS), pp. 333–336.
RecSys-2014-EkstrandHWK #algorithm #difference: User perception of differences in recommender algorithms (MDE, FMH, MCW, JAK), pp. 161–168.
RecSys-2014-GaoTL #network #personalisation #social: Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.
RecSys-2014-GarcinFDABH #evaluation #online: Offline and online evaluation of news recommender systems at swissinfo.ch (FG, BF, OD, AA, CB, AH), pp. 169–176.
RecSys-2014-GueyeAN #algorithm: A parameter-free algorithm for an optimized tag recommendation list size (MG, TA, HN), pp. 233–240.
RecSys-2014-GuyG #social #tutorial: Social recommender system tutorial (IG, WG), pp. 403–404.
RecSys-2014-HaririMB #adaptation #interactive: Context adaptation in interactive recommender systems (NH, BM, RDB), pp. 41–48.
RecSys-2014-HarmanOAG #trust: Dynamics of human trust in recommender systems (JLH, JO, TFA, CG), pp. 305–308.
RecSys-2014-JannachF #data mining #mining #modelling #process: Recommendation-based modeling support for data mining processes (DJ, SF), pp. 337–340.
RecSys-2014-KellerR #e-commerce #framework #named #platform: Cosibon: an E-commerce like platform enabling bricks-and-mortar stores to use sophisticated product recommender systems (TK, MR), pp. 367–368.
RecSys-2014-KluverK #behaviour: Evaluating recommender behavior for new users (DK, JAK), pp. 121–128.
RecSys-2014-KrishnanPFG #bias #learning #social: A methodology for learning, analyzing, and mitigating social influence bias in recommender systems (SK, JP, MJF, KG), pp. 137–144.
RecSys-2014-LingLK #approach: Ratings meet reviews, a combined approach to recommend (GL, MRL, IK), pp. 105–112.
RecSys-2014-Liu0L: Recommending user generated item lists (YL, MX, LVSL), pp. 185–192.
RecSys-2014-LiuA #framework #towards: Towards a dynamic top-N recommendation framework (XL, KA), pp. 217–224.
RecSys-2014-LoniS #library #named: WrapRec: an easy extension of recommender system libraries (BL, AS), pp. 377–378.
RecSys-2014-Mayeku #personalisation: Enhancing personalization and learner engagement through context-aware recommendation in TEL (BM), pp. 413–415.
RecSys-2014-Nguyen #lifecycle: Improving recommender systems: user roles and lifecycles (TTN), pp. 417–420.
RecSys-2014-NoiaORTS #analysis #towards: An analysis of users’ propensity toward diversity in recommendations (TDN, VCO, JR, PT, EDS), pp. 285–288.
RecSys-2014-PalovicsBKKF #online: Exploiting temporal influence in online recommendation (RP, AAB, LK, TK, EF), pp. 273–280.
RecSys-2014-PedroK #collaboration: Question recommendation for collaborative question answering systems with RankSLDA (JSP, AK), pp. 193–200.
RecSys-2014-PeraN14a #automation: Automating readers’ advisory to make book recommendations for K-12 readers (MSP, YKN), pp. 9–16.
RecSys-2014-SaidB #benchmark #comparative #evaluation #framework #metric: Comparative recommender system evaluation: benchmarking recommendation frameworks (AS, AB), pp. 129–136.
RecSys-2014-SaidB14a #evaluation #named #tool support: Rival: a toolkit to foster reproducibility in recommender system evaluation (AS, AB), pp. 371–372.
RecSys-2014-SaidDLT #challenge: Recommender systems challenge 2014 (AS, SD, BL, DT), pp. 387–388.
RecSys-2014-SaveskiM #learning: Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
RecSys-2014-SedhainSBXC #collaboration #social: Social collaborative filtering for cold-start recommendations (SS, SS, DB, LX, JC), pp. 345–348.
RecSys-2014-SeminarioW: Attacking item-based recommender systems with power items (CES, DCW), pp. 57–64.
RecSys-2014-SuiB #feedback #online #rank: Clinical online recommendation with subgroup rank feedback (YS, JWB), pp. 289–292.
RecSys-2014-SureshRE #mining: Aspect-based opinion mining and recommendationsystem for restaurant reviews (VS, SR, ME), pp. 361–362.
RecSys-2014-TangJLL #personalisation: Ensemble contextual bandits for personalized recommendation (LT, YJ, LL, TL), pp. 73–80.
RecSys-2014-TrevisiolASJ #graph: Cold-start news recommendation with domain-dependent browse graph (MT, LMA, RS, AJ), pp. 81–88.
RecSys-2014-Vahedian #hybrid #network: Weighted hybrid recommendation for heterogeneous networks (FV), pp. 429–432.
RecSys-2014-VanchinathanNBK #process: Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.
RecSys-2014-VargasBKC: Coverage, redundancy and size-awareness in genre diversity for recommender systems (SV, LB, AK, PC), pp. 209–216.
RecSys-2014-VargasC: Improving sales diversity by recommending users to items (SV, PC), pp. 145–152.
RecSys-2014-WaldnerV #exclamation #game studies #timeline #twitter: Emphasize, don’t filter!: displaying recommendations in Twitter timelines (WW, JV), pp. 313–316.
RecSys-2014-XuPA #predict #ranking: Controlled experimentation in recommendations, ranking & response prediction (YX, RP, JA), p. 389.
RecSys-2014-YangAR #constraints #online: Question recommendation with constraints for massive open online courses (DY, DA, CPR), pp. 49–56.
RecSys-2014-Zhang: Browser-oriented universal cross-site recommendation and explanation based on user browsing logs (YZ), pp. 433–436.
RecSys-2014-Zheng #algorithm #similarity: Deviation-based and similarity-based contextual SLIM recommendation algorithms (YZ), pp. 437–440.
RecSys-2014-ZhengMB #algorithm #named: CSLIM: contextual SLIM recommendation algorithms (YZ, BM, RDB), pp. 301–304.
SEKE-2014-TianWHZG #feedback #using #web #web service: Cold-Start Web Service Recommendation Using Implicit Feedback (GT, JW, KH, WZ, PG), pp. 371–376.
SIGIR-2014-ChengSM #adaptation #music #named #personalisation #social: Just-for-me: an adaptive personalization system for location-aware social music recommendation (ZC, JS, TM), pp. 1267–1268.
SIGIR-2014-ChengYWL #behaviour #multi: Group latent factor model for recommendation with multiple user behaviors (JC, TY, JW, HL), pp. 995–998.
SIGIR-2014-ChenJZBZSY #category theory: Does product recommendation meet its waterloo in unexplored categories?: no, price comes to help (JC, QJ, SZ, SB, LZ, ZS, YY), pp. 667–676.
SIGIR-2014-GrausDTWR #communication #email #enterprise #graph #using: Recipient recommendation in enterprises using communication graphs and email content (DG, DvD, MT, WW, MdR), pp. 1079–1082.
SIGIR-2014-Ifada #modelling #personalisation #topic #using: A tag-based personalized item recommendation system using tensor modeling and topic model approaches (NI), p. 1280.
SIGIR-2014-LinSKC #modelling: New and improved: modeling versions to improve app recommendation (JL, KS, MYK, TSC), pp. 647–656.
SIGIR-2014-LivneGTDA #difference #named #using: CiteSight: supporting contextual citation recommendation using differential search (AL, VG, JT, STD, EA), pp. 807–816.
SIGIR-2014-LiWM #social: A revisit to social network-based recommender systems (HL, DW, NM), pp. 1239–1242.
SIGIR-2014-LuLMWXW #interactive #microblog #topic: Computing and applying topic-level user interactions in microblog recommendation (XL, PL, HM, SW, AX, BW), pp. 843–846.
SIGIR-2014-Ma #on the #social: On measuring social friend interest similarities in recommender systems (HM), pp. 465–474.
SIGIR-2014-NguyenKB #process: Gaussian process factorization machines for context-aware recommendations (TVN, AK, LB), pp. 63–72.
SIGIR-2014-RonenGKB #community #social #social media: Recommending social media content to community owners (IR, IG, EK, MB), pp. 243–252.
SIGIR-2014-SchedlVF #microblog #music: User geospatial context for music recommendation in microblogs (MS, AV, KF), pp. 987–990.
SIGIR-2014-SedhaiS #hashtag #twitter: Hashtag recommendation for hyperlinked tweets (SS, AS), pp. 831–834.
SIGIR-2014-TangWZ: Cross-language context-aware citation recommendation in scientific articles (XT, XW, XZ), pp. 817–826.
SIGIR-2014-Vargas #evaluation #information retrieval: Novelty and diversity enhancement and evaluation in recommender systems and information retrieval (SV), p. 1281.
SIGIR-2014-YaoHHZ #modelling #trust: Modeling dual role preferences for trust-aware recommendation (WY, JH, GH, YZ), pp. 975–978.
SIGIR-2014-YaoSNAL #internet: Exploring recommendations in internet of things (LY, QZS, AHHN, HA, XL), pp. 855–858.
SIGIR-2014-ZhangL0ZLM #analysis #modelling #sentiment: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (YZ, GL, MZ, YZ, YL, SM), pp. 83–92.
SIGIR-2014-ZhangTZX #algorithm: Addressing cold start in recommender systems: a semi-supervised co-training algorithm (MZ, JT, XZ, XX), pp. 73–82.
SIGIR-2014-ZhangWRS #performance: Preference preserving hashing for efficient recommendation (ZZ, QW, LR, LS), pp. 183–192.
SIGIR-2014-ZhouKWAD #detection: Detection of abnormal profiles on group attacks in recommender systems (WZ, YSK, JW, SA, GD), pp. 955–958.
SIGIR-2014-ZhuHLT: Bundle recommendation in ecommerce (TZ, PH, JL, LT), pp. 657–666.
SKY-2014-ExmanN #network #performance #social: Location-based Fast Recommendation Social Network (IE, EN), pp. 55–62.
ASE-2014-BavotaPTPOC #refactoring: Recommending refactorings based on team co-maintenance patterns (GB, SP, NT, MDP, RO, GC), pp. 337–342.
ASE-2014-MkaouerKBDC #interactive #optimisation #refactoring #using: Recommendation system for software refactoring using innovization and interactive dynamic optimization (MWM, MK, SB, KD, MÓC), pp. 331–336.
SAC-2014-ChenCWD #scalability: Instant expert hunting: building an answerer recommender system for a large scale Q&A website (TC, JC, HW, YD), pp. 260–265.
SAC-2014-ChenZTWS #modelling: Comparing the staples in latent factor models for recommender systems (CC, LZ, AT, KW, SV), pp. 91–96.
SAC-2014-GuoZTBY #empirical #trust: From ratings to trust: an empirical study of implicit trust in recommender systems (GG, JZ, DT, AB, NYS), pp. 248–253.
SAC-2014-HongHKK #mobile #music #smarttech: Context-aware music recommendation in mobile smart devices (JH, WSH, JHK, SWK), pp. 1463–1468.
SAC-2014-LiuMHHSC #algorithm #hybrid #twitter: A hybrid algorithm for recommendation twitter peers (JNKL, ZM, YXH, YLH, SCKS, VWSC), pp. 644–649.
SAC-2014-NgoPLS #image #named #query: Recommend-Me: recommending query regions for image search (TDN, SP, DDL, SS), pp. 913–918.
SAC-2014-RolimBCCAPM #approach #multimodal: A recommendation approach for digital TV systems based on multimodal features (RR, FB, AC, GC, HOdA, AP, AFM), pp. 289–291.
SAC-2014-ShangHHCK #personalisation #towards: Beyond personalization and anonymity: towards a group-based recommender system (SS, YH, PH, PC, SRK), pp. 266–273.
SAC-2014-UnoI #music #named: MALL: a life log based music recommendation system and portable music player (AU, TI), pp. 939–944.
SAC-2014-WangMLG #social: Recommendation based on weighted social trusts and item relationships (DW, JM, TL, LG), pp. 254–259.
SAC-2014-YangZL #algorithm #debugging #developer #effectiveness #multi: Utilizing a multi-developer network-based developer recommendation algorithm to fix bugs effectively (GY, TZ, BL), pp. 1134–1139.
SAC-2014-ZhengBM #empirical: Splitting approaches for context-aware recommendation: an empirical study (YZ, RDB, BM), pp. 274–279.
HT-2013-WuCH #using: Using personality to adjust diversity in recommender systems (WW, LC, LH), pp. 225–229.
HT-2013-YangZYW #personalisation #sentiment: A sentiment-enhanced personalized location recommendation system (DY, DZ, ZY, ZW), pp. 119–128.
JCDL-2013-CarageaSMG: Can’t see the forest for the trees?: a citation recommendation system (CC, AS, PM, CLG), pp. 111–114.
JCDL-2013-SugiyamaK: Exploiting potential citation papers in scholarly paper recommendation (KS, MYK), pp. 153–162.
JCDL-2013-TuarobPG #automation #metadata #modelling #probability #topic #using: Automatic tag recommendation for metadata annotation using probabilistic topic modeling (ST, LCP, CLG), pp. 239–248.
SIGMOD-2013-VartakM #named: CHIC: a combination-based recommendation system (MV, SM), pp. 981–984.
TPDL-2013-BeelLG #research: Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling (JB, SL, MG), pp. 391–395.
TPDL-2013-BeelLGN #multi #persistent: Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times (JB, SL, MG, AN), pp. 386–390.
TPDL-2013-BeelLNG #gender: The Impact of Demographics (Age and Gender) and Other User-Characteristics on Evaluating Recommender Systems (JB, SL, AN, MG), pp. 396–400.
VLDB-2013-ChenYYC #named #twitter: TeRec: A Temporal Recommender System Over Tweet Stream (CC, HY, JY, BC), pp. 1254–1257.
VLDB-2013-SarwatAM #database #relational: A RecDB in Action: Recommendation Made Easy in Relational Databases (MS, JLA, MFM), pp. 1242–1245.
ITiCSE-WGR-2013-ShumbaFSTFTSABH #women: Cybersecurity, women and minorities: findings and recommendations from a preliminary investigation (RS, KFB, ES, CT, GF, CT, CS, GA, RB, LH), pp. 1–14.
MSR-2013-NaguibNBH #debugging #process #using: Bug report assignee recommendation using activity profiles (HN, NN, BB, DH), pp. 22–30.
MSR-2013-ShokripourAKZ #debugging #why: Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendation (RS, JA, ZMK, SZ), pp. 2–11.
MSR-2013-XiaLWZ: Tag recommendation in software information sites (XX, DL, XW, BZ), pp. 287–296.
WCRE-2013-SalesTMV #dependence #refactoring #set #using: Recommending Move Method refactorings using dependency sets (VS, RT, LFM, MTV), pp. 232–241.
WCRE-2013-ThungLL #automation #library: Automated library recommendation (FT, DL, JLL), pp. 182–191.
WCRE-2013-XiaLWZ #debugging #developer: Accurate developer recommendation for bug resolution (XX, DL, XW, BZ), pp. 72–81.
ICALP-v2-2013-BachrachP #big data #performance #pseudo #sketching #using: Sketching for Big Data Recommender Systems Using Fast Pseudo-random Fingerprints (YB, EP), pp. 459–471.
CSCW-2013-SaidFJA #algorithm #collaboration #evaluation: User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm (AS, BF, BJJ, SA), pp. 1399–1408.
HCI-AS-2013-BitontoLRR #collaboration #process: Recommendation of Collaborative Activities in E-learning Environments (PDB, ML, TR, VR), pp. 484–492.
HCI-UC-2013-Baguma #design #mobile: Mobile Money Services in Uganda: Design Gaps and Recommendations (RB), pp. 249–258.
HCI-UC-2013-BelliniBNP #network: A Static and Dynamic Recommendations System for Best Practice Networks (PB, IB, PN, MP), pp. 259–268.
HIMI-D-2013-IsogaiN #modelling #motivation #music: Modeling of Music Recommendation Methods to Promote the User’s Singing Motivation — For Next-Generation Japanese Karaoke Systems (SI, MN), pp. 439–448.
HIMI-D-2013-ShigeyoshiTSTIU #empirical #energy #social: Social Experiment on Advisory Recommender System for Energy-Saving (HS, KT, RS, HT, SI, TU), pp. 545–554.
HIMI-LCCB-2013-ShiTS #consistency #nondeterminism #online: Timing and Basis of Online Product Recommendation: The Preference Inconsistency Paradox (AS, CHT, CLS), pp. 531–539.
HIMI-LCCB-2013-WuN #interactive #process: Integrating the Anchoring Process with Preference Stability for Interactive Movie Recommendations (ICW, YFN), pp. 639–648.
OCSC-2013-Popescu13b #problem: Group Recommender Systems as a Voting Problem (GP), pp. 412–421.
ICEIS-v1-2013-Al-ShamriA #collaboration #correlation: Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems (MYHAS, NHAA), pp. 409–414.
ICEIS-v1-2013-Kozmina: Adding Recommendations to OLAP Reporting Tool (NK), pp. 169–176.
ICEIS-v2-2013-DjuanaXLJC #ontology #problem: An Ontology-based Method for Sparsity Problem in Tag Recommendation (ED, YX, YL, AJ, CC), pp. 467–474.
ICEIS-v2-2013-KuCY #approach: Effect of Product Type and Recommendation Approach on Consumers’ Intention to Purchase Recommended Products (YCK, CHC, CSY), pp. 475–480.
ICEIS-v2-2013-PanfilenkoHEL #architecture #model transformation: Model Transformation Recommendations for Service-Oriented Architectures (DVP, KH, BE, EL), pp. 248–256.
ICEIS-v2-2013-SierraCVCV #education #re-engineering: Microworld-type Ethnoeducational Computer Materials to Support the Teaching of Nasa-Yuwe — Recommendations from a Software Engineering Disciplines Viewpoint for Constructing Microworld-type Ethnoeducational Materials Aimed at Supporting Nasa Yuwe Language Teaching (LMS, EASC, JAV, TRC, EMV), pp. 526–531.
ICEIS-v2-2013-ZhangWSP #linked data #network #open data #social: Event Recommendation in Social Networks with Linked Data Enablement (YZ, HW, VSS, VKP), pp. 371–379.
CIKM-2013-FerenceYL #network #social: Location recommendation for out-of-town users in location-based social networks (GF, MY, WCL), pp. 721–726.
CIKM-2013-LiuLAM #mining #personalisation: Personalized point-of-interest recommendation by mining users’ preference transition (XL, YL, KA, CM), pp. 733–738.
CIKM-2013-LiYZ: Scientific articles recommendation (YL, MY, Z(Z), pp. 1147–1156.
CIKM-2013-ThostVS #query: Query matching for report recommendation (VT, KV, DS), pp. 1391–1400.
CIKM-2013-ZhaoLHCH #network #social: Community-based user recommendation in uni-directional social networks (GZ, MLL, WH, WC, HH), pp. 189–198.
ECIR-2013-BelemMAG: Exploiting Novelty and Diversity in Tag Recommendation (FB, EFM, JMA, MAG), pp. 380–391.
ECIR-2013-Cleger-TamayoFHT #predict #quality: Being Confident about the Quality of the Predictions in Recommender Systems (SCT, JMFL, JFH, NT), pp. 411–422.
ECIR-2013-McParlaneMW #detection #semantics: Detecting Friday Night Party Photos: Semantics for Tag Recommendation (PJM, YM, IW), pp. 756–759.
ECIR-2013-ZhuGCLN #graph #query: Recommending High Utility Query via Session-Flow Graph (XZ, JG, XC, YL, WN), pp. 642–655.
KDD-2013-ChenHL #multi: Making recommendations from multiple domains (WC, WH, MLL), pp. 892–900.
KDD-2013-KabburNK #modelling #named #similarity: FISM: factored item similarity models for top-N recommender systems (SK, XN, GK), pp. 659–667.
KDD-2013-LiuFYX #learning: Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDD-2013-NiemannW #approach #collaboration: A new collaborative filtering approach for increasing the aggregate diversity of recommender systems (KN, MW), pp. 955–963.
KDD-2013-YinLLW #perspective: Silence is also evidence: interpreting dwell time for recommendation from psychological perspective (PY, PL, WCL, MW), pp. 989–997.
KDD-2013-YinSCHC #named: LCARS: a location-content-aware recommender system (HY, YS, BC, ZH, LC), pp. 221–229.
KDD-2013-ZhangWF: Combining latent factor model with location features for event-based group recommendation (WZ, JW, WF), pp. 910–918.
KDIR-KMIS-2013-BerkaniN #collaboration #learning #semantics: Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIR-KMIS-2013-NartTF #automation #personalisation #using: Personalized Recommendation and Explanation by using Keyphrases Automatically extracted from Scientific Literature (DDN, CT, FF), pp. 96–103.
KDIR-KMIS-2013-OliveiraOC #process #student: Recommending the Right Activities based on the Needs of each Student (EO, MGdO, PMC), pp. 183–190.
MLDM-2013-ChungJKL #identification #personalisation: Personalized Expert-Based Recommender System: Training C-SVM for Personalized Expert Identification (YC, HWJ, JK, JHL), pp. 434–441.
RecSys-2013-Adamopoulos #predict #rating: Beyond rating prediction accuracy: on new perspectives in recommender systems (PA), pp. 459–462.
RecSys-2013-AdamopoulosT #collaboration #predict #using: Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems (PA, AT), pp. 351–354.
RecSys-2013-AharonABLABLRS #named #online #persistent #set: OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings (MA, NA, EB, RL, RA, TB, LL, RR, OS), pp. 375–378.
RecSys-2013-AhnPLL #graph: A heterogeneous graph-based recommendation simulator (YA, SP, SL, SgL), pp. 471–472.
RecSys-2013-Aiolli #dataset #performance #scalability: Efficient top-n recommendation for very large scale binary rated datasets (FA), pp. 273–280.
RecSys-2013-AlanaziB #markov #modelling #using: A people-to-people content-based reciprocal recommender using hidden markov models (AA, MB), pp. 303–306.
RecSys-2013-AzariaHKEWN: Movie recommender system for profit maximization (AA, AH, SK, AE, OW, IN), pp. 121–128.
RecSys-2013-BabasCT #personalisation #what: You are what you consume: a bayesian method for personalized recommendations (KB, GC, ET), pp. 221–228.
RecSys-2013-BelemSAG #topic: Topic diversity in tag recommendation (FB, RLTS, JMA, MAG), pp. 141–148.
RecSys-2013-Ben-Shimon #algorithm: Anytime algorithms for top-N recommenders (DBS), pp. 463–466.
RecSys-2013-BlancoR #feedback: Acquiring user profiles from implicit feedback in a conversational recommender system (HB, FR), pp. 307–310.
RecSys-2013-BlankRS #graph #keyword: Leveraging the citation graph to recommend keywords (IB, LR, GS), pp. 359–362.
RecSys-2013-BugaychenkoD #network #personalisation #social: Musical recommendations and personalization in a social network (DB, AD), pp. 367–370.
RecSys-2013-ChowJKS #data analysis #difference: Differential data analysis for recommender systems (RC, HJ, BPK, GS), pp. 323–326.
RecSys-2013-CremonesiGQ: Evaluating top-n recommendations “when the best are gone” (PC, FG, MQ), pp. 339–342.
RecSys-2013-DoerfelJ #analysis #evaluation: An analysis of tag-recommender evaluation procedures (SD, RJ), pp. 343–346.
RecSys-2013-DongOSMS #sentiment: Sentimental product recommendation (RD, MPO, MS, KM, BS), pp. 411–414.
RecSys-2013-Dooms #generative #hybrid #personalisation: Dynamic generation of personalized hybrid recommender systems (SD), pp. 443–446.
RecSys-2013-DzyaburaT #how: Not by search alone: how recommendations complement search results (DD, AT), pp. 371–374.
RecSys-2013-Ester #network #social: Recommendation in social networks (ME), pp. 491–492.
RecSys-2013-GaoTHL #network #social: Exploring temporal effects for location recommendation on location-based social networks (HG, JT, XH, HL), pp. 93–100.
RecSys-2013-GarcinDF #personalisation: Personalized news recommendation with context trees (FG, CD, BF), pp. 105–112.
RecSys-2013-GarcinF #framework #personalisation: PEN RecSys: a personalized news recommender systems framework (FG, BF), pp. 469–470.
RecSys-2013-GraschFR #interactive #named #speech #towards: ReComment: towards critiquing-based recommendation with speech interaction (PG, AF, FR), pp. 157–164.
RecSys-2013-Guo #similarity #trust: Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems (GG), pp. 451–454.
RecSys-2013-GuoZTY #e-commerce: Prior ratings: a new information source for recommender systems in e-commerce (GG, JZ, DT, NYS), pp. 383–386.
RecSys-2013-HammarKN #e-commerce #using: Using maximum coverage to optimize recommendation systems in e-commerce (MH, RK, BJN), pp. 265–272.
RecSys-2013-HaririMB: Query-driven context aware recommendation (NH, BM, RDB), pp. 9–16.
RecSys-2013-HuE #modelling #online #social #social media #topic: Spatial topic modeling in online social media for location recommendation (BH, ME), pp. 25–32.
RecSys-2013-HuY #learning #process: Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSys-2013-KaminskasRS #hybrid #music #using: Location-aware music recommendation using auto-tagging and hybrid matching (MK, FR, MS), pp. 17–24.
RecSys-2013-KaratzoglouBS #learning #rank: Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSys-2013-KhroufT #hybrid #linked data #open data #using: Hybrid event recommendation using linked data and user diversity (HK, RT), pp. 185–192.
RecSys-2013-KoenigsteinK #scalability #towards: Towards scalable and accurate item-oriented recommendations (NK, YK), pp. 419–422.
RecSys-2013-KoenigsteinP #embedded #feature model #matrix: Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection (NK, UP), pp. 129–136.
RecSys-2013-KrestelS #topic: Recommending patents based on latent topics (RK, PS), pp. 395–398.
RecSys-2013-LacerdaVZ #interactive: Exploratory and interactive daily deals recommendation (AL, AV, NZ), pp. 439–442.
RecSys-2013-MouraoRKM #hybrid: Exploiting non-content preference attributes through hybrid recommendation method (FM, LCdR, JAK, WMJ), pp. 177–184.
RecSys-2013-NewellM #design #evaluation: Design and evaluation of a client-side recommender system (CN, LM), pp. 473–474.
RecSys-2013-NguyenKWHEWR #experience #rating #user interface: Rating support interfaces to improve user experience and recommender accuracy (TTN, DK, TYW, PMH, MDE, MCW, JR), pp. 149–156.
RecSys-2013-OstuniNSM #feedback #linked data #open data: Top-N recommendations from implicit feedback leveraging linked open data (VCO, TDN, EDS, RM), pp. 85–92.
RecSys-2013-PanCCY #personalisation #social: Diffusion-aware personalized social update recommendation (YP, FC, KC, YY), pp. 69–76.
RecSys-2013-PeraN #personalisation #what: What to read next?: making personalized book recommendations for K-12 users (MSP, YKN), pp. 113–120.
RecSys-2013-PessemierDM13a: A food recommender for patients in a care facility (TDP, SD, LM), pp. 209–212.
RecSys-2013-PizzatoB #network #people #social: Beyond friendship: the art, science and applications of recommending people to people in social networks (LAP, AB), pp. 495–496.
RecSys-2013-PuF #comprehension #matrix #relational: Understanding and improving relational matrix factorization in recommender systems (LP, BF), pp. 41–48.
RecSys-2013-RonenKZN #collaboration: Selecting content-based features for collaborative filtering recommenders (RR, NK, EZ, NN), pp. 407–410.
RecSys-2013-RonenKZSYH #named: Sage: recommender engine as a cloud service (RR, NK, EZ, MS, RY, NHW), pp. 475–476.
RecSys-2013-SaayaRSS #challenge #web: The curated web: a recommendation challenge (ZS, RR, MS, BS), pp. 101–104.
RecSys-2013-SavirBS: Recommending improved configurations for complex objects with an application in travel planning (AS, RIB, GS), pp. 391–394.
RecSys-2013-Seminario #collaboration #robust: Accuracy and robustness impacts of power user attacks on collaborative recommender systems (CES), pp. 447–450.
RecSys-2013-SharmaY #community #learning: Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSys-2013-Shi #approach #graph #similarity: Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach (LS), pp. 57–64.
RecSys-2013-SilbermannBR: Sample selection for MCMC-based recommender systems (TS, IB, SR), pp. 403–406.
RecSys-2013-Steck #evaluation #predict #ranking: Evaluation of recommendations: rating-prediction and ranking (HS), pp. 213–220.
RecSys-2013-SuYCY #personalisation #ranking: Set-oriented personalized ranking for diversified top-n recommendation (RS, LY, KC, YY), pp. 415–418.
RecSys-2013-TaghaviBS: Agent-based computational investing recommender system (MT, KB, ES), pp. 455–458.
RecSys-2013-TianJ #graph #using: Recommending scientific articles using bi-relational graph-based iterative RWR (GT, LJ), pp. 399–402.
RecSys-2013-VahabiALBL #orthogonal #query: Orthogonal query recommendation (HV, MA, DL, RABY, ALO), pp. 33–40.
RecSys-2013-WangHZL #collaboration #multi #on the fly #online: Online multi-task collaborative filtering for on-the-fly recommender systems (JW, SCHH, PZ, ZL), pp. 237–244.
RecSys-2013-WestonYW #learning #rank #statistics: Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
RecSys-2013-WilsonS #collaboration: When power users attack: assessing impacts in collaborative recommender systems (DCW, CES), pp. 427–430.
RecSys-2013-WuLCHLCH #online #personalisation: Personalized next-song recommendation in online karaokes (XW, QL, EC, LH, JL, CC, GH), pp. 137–140.
RecSys-2013-XuBATMK: Catch-up TV recommendations: show old favourites and find new ones (MX, SB, SA, ST, AM, IK), pp. 285–294.
RecSys-2013-YuRSSKGNH #feedback #network: Recommendation in heterogeneous information networks with implicit user feedback (XY, XR, YS, BS, UK, QG, BN, JH), pp. 347–350.
RecSys-2013-ZhangP #social #social media: Recommending branded products from social media (YZ, MP), pp. 77–84.
RecSys-2013-ZhangSKH #artificial reality #using: Improving augmented reality using recommender systems (ZZ, SS, SRK, PH), pp. 173–176.
SEKE-2013-DuHCLH #incremental #named #personalisation: ABEY: an Incremental Personalized Method Based on Attribute Entropy for Recommender Systems (XD, TH, ZC, JL, CH), pp. 318–321.
SEKE-2013-WangWTZ #evaluation #named #network #trust: STERS: A System for Service Trustworthiness Evaluation and Recommendation based on the Trust Network (YW, JW, YT, JZ), pp. 322–325.
SIGIR-2013-BalogR #classification #cumulative #ranking: Cumulative citation recommendation: classification vs. ranking (KB, HR), pp. 941–944.
SIGIR-2013-Belem: Beyond relevance: on novelty and diversity in tag recommendation (FB), p. 1140.
SIGIR-2013-ChenHL #modelling: Modeling user’s receptiveness over time for recommendation (WC, WH, MLL), pp. 373–382.
SIGIR-2013-ChenHL13a #feedback: Tagcloud-based explanation with feedback for recommender systems (WC, WH, MLL), pp. 945–948.
SIGIR-2013-FanLGLC #collaboration: Collaborative factorization for recommender systems (CF, YL, JG, ZL, XC), pp. 949–953.
SIGIR-2013-FeildA #query: Task-aware query recommendation (HAF, JA), pp. 83–92.
SIGIR-2013-KarkaliPV #realtime: Match the news: a firefox extension for real-time news recommendation (MK, DP, MV), pp. 1117–1118.
SIGIR-2013-LinSKC #modelling #twitter: Addressing cold-start in app recommendation: latent user models constructed from twitter followers (JL, KS, MYK, TSC), pp. 283–292.
SIGIR-2013-LukeSM #framework: A framework for specific term recommendation systems (TL, PS, PM), pp. 1093–1094.
SIGIR-2013-Ma #case study #social: An experimental study on implicit social recommendation (HM), pp. 73–82.
SIGIR-2013-McParlaneMJ #on the: On contextual photo tag recommendation (PJM, YM, JMJ), pp. 965–968.
SIGIR-2013-RoitmanCME #modelling: Modeling the uniqueness of the user preferences for recommendation systems (HR, DC, YM, IE), pp. 777–780.
SIGIR-2013-SappelliVK #personalisation #using: Recommending personalized touristic sights using google places (MS, SV, WK), pp. 781–784.
SIGIR-2013-SchallerHE #distributed #visitor: RecSys for distributed events: investigating the influence of recommendations on visitor plans (RS, MH, DE), pp. 953–956.
SIGIR-2013-SchedlS #hybrid #music #retrieval: Hybrid retrieval approaches to geospatial music recommendation (MS, DS), pp. 793–796.
SIGIR-2013-ShenWYC #multi: Multimedia recommendation: technology and techniques (JS, MW, SY, PC), p. 1131.
SIGIR-2013-ShouML00H: Competence-based song recommendation (LS, KM, XL, KC, GC, TH), pp. 423–432.
SIGIR-2013-SonKP #analysis #locality #semantics: A location-based news article recommendation with explicit localized semantic analysis (JWS, AYK, SBP), pp. 293–302.
SIGIR-2013-Wang0 #e-commerce: Opportunity model for e-commerce recommendation: right product; right time (JW, YZ), pp. 303–312.
SIGIR-2013-WanLGFC #social #social media: Informational friend recommendation in social media (SW, YL, JG, CF, XC), pp. 1045–1048.
SIGIR-2013-YuanCMSM: Time-aware point-of-interest recommendation (QY, GC, ZM, AS, NMT), pp. 363–372.
SKY-2013-ExmanK #anti #network #social: An Anti-Turing Test: Social Network Friends’ Recommendations (IE, AK), pp. 55–61.
SKY-2013-Gomes #how #ontology #representation #using: Representing Knowledge using Ontologies: How Search, Browse and Recommendation Can Be Performed (PG), pp. 1–3.
MoDELS-2013-KuschkeMR #modelling #process: Recommending Auto-completions for Software Modeling Activities (TK, PM, PR), pp. 170–186.
ASE-2013-ThungWLL #api #automation #feature model: Automatic recommendation of API methods from feature requests (FT, SW, DL, JLL), pp. 290–300.
ICSE-2013-Balachandran #automation #code review #quality #static analysis #using: Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation (VB), pp. 931–940.
ICSE-2013-LeeKS #named #visual notation: NavClus: a graphical recommender for assisting code exploration (SL, SK, MS), pp. 1315–1318.
ICSE-2013-SawadskyMJ #named #web: Reverb: recommending code-related web pages (NS, GCM, RJ), pp. 812–821.
SAC-2013-BlancoR #query: Inferring user utility for query revision recommendation (HB, FR), pp. 245–252.
SAC-2013-CapelleHHF #semantics #using: Semantic news recommendation using wordnet and bing similarities (MC, FH, AH, FF), pp. 296–302.
SAC-2013-CeccarelliGLNP #query #semantics: When entities meet query recommender systems: semantic search shortcuts (DC, SG, CL, FMN, RP), pp. 933–938.
SAC-2013-ChenNKX: Users segmentations for recommendation (LC, RN, SK, YX), pp. 279–280.
SAC-2013-HayashiIN #visual notation: A visual analytics tool for system logs adopting variable recommendation and feature-based filtering (AH, TI, SN), pp. 996–998.
SAC-2013-KoutrouliT: Credible recommendation exchange mechanism for P2P reputation systems (EK, AT), pp. 1943–1948.
SAC-2013-LeeKP #hybrid: A tour recommendation service for electric vehicles based on a hybrid orienteering model (JL, SWK, GLP), pp. 1652–1654.
SAC-2013-LommatzschKA #hybrid #learning #modelling #semantics: Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SAC-2013-Manzato #feedback #metadata: gSVD++: supporting implicit feedback on recommender systems with metadata awareness (MGM), pp. 908–913.
SAC-2013-RokachSSCS: Recommending insurance riders (LR, GS, BS, EC, GS), pp. 253–260.
SAC-2013-ZengC #data fusion #matrix #semistructured data: Heterogeneous data fusion via matrix factorization for augmenting item, group and friend recommendations (WZ, LC), pp. 237–244.
SAC-2013-ZhangL #algorithm #debugging #developer #hybrid: A hybrid bug triage algorithm for developer recommendation (TZ, BL), pp. 1088–1094.
HT-2012-DellschaftS #quality: Measuring the influence of tag recommenders on the indexing quality in tagging systems (KD, SS), pp. 73–82.
PODS-2012-DengFG #complexity #on the #problem: On the complexity of package recommendation problems (TD, WF, FG), pp. 261–272.
SIGMOD-2012-BarbosaMLO #named #network #visualisation #web: VRRC: web based tool for visualization and recommendation on co-authorship network (EMB, MMM, GRL, JPMdO), p. 865.
SIGMOD-2012-PavlidisMCBBRYMHR #social #social media: Anatomy of a gift recommendation engine powered by social media (YP, MM, IC, AB, RB, RR, RY, MM, VH, AR), pp. 757–764.
VLDB-2012-KanagalAPJYP #behaviour #learning #taxonomy #using: Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior (BK, AA, SP, VJ, JY, LGP), pp. 956–967.
VLDB-2012-YinCLYC: Challenging the Long Tail Recommendation (HY, BC, JL, JY, CC), pp. 896–907.
CSMR-2012-HeinemannBHH #api: Identifier-Based Context-Dependent API Method Recommendation (LH, VB, MH, BH), pp. 31–40.
CSMR-2012-TerraVCB #architecture #refactoring: Recommending Refactorings to Reverse Software Architecture Erosion (RT, MTV, KC, RdSB), pp. 335–340.
WCRE-2012-SteidlHJ #analysis #network #using: Using Network Analysis for Recommendation of Central Software Classes (DS, BH, EJ), pp. 93–102.
AIIDE-2012-LeeBL #automation #machine learning: Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative (GL, VB, EAL).
CHI-2012-BauerCGSWWK #mobile #named: ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display (JSB, SC, BG, JWS, EW, NFW, JAK), pp. 1401–1410.
CHI-2012-NowakN #behaviour #monitoring #online: Effects of behavior monitoring and perceived system benefit in online recommender systems (MN, CN), pp. 2243–2246.
CHI-2012-OganWBRCLC #case study #collaboration #design: Collaboration in cognitive tutor use in latin America: field study and design recommendations (AO, EW, RSJdB, GRM, MJC, TL, AMJBdC), pp. 1381–1390.
CHI-2012-PiorkowskiFSBBJBS #empirical #information management: Reactive information foraging: an empirical investigation of theory-based recommender systems for programmers (DP, SDF, CS, CB, MMB, BEJ, RKEB, CS), pp. 1471–1480.
CSCW-2012-PriedhorskyPST #algorithm #evaluation #personalisation: Recommending routes in the context of bicycling: algorithms, evaluation, and the value of personalization (RP, DP, SS, LGT), pp. 979–988.
ICEIS-J-2012-GeJG #analysis: Bringing Diversity to Recommendation Lists — An Analysis of the Placement of Diverse Items (MG, DJ, FG), pp. 293–305.
ICEIS-v2-2012-GeJGH: Effects of the Placement of Diverse Items in Recommendation Lists (MG, DJ, FG, MH), pp. 201–208.
CIKM-2012-AntonellisSD: Dynamic covering for recommendation systems (IA, ADS, SD), pp. 26–34.
CIKM-2012-BaeK #classification #effectiveness: An effective category classification method based on a language model for question category recommendation on a cQA service (KB, YK), pp. 2255–2258.
CIKM-2012-BambaSGBF #concept #scalability #using: The twitaholic next door.: scalable friend recommender system using a concept-sensitive hash function (PB, JS, CG, NB, JF), pp. 2275–2278.
CIKM-2012-BlancoCLPS #exclamation #why: You should read this! let me explain you why: explaining news recommendations to users (RB, DC, CL, RP, FS), pp. 1995–1999.
CIKM-2012-CaoYDWW #graph #modelling #process #workflow: Graph-based workflow recommendation: on improving business process modeling (BC, JY, SD, DW, ZW), pp. 1527–1531.
CIKM-2012-CheungSM #social #synthesis #using: Using program synthesis for social recommendations (AC, ASL, SM), pp. 1732–1736.
CIKM-2012-Diaz-AvilesDGSN #online #topic #twitter #what: What is happening right now ... that interests me?: online topic discovery and recommendation in twitter (EDA, LD, ZG, LST, WN), pp. 1592–1596.
CIKM-2012-DuttingHW #trust: Maximizing revenue from strategic recommendations under decaying trust (PD, MH, IW), pp. 2283–2286.
CIKM-2012-FangS #approach #feedback #learning: A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKM-2012-GaoZLH #clustering #twitter: Twitter hyperlink recommendation with user-tweet-hyperlink three-way clustering (DG, RZ, WL, YH), pp. 2535–2538.
CIKM-2012-GuoMCJ #learning #social: Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKM-2012-HaKKFP: Top-N recommendation through belief propagation (JH, SHK, SWK, CF, SP), pp. 2343–2346.
CIKM-2012-HuangKCMGR: Recommending citations: translating papers into references (WH, SK, CC, PM, CLG, LR), pp. 1910–1914.
CIKM-2012-HwangLKL #on the #performance #using: On using category experts for improving the performance and accuracy in recommender systems (WSH, HJL, SWK, ML), pp. 2355–2358.
CIKM-2012-JiangCLYWZY #social: Social contextual recommendation (MJ, PC, RL, QY, FW, WZ, SY), pp. 45–54.
CIKM-2012-JiangCWYZY #multi #relational #social: Social recommendation across multiple relational domains (MJ, PC, FW, QY, WZ, SY), pp. 1422–1431.
CIKM-2012-KaratzoglouBCB #mobile: Climbing the app wall: enabling mobile app discovery through context-aware recommendations (AK, LB, KC, MB), pp. 2527–2530.
CIKM-2012-KoenigsteinRS #framework #matrix #performance #retrieval: Efficient retrieval of recommendations in a matrix factorization framework (NK, PR, YS), pp. 535–544.
CIKM-2012-LeePKL #graph #named #novel #ranking: PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems (SL, SP, MK, SgL), pp. 1637–1641.
CIKM-2012-LiangXTC #topic: Time-aware topic recommendation based on micro-blogs (HL, YX, DT, PC), pp. 1657–1661.
CIKM-2012-LiKBCL #approach #named #probability #query: DQR: a probabilistic approach to diversified query recommendation (RL, BK, BB, RC, EL), pp. 16–25.
CIKM-2012-LiL #framework #named: MEET: a generalized framework for reciprocal recommender systems (LL, TL), pp. 35–44.
CIKM-2012-LinXLHL #named #personalisation #social: PRemiSE: personalized news recommendation via implicit social experts (CL, RX, LL, ZH, TL), pp. 1607–1611.
CIKM-2012-LiuTYL: Exploring personal impact for group recommendation (XL, YT, MY, WCL), pp. 674–683.
CIKM-2012-MahajanRTM #algorithm #named: LogUCB: an explore-exploit algorithm for comments recommendation (DKM, RR, CT, AM), pp. 6–15.
CIKM-2012-MeleBG #graph: The early-adopter graph and its application to web-page recommendation (IM, FB, AG), pp. 1682–1686.
CIKM-2012-OliveiraGBBAZG #automation #query: Automatic query expansion based on tag recommendation (VCdO, GdCMG, FB, WCB, JMA, NZ, MAG), pp. 1985–1989.
CIKM-2012-PapaioannouROA #assessment #distributed #effectiveness #web: A decentralized recommender system for effective web credibility assessment (TGP, JER, AO, KA), pp. 704–713.
CIKM-2012-RedaPTPS #named: Metaphor: a system for related search recommendations (AR, YP, MT, CP, SS), pp. 664–673.
CIKM-2012-SunWGM #hybrid #learning #rank: Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKM-2012-TangZLW #mining #quality: Incorporating occupancy into frequent pattern mining for high quality pattern recommendation (LT, LZ, PL, MW), pp. 75–84.
CIKM-2012-TorresHWS #query: Query recommendation for children (SDT, DH, IW, PS), pp. 2010–2014.
CIKM-2012-WanKC #social: Location-sensitive resources recommendation in social tagging systems (CW, BK, DWC), pp. 1960–1964.
CIKM-2012-ZhangLZW: Relation regularized subspace recommending for related scientific articles (QZ, JL, ZZ, LW), pp. 2503–2506.
CIKM-2012-ZhuGCL #behaviour #mining #query: More than relevance: high utility query recommendation by mining users’ search behaviors (XZ, JG, XC, YL), pp. 1814–1818.
ECIR-2012-YanZ #approach #community: A New Approach to Answerer Recommendation in Community Question Answering Services (ZY, JZ), pp. 121–132.
ICML-2012-PurushothamL #collaboration #matrix #social #topic: Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (SP, YL), p. 92.
KDD-2012-0002L #named: RecMax: exploiting recommender systems for fun and profit (AG, LVSL), pp. 1294–1302.
KDD-2012-BawabMC #personalisation #query #topic: Finding trending local topics in search queries for personalization of a recommendation system (ZAB, GHM, JFC), pp. 397–405.
KDD-2012-FengW #personalisation #social: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems (WF, JW), pp. 1276–1284.
KDD-2012-Posse #lessons learnt #network #scalability #social: Key lessons learned building recommender systems for large-scale social networks (CP), p. 587.
KDD-2012-ShenJ #learning #social: Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDD-2012-ShiA #dataset #mobile: GetJar mobile application recommendations with very sparse datasets (KS, KA), pp. 204–212.
KDD-2012-ShiZKYLW #named #network #semantics: HeteRecom: a semantic-based recommendation systemin heterogeneous networks (CS, CZ, XK, PSY, GL, BW), pp. 1552–1555.
KDD-2012-TangWSS #collaboration: Cross-domain collaboration recommendation (JT, SW, JS, HS), pp. 1285–1293.
KDD-2012-WuWCT #detection #hybrid #named: HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation (ZW, JW, JC, DT), pp. 985–993.
KDD-2012-YangSL #network #online #social: Circle-based recommendation in online social networks (XY, HS, YL), pp. 1267–1275.
KDIR-2012-BarbieriBCMR #modelling #probability #sequence: Probabilistic Sequence Modeling for Recommender Systems (NB, AB, MC, GM, ER), pp. 75–84.
KDIR-2012-DinsoreanuMHP #approach: A Unified Approach for Context-sensitive Recommendations (MD, FCM, OLH, RP), pp. 85–94.
KDIR-2012-FormosoFCC #performance #using: Using Neighborhood Pre-computation to Increase Recommendation Efficiency (VF, DF, FC, VC), pp. 333–335.
KDIR-2012-FukumotoMM #analysis #collaboration #sentiment: Collaborative Filtering based on Sentiment Analysis of Guest Reviews for Hotel Recommendation (FF, CM, SM), pp. 193–198.
KDIR-2012-LuongHGH #network #social: Exploiting Social Networks for Publication Venue Recommendations (HPL, TH, SG, KH), pp. 239–245.
KEOD-2012-AbdelazzizN #ontology #using: Enhancing the Results of Recommender Systems using Implicit Ontology Relations (LA, KN), pp. 5–14.
KEOD-2012-LuC #clustering #documentation #order: Bringing Order to Legal Documents — An Issue-based Recommendation System Via Cluster Association (QL, JGC), pp. 76–88.
KMIS-2012-SmirnovKS #collaboration #ontology: Ontology Matching in Context-driven Collaborative Recommending Systems (AVS, AK, NS), pp. 139–144.
RecSys-2012-AharonKLK #elicitation #personalisation: Dynamic personalized recommendation of comment-eliciting stories (MA, AK, RL, YK), pp. 209–212.
RecSys-2012-Amatriain: Building industrial-scale real-world recommender systems (XA), pp. 7–8.
RecSys-2012-AminYSBP #delivery #network #social: Social referral: leveraging network connections to deliver recommendations (MSA, BY, SS, AB, CP), pp. 273–276.
RecSys-2012-AntunesCG #approach #development: An approach to context-based recommendation in software development (BA, JC, PG), pp. 171–178.
RecSys-2012-BellufXG #case study #online #personalisation #scalability: Case study on the business value impact of personalized recommendations on a large online retailer (TB, LX, RG), pp. 277–280.
RecSys-2012-BostandjievOH #hybrid #interactive #named #visual notation: TasteWeights: a visual interactive hybrid recommender system (SB, JO, TH), pp. 35–42.
RecSys-2012-ChhabraR #named: CubeThat: news article recommender (SC, PR), pp. 295–296.
RecSys-2012-Diaz-AvilesDSN #realtime #social: Real-time top-n recommendation in social streams (EDA, LD, LST, WN), pp. 59–66.
RecSys-2012-Diaz-AvilesGN #rank: Swarming to rank for recommender systems (EDA, MG, WN), pp. 229–232.
RecSys-2012-EkstrandR #algorithm #predict: When recommenders fail: predicting recommender failure for algorithm selection and combination (MDE, JR), pp. 233–236.
RecSys-2012-GertnerLW #enterprise: Recommenders for the enterprise: event, contact, and group (ASG, BL, JW), pp. 299–300.
RecSys-2012-HaririMB #music #topic: Context-aware music recommendation based on latenttopic sequential patterns (NH, BM, RDB), pp. 131–138.
RecSys-2012-JiangJFZ: Recommending academic papers via users’ reading purposes (YJ, AJ, YF, DZ), pp. 241–244.
RecSys-2012-KarimiFNS #learning #matrix: Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSys-2012-Knijnenburg: Conducting user experiments in recommender systems (BPK), pp. 3–4.
RecSys-2012-KnijnenburgBOK #social: Inspectability and control in social recommenders (BPK, SB, JO, AK), pp. 43–50.
RecSys-2012-KoenigsteinNPS: The Xbox recommender system (NK, NN, UP, NS), pp. 281–284.
RecSys-2012-Landia #documentation #folksonomy: Utilising document content for tag recommendation in folksonomies (NL), pp. 325–328.
RecSys-2012-Lempel #challenge #web: Recommendation challenges in web media settings (RL), pp. 205–206.
RecSys-2012-LeviMDT: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system (AL, OM, CD, NT), pp. 115–122.
RecSys-2012-LeviMDT12a: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system demo (AL, OM, CD, NT), pp. 305–306.
RecSys-2012-LiuCCY #named: Enlister: baidu’s recommender system for the biggest chinese Q&A website (QL, TC, JC, DY), pp. 285–288.
RecSys-2012-LiuXCGXBZ: Influential seed items recommendation (QL, BX, EC, YG, HX, TB, YZ), pp. 245–248.
RecSys-2012-Manzato: Discovering latent factors from movies genres for enhanced recommendation (MGM), pp. 249–252.
RecSys-2012-MolingBR #feedback: Optimal radio channel recommendations with explicit and implicit feedback (OM, LB, FR), pp. 75–82.
RecSys-2012-Ninaus #heuristic #requirements #using: Using group recommendation heuristics for the prioritization of requirements (GN), pp. 329–332.
RecSys-2012-NingK #linear: Sparse linear methods with side information for top-n recommendations (XN, GK), pp. 155–162.
RecSys-2012-NoiaMOR #modelling #web: Exploiting the web of data in model-based recommender systems (TDN, RM, VCO, DR), pp. 253–256.
RecSys-2012-NunesH #overview #perspective: Personality-based recommender systems: an overview (MASNN, RH), pp. 5–6.
RecSys-2012-Parra #visualisation: Beyond lists: studying the effect of different recommendation visualizations (DP), pp. 333–336.
RecSys-2012-PessemierDM #design #evaluation: Design and evaluation of a group recommender system (TDP, SD, LM), pp. 225–228.
RecSys-2012-PraweshP #feedback #probability: Probabilistic news recommender systems with feedback (SP, BP), pp. 257–260.
RecSys-2012-RibeiroLVZ #multi: Pareto-efficient hybridization for multi-objective recommender systems (MTR, AL, AV, NZ), pp. 19–26.
RecSys-2012-RodriguezPZ #multi #optimisation: Multiple objective optimization in recommender systems (MR, CP, EZ), pp. 11–18.
RecSys-2012-SaidTH #challenge: The challenge of recommender systems challenges (AS, DT, AH), pp. 9–10.
RecSys-2012-SklarSH #realtime: Recommending interesting events in real-time with foursquare check-ins (MS, BS, AH), pp. 311–312.
RecSys-2012-StrickrothP #community #network #quality: High quality recommendations for small communities: the case of a regional parent network (SS, NP), pp. 107–114.
RecSys-2012-Wakeling #design #library: The user-centered design of a recommender system for a universal library catalogue (SW), pp. 337–340.
RecSys-2012-WuGRR #microblog: Making recommendations in a microblog to improve the impact of a focal user (SW, LG, WR, LR), pp. 265–268.
RecSys-2012-YangCZLY #feedback #mining #music: Local implicit feedback mining for music recommendation (DY, TC, WZ, QL, YY), pp. 91–98.
RecSys-2012-YangSGL #network #on the #social #using: On top-k recommendation using social networks (XY, HS, YG, YL), pp. 67–74.
RecSys-2012-Zanker #information management: The influence of knowledgeable explanations on users’ perception of a recommender system (MZ), pp. 269–272.
RecSys-2012-ZelenikB #information management: Reducing the sparsity of contextual information for recommender systems (DZ, MB), pp. 341–344.
RecSys-2012-ZhangTSWY #approach #image #semantics: A semantic approach to recommending text advertisements for images (WZ, LT, XS, HW, YY), pp. 179–186.
SEKE-2012-HuiLCDM #e-commerce #empirical: An Empirical Study on Recommendation Methods for Vertical B2C E-commerce (CH, JL, ZC, XD, WM), pp. 139–142.
SEKE-2012-RadulovicG #network #process #semantics: Semantic Technology Recommendation Based on the Analytic Network Process (FR, RGC), pp. 611–616.
SEKE-2012-SmithP #design pattern: Dynamically recommending design patterns (SS, DRP), pp. 499–504.
SIGIR-2012-AdeyanjuSAKRF #adaptation #concept #query: Adaptation of the concept hierarchy model with search logs for query recommendation on intranets (IAA, DS, MDA, UK, ANDR, MF), pp. 5–14.
SIGIR-2012-AgarwalCEW #online #personalisation: Personalized click shaping through lagrangian duality for online recommendation (DA, BCC, PE, XW), pp. 485–494.
SIGIR-2012-BonchiPSVV #performance #query: Efficient query recommendations in the long tail via center-piece subgraphs (FB, RP, FS, HV, RV), pp. 345–354.
SIGIR-2012-ChenCSX #music #named: Pictune: situational music recommendation from geotagged pictures (KC, GC, LS, FX), p. 1011.
SIGIR-2012-ChenCZJYY #collaboration #personalisation #twitter: Collaborative personalized tweet recommendation (KC, TC, GZ, OJ, EY, YY), pp. 661–670.
SIGIR-2012-Cleger-TamayoFH: Explaining neighborhood-based recommendations (SCT, JMFL, JFH), pp. 1063–1064.
SIGIR-2012-MaoLCCS #named: myDJ: recommending karaoke songs from one’s own voice (KM, XL, KC, GC, LS), p. 1009.
SIGIR-2012-PeraN #named: BReK12: a book recommender for K-12 users (MSP, YKN), pp. 1037–1038.
SIGIR-2012-QumsiyehN #multi #personalisation #predict: Predicting the ratings of multimedia items for making personalized recommendations (RQ, YKN), pp. 475–484.
SIGIR-2012-SaidJNPAS #case study #user study: Estimating the magic barrier of recommender systems: a user study (AS, BJJ, SN, TP, SA, CS), pp. 1061–1062.
SIGIR-2012-ShiKBLHO #named #optimisation: TFMAP: optimizing MAP for top-n context-aware recommendation (YS, AK, LB, ML, AH, NO), pp. 155–164.
SIGIR-2012-ShiZWLH #adaptation: Adaptive diversification of recommendation results via latent factor portfolio (YS, XZ, JW, ML, AH), pp. 175–184.
SIGIR-2012-XuJW #community: Dual role model for question recommendation in community question answering (FX, ZJ, BW), pp. 771–780.
SIGIR-2012-YeLL #approach #generative #social: Exploring social influence for recommendation: a generative model approach (MY, XL, WCL), pp. 671–680.
MoDELS-2012-MaraeeB #analysis #comparative #constraints #guidelines #modelling #uml: Inter-association Constraints in UML2: Comparative Analysis, Usage Recommendations, and Modeling Guidelines (AM, MB), pp. 302–318.
OOPSLA-2012-MusluBHEN #analysis #development #ide: Speculative analysis of integrated development environment recommendations (KM, YB, RH, MDE, DN), pp. 669–682.
RE-2012-Cleland-HuangMMA #traceability: Breaking the big-bang practice of traceability: Pushing timely trace recommendations to project stakeholders (JCH, PM, MM, SA), pp. 231–240.
FSE-2012-Murphy-HillJM #developer #development: Improving software developers’ fluency by recommending development environment commands (ERMH, RJ, GCM), p. 42.
ICSE-2012-HuangLXW #mining #repository #xml: Mining application repository to recommend XML configuration snippets (SH, YL, YX, WW), pp. 1451–1452.
ICSE-2012-McMillanHPCM #agile #prototype #source code: Recommending source code for use in rapid software prototypes (CM, NH, DP, JCH, BM), pp. 848–858.
ICSE-2012-MusluBHEN #ide: Improving IDE recommendations by considering global implications of existing recommendations (KM, YB, RH, MDE, DN), pp. 1349–1352.
ICSE-2012-ZhangYZFZZO #api #automation #parametricity: Automatic parameter recommendation for practical API usage (CZ, JY, YZ, JF, XZ, JZ, PO), pp. 826–836.
SAC-2012-ChenNX #collaboration: A common neighbour based two-way collaborative recommendation method (LC, RN, YX), pp. 214–215.
SAC-2012-HijikataKN #user satisfaction: The relation between user intervention and user satisfaction for information recommendation (YH, YK, SN), pp. 2002–2007.
SAC-2012-KuttyCN #modelling #using: A people-to-people recommendation system using tensor space models (SK, LC, RN), pp. 187–192.
SAC-2012-LageDD #effectiveness #microblog #towards: Towards effective group recommendations for microblogging users (RL, FAD, PD), pp. 923–928.
SAC-2012-ManzatoG #multi: A multimedia recommender system based on enriched user profiles (MGM, RG), pp. 975–980.
SAC-2012-MirizziNSR #semantics #web: Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations (RM, TDN, EDS, AR), pp. 403–405.
SAC-2012-TrabelsiMY #folksonomy #markov #modelling #named: HMM-CARe: Hidden Markov Models for context-aware tag recommendation in folksonomies (CT, BM, SBY), pp. 957–961.
SAC-2012-WangZHHZWT #mining #mobile: Context-aware role mining for mobile service recommendation (JW, CZ, CH, LH, LZ, RKW, JT), pp. 173–178.
CBSE-2011-MeloP #component #framework #open source: A component-based open-source framework for general-purpose recommender systems (FMM, ÁRPJ), pp. 67–72.
DocEng-2011-ChidlovskiiB #learning #metric #network #social: Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
JCDL-2011-LiZYWWW #gpu #social #using: A social network-aware top-N recommender system using GPU (RL, YZ, HY, XW, JW, BW), pp. 287–296.
JCDL-2011-NascimentoLSG #framework #independence #research: A source independent framework for research paper recommendation (CN, AHFL, ASdS, MAG), pp. 297–306.
JCDL-2011-SugiyamaK #research: Serendipitous recommendation for scholarly papers considering relations among researchers (KS, MYK), pp. 307–310.
SIGMOD-2011-ChandramouliLEM #named #realtime: StreamRec: a real-time recommender system (BC, JJL, AE, MFM), pp. 1243–1246.
VLDB-2011-LevandoskiELEMR #architecture #benchmark #metric #named #performance: RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures (JJL, MDE, ML, AE, MFM, JR), pp. 911–920.
VLDB-2011-MachanavajjhalaKS #personalisation #question #social: Personalized Social Recommendations — Accurate or Private? (AM, AK, ADS), pp. 440–450.
ITiCSE-2011-HarrachA #collaboration #learning #optimisation #process #using: Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ICSM-2011-LeeK #clustering: Clustering and recommending collections of code relevant to tasks (SL, SK), pp. 536–539.
WCRE-2011-SurianLLTLF #collaboration #developer #network #people: Recommending People in Developers’ Collaboration Network (DS, NL, DL, HT, EPL, CF), pp. 379–388.
VS-Games-2011-VescoukisD #evaluation: Disaster Management Evaluation and Recommendation (VCV, NDD), pp. 244–249.
CHI-2011-ChenNC #online #social: Speak little and well: recommending conversations in online social streams (JC, RN, EHhC), pp. 217–226.
CHI-2011-John #design #modelling #performance #predict #user interface #using: Using predictive human performance models to inspire and support UI design recommendations (BEJ), pp. 983–986.
CHI-2011-SchwindBH: I will do it, but i don’t like it: user reactions to preference-inconsistent recommendations (CS, JB, FWH), pp. 349–352.
CSCW-2011-GuyURPJ #enterprise: Do you want to know?: recommending strangers in the enterprise (IG, SU, IR, AP, MJ), pp. 285–294.
DUXU-v1-2011-BaltrunasLPR #mobile: Context-Aware Places of Interest Recommendations for Mobile Users (LB, BL, SP, FR), pp. 531–540.
HCI-DDA-2011-KogaT #topic #twitter #using: Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics (HK, TT), pp. 461–470.
HCI-ITE-2011-KuramotoYMT #interactive #multi: Recommendation System Based on Interaction with Multiple Agents for Users with Vague Intention (IK, AY, MM, YT), pp. 351–357.
HCI-MIIE-2011-AiharaKT #behaviour #cost analysis: Behavioral Cost-Based Recommendation Model for Wanderers in Town (KA, HK, HT), pp. 271–279.
HCI-UA-2011-OlivierWP #game studies #music #named: MusicTagger: Exploiting User Generated Game Data for Music Recommendation (HO, MW, NP), pp. 678–687.
HIMI-v1-2011-BreyerNSBK #personalisation: A Comprehensive Reference Model for Personalized Recommender Systems (MB, KN, CS, DB, AK), pp. 528–537.
HIMI-v1-2011-KoCECCKH: A Smart Movie Recommendation System (SKK, SMC, HSE, JWC, HC, LK, YSH), pp. 558–566.
OCSC-2011-PujariK #approach #machine learning #predict: A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
OCSC-2011-PuseyM #collaboration #design #learning #wiki: Assessments in Large- and Small-Scale Wiki Collaborative Learning Environments: Recommendations for Educators and Wiki Designers (PP, GM), pp. 60–68.
ICEIS-v2-2011-ChenGC #enterprise: Enterprise Knowledge Practice and Recommendation based on HOTP Model (BC, YG, DC), pp. 444–450.
ICEIS-v2-2011-WangS #education #information management: Application of Recommender Engine in Academic Degree and Postgraduate Education Knowledge Management System (XW, HS), pp. 455–458.
CIKM-2011-BoimMN #collaboration #refinement: Diversification and refinement in collaborative filtering recommender (RB, TM, SN), pp. 739–744.
CIKM-2011-DongBHRC #optimisation #personalisation: User action interpretation for personalized content optimization in recommender systems (AD, JB, XH, SR, YC), pp. 2129–2132.
CIKM-2011-DraidiPPV #named: P2Prec: a social-based P2P recommendation system (FD, EP, DP, GV), pp. 2593–2596.
CIKM-2011-DrosouP #database #named: ReDRIVE: result-driven database exploration through recommendations (MD, EP), pp. 1547–1552.
CIKM-2011-LiLSG #community #named #online #performance #privacy #social: YANA: an efficient privacy-preserving recommender system for online social communities (DL, QL, LS, NG), pp. 2269–2272.
CIKM-2011-LuHSY: Recommending citations with translation model (YL, JH, DS, HY), pp. 2017–2020.
CIKM-2011-MoghaddamJE #online #overview #personalisation #predict #quality: Review recommendation: personalized prediction of the quality of online reviews (SM, MJ, ME), pp. 2249–2252.
CIKM-2011-PeraN #personalisation: A personalized recommendation system on scholarly publications (MSP, YKN), pp. 2133–2136.
CIKM-2011-SeguelaS #hybrid: A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi (JS, GS), pp. 2265–2268.
CIKM-2011-ShiehLW: Recommendation in the end-to-end encrypted domain (JRS, CYL, JLW), pp. 915–924.
CIKM-2011-SongQF #visualisation: Hierarchical tag visualization and application for tag recommendations (YS, BQ, UF), pp. 1331–1340.
CIKM-2011-WangHLCH #learning: Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKM-2011-YanGC #higher-order #learning #query: Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
ECIR-2011-ChilukaAP #approach #predict #scalability: A Link Prediction Approach to Recommendations in Large-Scale User-Generated Content Systems (NC, NA, JAP), pp. 189–200.
ECIR-2011-HannonMS #twitter: Finding Useful Users on Twitter: Twittomender the Followee Recommender (JH, KM, BS), pp. 784–787.
ECIR-2011-MoshfeghiJ #collaboration: Role of Emotional Features in Collaborative Recommendation (YM, JMJ), pp. 738–742.
ECIR-2011-PhelanMBS #twitter #using: Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (OP, KM, MB, BS), pp. 448–459.
ECIR-2011-ShiLH11a #how #question #trust: How Far Are We in Trust-Aware Recommendation? (YS, ML, AH), pp. 704–707.
ECIR-2011-WartenaW #topic: Improving Tag-Based Recommendation by Topic Diversification (CW, MW), pp. 43–54.
KDD-2011-AgarwalCL #locality #modelling #multi: Localized factor models for multi-context recommendation (DA, BCC, BL), pp. 609–617.
KDD-2011-DrorKMS #exclamation: I want to answer; who has a question?: Yahoo! answers recommender system (GD, YK, YM, IS), pp. 1109–1117.
KDD-2011-GeLXTC #cost analysis: Cost-aware travel tour recommendation (YG, QL, HX, AT, JC), pp. 983–991.
KDD-2011-PradelSDGRUFD #case study: A case study in a recommender system based on purchase data (BP, SS, JD, SG, CR, NU, FFS, FDJ), pp. 377–385.
KDD-2011-WangB #collaboration #modelling #topic: Collaborative topic modeling for recommending scientific articles (CW, DMB), pp. 448–456.
KEOD-2011-HusakovaC #multi #ontology #simulation: Exploitation of Ontology-based Recommendation System with Multi-agent Simulations (MH, PC), pp. 433–436.
KMIS-2011-Borchardt #towards: Towards a Value-oriented KMS Recommendation for SME (UB), pp. 347–350.
RecSys-2011-Alam #clustering #web: Intelligent web usage clustering based recommender system (SA), pp. 367–370.
RecSys-2011-BaltrunasLR #matrix: Matrix factorization techniques for context aware recommendation (LB, BL, FR), pp. 301–304.
RecSys-2011-BarbieriCMO #approach #modelling: Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.
RecSys-2011-Bellogin #performance #predict: Predicting performance in recommender systems (AB), pp. 371–374.
RecSys-2011-BelloginCC #algorithm #comparison #evaluation: Precision-oriented evaluation of recommender systems: an algorithmic comparison (AB, PC, IC), pp. 333–336.
RecSys-2011-BourkeMS #people #social: Power to the people: exploring neighbourhood formations in social recommender system (SB, KM, BS), pp. 337–340.
RecSys-2011-BraunhoferKR #mobile #music: Recommending music for places of interest in a mobile travel guide (MB, MK, FR), pp. 253–256.
RecSys-2011-CamposDS #evaluation #matrix #predict #testing #towards: Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders (PGC, FD, MASM), pp. 309–312.
RecSys-2011-CelmaL #music #revisited: Music recommendation and discovery revisited (ÒC, PL), pp. 7–8.
RecSys-2011-Chen #design #interactive #interface #social: Interface and interaction design for group and social recommender systems (YC), pp. 363–366.
RecSys-2011-DalyG #effectiveness #social #using: Effective event discovery: using location and social information for scoping event recommendations (EMD, WG), pp. 277–280.
RecSys-2011-DayanKBRSASF #benchmark #framework #metric: Recommenders benchmark framework (AD, GK, NB, LR, BS, AA, RS, RF), pp. 353–354.
RecSys-2011-EkstrandLKR #ecosystem #research: Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit (MDE, ML, JAK, JR), pp. 133–140.
RecSys-2011-EkstrandLKR11a #composition #framework #named: LensKit: a modular recommender framework (MDE, ML, JK, JR), pp. 349–350.
RecSys-2011-Faridani #analysis #canonical #correlation #sentiment #using: Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search (SF), pp. 355–358.
RecSys-2011-ForbesZ #matrix: Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation (PF, MZ), pp. 261–264.
RecSys-2011-GantnerRFS #library #named: MyMediaLite: a free recommender system library (ZG, SR, CF, LST), pp. 305–308.
RecSys-2011-GorgoglionePT #behaviour #trust: The effect of context-aware recommendations on customer purchasing behavior and trust (MG, UP, AT), pp. 85–92.
RecSys-2011-GuzziRB #interactive #multi: Interactive multi-party critiquing for group recommendation (FG, FR, RDB), pp. 265–268.
RecSys-2011-Hurley #robust: Robustness of recommender systems (NJH), pp. 9–10.
RecSys-2011-JamaliHE #network #probability #rating #social: A generalized stochastic block model for recommendation in social rating networks (MJ, TH, ME), pp. 53–60.
RecSys-2011-KimE #personalisation #rank: Personalized PageRank vectors for tag recommendations: inside FolkRank (HNK, AES), pp. 45–52.
RecSys-2011-KnijnenburgRW #how #interactive: Each to his own: how different users call for different interaction methods in recommender systems (BPK, NJMR, MCW), pp. 141–148.
RecSys-2011-KnijnenburgWK #evaluation: A pragmatic procedure to support the user-centric evaluation of recommender systems (BPK, MCW, AK), pp. 321–324.
RecSys-2011-KoenigsteinDK #exclamation #modelling #music #taxonomy: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy (NK, GD, YK), pp. 165–172.
RecSys-2011-LeeL #analysis #behaviour #music: My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation (KL, KL), pp. 213–220.
RecSys-2011-LeeSKLL #graph #multi #random #ranking: Random walk based entity ranking on graph for multidimensional recommendation (SL, SiS, MK, DL, SgL), pp. 93–100.
RecSys-2011-LiuMLY #elicitation #rating: Wisdom of the better few: cold start recommendation via representative based rating elicitation (NNL, XM, CL, QY), pp. 37–44.
RecSys-2011-LiuMX #multi: Multi-criteria service recommendation based on user criteria preferences (LL, NM, DLX), pp. 77–84.
RecSys-2011-LiZL #integration #named #personalisation: LOGO: a long-short user interest integration in personalized news recommendation (LL, LZ, TL), pp. 317–320.
RecSys-2011-Makrehchi #learning #social #topic: Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSys-2011-PaparrizosCG: Machine learned job recommendation (IKP, BBC, AG), pp. 325–328.
RecSys-2011-PizzatoS #collaboration #people #probability: Stochastic matching and collaborative filtering to recommend people to people (LASP, CS), pp. 341–344.
RecSys-2011-PraweshP: The “top N” news recommender: count distortion and manipulation resistance (SP, BP), pp. 237–244.
RecSys-2011-PuCH #evaluation #framework: A user-centric evaluation framework for recommender systems (PP, LC, RH), pp. 157–164.
RecSys-2011-SabinC #named #online: myMicSound: an online sound-based microphone recommendation system (ATS, CLC), pp. 351–352.
RecSys-2011-SaidBLH #challenge: Challenge on context-aware movie recommendation: CAMRa2011 (AS, SB, EWDL, JH), pp. 385–386.
RecSys-2011-SekoYMM #behaviour #representation #using: Group recommendation using feature space representing behavioral tendency and power balance among members (SS, TY, MM, SyM), pp. 101–108.
RecSys-2011-Steck: Item popularity and recommendation accuracy (HS), pp. 125–132.
RecSys-2011-Sundaresan: Recommender systems at the long tail (NS), pp. 1–6.
RecSys-2011-SymeonidisTM #multi #network #predict #rating #social: Product recommendation and rating prediction based on multi-modal social networks (PS, ET, YM), pp. 61–68.
RecSys-2011-TayebiJEGF #named: CrimeWalker: a recommendation model for suspect investigation (MAT, MJ, ME, UG, RF), pp. 173–180.
RecSys-2011-Tschersich #design #guidelines #mobile: Design guidelines for mobile group recommender systems to handle inaccurate or missing location data (MT), pp. 359–362.
RecSys-2011-Tunkelang: Recommendations as a conversation with the user (DT), pp. 11–12.
RecSys-2011-VargasC #metric #rank: Rank and relevance in novelty and diversity metrics for recommender systems (SV, PC), pp. 109–116.
RecSys-2011-WangSS #e-commerce: Utilizing related products for post-purchase recommendation in e-commerce (JW, BS, NS), pp. 329–332.
RecSys-2011-WoerndlHBG #mobile: A model for proactivity in mobile, context-aware recommender systems (WW, JH, RB, DGV), pp. 273–276.
RecSys-2011-WuRR #monitoring #social #social media: Recommendations in social media for brand monitoring (SW, WR, LR), pp. 345–348.
RecSys-2011-XinS #matrix #multi #probability: Multi-value probabilistic matrix factorization for IP-TV recommendations (YX, HS), pp. 221–228.
RecSys-2011-YuanCZ #social: Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation (QY, LC, SZ), pp. 245–252.
RecSys-2011-YuPL #adaptation #social: Adaptive social similarities for recommender systems (LY, RP, ZL), pp. 257–260.
RecSys-2011-Zhang: Anchoring effects of recommender systems (JZ), pp. 375–378.
SEKE-2011-CaiZWXS #approach #component: Recommending Component by Citation: A Semi-supervised Approach for Determination (SC, YZ, LW, BX, WS), pp. 489–494.
SEKE-2011-ZhangSPCM #architecture #design #quality #towards: Towards Quality Based Solution Recommendation in Decision-Centric Architecture Design (LZ, YS, YP, XC, HM), pp. 776–781.
SIGIR-2011-BelemMPAG #multi: Associative tag recommendation exploiting multiple textual features (FB, EFM, TP, JMA, MAG), pp. 1033–1042.
SIGIR-2011-BelloginCC #analysis #hybrid #network #self #social: Self-adjusting hybrid recommenders based on social network analysis (AB, PC, IC), pp. 1147–1148.
SIGIR-2011-ChenC #web: Recommending ephemeral items at web scale (YC, JFC), pp. 1013–1022.
SIGIR-2011-LeungLL #clustering #collaboration #framework #named: CLR: a collaborative location recommendation framework based on co-clustering (KWTL, DLL, WCL), pp. 305–314.
SIGIR-2011-LiDS #concept #semantics #using: Semantic tag recommendation using concept model (CL, AD, AS), pp. 1159–1160.
SIGIR-2011-LiLS #social: Exploiting endorsement information and social influence for item recommendation (CTL, SDL, MKS), pp. 1131–1132.
SIGIR-2011-LinDW #image: Image annotation based on recommendation model (ZL, GD, JW), pp. 1097–1098.
SIGIR-2011-LiWLKP #named #personalisation #scalability: SCENE: a scalable two-stage personalized news recommendation system (LL, DW, TL, DK, BP), pp. 125–134.
SIGIR-2011-MaLKL #modelling #probability #web: Probabilistic factor models for web site recommendation (HM, CL, IK, MRL), pp. 265–274.
SIGIR-2011-RendleGFS #performance: Fast context-aware recommendations with factorization machines (SR, ZG, CF, LST), pp. 635–644.
SIGIR-2011-RicciGBAGP #named #quality #web: GreenMeter: a tool for assessing the quality and recommending tags for web 2.0 applications (SMRR, DAG, FMB, JMA, MAG, ROP), pp. 1279–1280.
SIGIR-2011-ShiYGN #machine learning #network #scalability #social: A large scale machine learning system for recommending heterogeneous content in social networks (YS, DY, AG, SN), pp. 1337–1338.
SIGIR-2011-TangWB: Recommending interesting activity-related local entities (JT, RWW, PB), pp. 1161–1162.
SIGIR-2011-VargasCV: Intent-oriented diversity in recommender systems (SV, PC, DV), pp. 1211–1212.
SIGIR-2011-Wang #comprehension #information management #using: Understanding and using contextual information in recommender systems (LW), pp. 1329–1330.
SIGIR-2011-WangZ #e-commerce: Utilizing marginal net utility for recommendation in e-commerce (JW, YZ), pp. 1003–1012.
SIGIR-2011-WeiHL #analysis #framework #semantics: A unified framework for recommendations based on quaternary semantic analysis (WC, WH, MLL), pp. 1023–1032.
SIGIR-2011-YangLSZZ #collaboration #learning #using: Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
SIGIR-2011-YeYLL #collaboration: Exploiting geographical influence for collaborative point-of-interest recommendation (MY, PY, WCL, DLL), pp. 325–334.
SIGIR-2011-ZhouYZ #functional #matrix: Functional matrix factorizations for cold-start recommendation (KZ, SHY, HZ), pp. 315–324.
ECOOP-2011-Duala-EkokoR #api #using: Using Structure-Based Recommendations to Facilitate Discoverability in APIs (EDE, MPR), pp. 79–104.
ASE-2011-LozanoKM #named #search-based #source code: Mendel: Source code recommendation based on a genetic metaphor (AL, AK, KM), pp. 384–387.
ASE-2011-WangFWLXY #api #effectiveness #java #named #web: APIExample: An effective web search based usage example recommendation system for java APIs (LW, LF, LW, GL, BX, FY), pp. 592–595.
ESEC-FSE-2011-ZhengZL #api #using #web: Cross-library API recommendation using web search engines (WZ, QZ, MRL), pp. 480–483.
ICSE-2011-DumitruGHCMCM #mining #on-demand: On-demand feature recommendations derived from mining public product descriptions (HD, MG, NH, JCH, BM, CCH, MM), pp. 181–190.
ICSE-2011-NguyenNNN #evolution: Aspect recommendation for evolving software (TTN, HVN, HAN, TNN), pp. 361–370.
PDP-2011-GrossBS #distributed #framework #named #platform: GroupRecoPF: Innovative Group Recommendations in a Distributed Platform (TG, CB, MS), pp. 293–300.
HT-2010-GasslerZTS #named #using: SnoopyDB: narrowing the gap between structured and unstructured information using recommendations (WG, EZ, MT, GS), pp. 271–272.
HT-2010-GuoJ #collaboration #personalisation #topic: Topic-based personalized recommendation for collaborative tagging system (YG, JBDJ), pp. 61–66.
HT-2010-LiangXLNT #personalisation: Connecting users and items with weighted tags for personalized item recommendations (HL, YX, YL, RN, XT), pp. 51–60.
HT-2010-LiuFZ #social: Speak the same language with your friends: augmenting tag recommenders with social relations (KL, BF, WZ), pp. 45–50.
JCDL-2010-SugiyamaK #research: Scholarly paper recommendation via user’s recent research interests (KS, MYK), pp. 29–38.
SIGMOD-2010-MoriczDB #named: PYMK: friend recommendation at myspace (MM, YD, MB), pp. 999–1002.
SIGMOD-2010-ParameswaranKBG #algorithm #mining #named #precedence: Recsplorer: recommendation algorithms based on precedence mining (AGP, GK, BB, HGM), pp. 87–98.
VLDB-2010-AkbarnejadCEKMOPV #sql: SQL QueRIE Recommendations (JA, GC, ME, SK, SM, DO, NP, JSVV), pp. 1597–1600.
CSEET-2010-GarousiM #education #testing: Current State of the Software Testing Education in North American Academia and Some Recommendations for the New Educators (VG, AM), pp. 89–96.
EDM-2010-RankaAC #named: Pundit: Intelligent Recommender of Courses (AR, FA, HSC), pp. 339–340.
SIGITE-2010-YuanZ #design #education #network #taxonomy: Design and implement a networking curriculum in light of ACM IT curriculum recommendations and bloom’s taxonomy (DY, JZ), pp. 155–156.
CHI-2010-ChenNNBC #twitter: Short and tweet: experiments on recommending content from information streams (JC, RN, LN, MSB, EHC), pp. 1185–1194.
CHI-2010-WalshG #game studies #named: Curator: a game with a purpose for collection recommendation (GW, JG), pp. 2079–2082.
CIKM-2010-BelemMAGP #metric #quality #web: Exploiting co-occurrence and information quality metrics to recommend tags in web 2.0 applications (FMB, EFM, JMdA, MAG, GLP), pp. 1793–1796.
CIKM-2010-CaiLLTL: Recommendation based on object typicality (YC, HfL, QL, JT, JL), pp. 1529–1532.
CIKM-2010-Garcia-AlvaradoCO #query: OLAP-based query recommendation (CGA, ZC, CO), pp. 1353–1356.
CIKM-2010-GemmellSMB #hybrid #social: Hybrid tag recommendation for social annotation systems (JG, TS, BM, RDB), pp. 829–838.
CIKM-2010-GolbandiKL #on the: On bootstrapping recommender systems (NG, YK, RL), pp. 1805–1808.
CIKM-2010-GuoCXS #approach #query #social: A structured approach to query recommendation with social annotation data (JG, XC, GX, HS), pp. 619–628.
CIKM-2010-KurashimaIIF #using: Travel route recommendation using geotags in photo sharing sites (TK, TI, GI, KF), pp. 579–588.
CIKM-2010-LiangXLN #folksonomy #personalisation #taxonomy: Personalized recommender system based on item taxonomy and folksonomy (HL, YX, YL, RN), pp. 1641–1644.
CIKM-2010-MinkovCLTJ #collaboration: Collaborative future event recommendation (EM, BC, JL, SJT, TSJ), pp. 819–828.
CIKM-2010-NakatsujiFTUFI #music #novel: Classical music for rock fans?: novel recommendations for expanding user interests (MN, YF, AT, TU, KF, TI), pp. 949–958.
CIKM-2010-ParameswaranGU: Evaluating, combining and generalizing recommendations with prerequisites (AGP, HGM, JDU), pp. 919–928.
CIKM-2010-PengZZW #collaboration #social: Collaborative filtering in social tagging systems based on joint item-tag recommendations (JP, DDZ, HZ, FYW), pp. 809–818.
CIKM-2010-ShavittWW #peer-to-peer #using: Building recommendation systems using peer-to-peer shared content (YS, EW, UW), pp. 1457–1460.
CIKM-2010-WartenaSW #keyword: Selecting keywords for content based recommendation (CW, WS, MW), pp. 1533–1536.
CIKM-2010-ZhangZ #modelling #personalisation: Discriminative factored prior models for personalized content-based recommendation (LZ, YZ), pp. 1569–1572.
CIKM-2010-ZhaoBCGWZ #concurrent #learning #online #thread: Learning a user-thread alignment manifold for thread recommendation in online forum (JZ, JB, CC, ZG, CW, CZ), pp. 559–568.
ECIR-2010-RedpathGMC #collaboration: Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings (JR, DHG, SIM, LC), pp. 394–406.
KDD-2010-AgarwalCE #learning #online #performance: Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDD-2010-GeXTXGP #energy #mobile: An energy-efficient mobile recommender system (YG, HX, AT, KX, MG, MJP), pp. 899–908.
KDD-2010-JahrerTL #predict: Combining predictions for accurate recommender systems (MJ, AT, RAL), pp. 693–702.
KDD-2010-Steck #random #testing: Training and testing of recommender systems on data missing not at random (HS), pp. 713–722.
KDD-2010-XiangYZCZYS #graph: Temporal recommendation on graphs via long- and short-term preference fusion (LX, QY, SZ, LC, XZ, QY, JS), pp. 723–732.
KDIR-2010-ArmanoV: A Unifying View of Contextual Advertising and Recommender Systems (GA, EV), pp. 463–466.
KDIR-2010-Quinteiro-GonzalezMHLMP #delivery #framework #platform: Recommendation System in an Audiovisual Delivery Platform (JMQG, EMJ, PHM, ALR, LMM, ASdP), pp. 379–382.
RecSys-2010-AdomaviciusZ #algorithm #on the: On the stability of recommendation algorithms (GA, JZ), pp. 47–54.
RecSys-2010-AlbanesedMPP #modelling #problem #social: Modeling recommendation as a social choice problem (MA, Ad, VM, FP, AP), pp. 329–332.
RecSys-2010-AydayF #online: A belief propagation based recommender system for online services (EA, FF), pp. 217–220.
RecSys-2010-Baeza-Yates #predict #query: Query intent prediction and recommendation (RABY), pp. 5–6.
RecSys-2010-BaglioniBBCFVP #lightweight #mobile #privacy: A lightweight privacy preserving SMS-based recommendation system for mobile users (EB, LB, LB, UMC, LF, AV, GP), pp. 191–198.
RecSys-2010-Balakrishnan #on-demand: On-demand set-based recommendations (SB), pp. 313–316.
RecSys-2010-BaltrunasMR #collaboration #rank: Group recommendations with rank aggregation and collaborative filtering (LB, TM, FR), pp. 119–126.
RecSys-2010-BarrioR #collaboration: Geolocated movie recommendations based on expert collaborative filtering (JBB, XAR), pp. 347–348.
RecSys-2010-BenchettaraKR #approach #collaboration #machine learning #predict: A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
RecSys-2010-BerkovskyF #analysis: Group-based recipe recommendations: analysis of data aggregation strategies (SB, JF), pp. 111–118.
RecSys-2010-BerkovskyFCB #algorithm #game studies #process: Recommender algorithms in activity motivating games (SB, JF, MC, DB), pp. 175–182.
RecSys-2010-BollenKWG #comprehension: Understanding choice overload in recommender systems (DGFMB, BPK, MCW, MPG), pp. 63–70.
RecSys-2010-Burke #algorithm: Evaluating the dynamic properties of recommendation algorithms (RDB), pp. 225–228.
RecSys-2010-CantadorBV #social: Content-based recommendation in social tagging systems (IC, AB, DV), pp. 237–240.
RecSys-2010-CastagnosJP: Eye-tracking product recommenders’ usage (SC, NJ, PP), pp. 29–36.
RecSys-2010-CebrianPVA #music: Music recommendations with temporal context awareness (TC, MP, PV, XA), pp. 349–352.
RecSys-2010-CremonesiKT #algorithm #performance: Performance of recommender algorithms on top-n recommendation tasks (PC, YK, RT), pp. 39–46.
RecSys-2010-DalyGM #network #social: The network effects of recommending social connections (EMD, WG, DRM), pp. 301–304.
RecSys-2010-DavidsonLLNVGGHLLS #video: The YouTube video recommendation system (JD, BL, JL, PN, TVV, UG, SG, YH, ML, BL, DS), pp. 293–296.
RecSys-2010-DeryKRS #nondeterminism: Iterative voting under uncertainty for group recommender systems (LND, MK, LR, BS), pp. 265–268.
RecSys-2010-EsparzaOS #on the #realtime #web: On the real-time web as a source of recommendation knowledge (SGE, MPO, BS), pp. 305–308.
RecSys-2010-FreyneBDG #network #social: Social networking feeds: recommending items of interest (JF, SB, EMD, WG), pp. 277–280.
RecSys-2010-GedikliJ #rating: Recommending based on rating frequencies (FG, DJ), pp. 233–236.
RecSys-2010-GeDJ: Beyond accuracy: evaluating recommender systems by coverage and serendipity (MG, CDB, DJ), pp. 257–260.
RecSys-2010-GuyJAMNNS #industrial #perspective: Will recommenders kill search?: recommender systems — an industry perspective (IG, AJ, PA, PM, PN, CN, HS), pp. 7–12.
RecSys-2010-HammerKA #named: MED-StyleR: METABO diabetes-lifestyle recommender (SH, JK, EA), pp. 285–288.
RecSys-2010-HannonBS #collaboration #twitter #using: Recommending twitter users to follow using content and collaborative filtering approaches (JH, MB, BS), pp. 199–206.
RecSys-2010-Hu #design: Design and user issues in personality-based recommender systems (RH), pp. 357–360.
RecSys-2010-ImK #personalisation: Personalizing the settings for Cf-based recommender systems (II, BHK), pp. 245–248.
RecSys-2010-JamaliE #matrix #network #social #trust: A matrix factorization technique with trust propagation for recommendation in social networks (MJ, ME), pp. 135–142.
RecSys-2010-JawaheerSK #feedback #music #online: Characterisation of explicit feedback in an online music recommendation service (GJ, MS, PK), pp. 317–320.
RecSys-2010-KaragiannidisAZV #framework #named: Hydra: an open framework for virtual-fusion of recommendation filters (SK, SA, CZ, AV), pp. 229–232.
RecSys-2010-KaratzoglouABO #collaboration #multi: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (AK, XA, LB, NO), pp. 79–86.
RecSys-2010-LappasG #interactive #network #social: Interactive recommendations in social endorsement networks (TL, DG), pp. 127–134.
RecSys-2010-LeeB #collaboration #process #self #using: Using self-defined group activities for improvingrecommendations in collaborative tagging systems (DHL, PB), pp. 221–224.
RecSys-2010-LipczakM #learning #performance: Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSys-2010-MarxHM #algorithm #comprehension #hybrid: Increasing consumers’ understanding of recommender results: a preference-based hybrid algorithm with strong explanatory power (PM, THT, AM), pp. 297–300.
RecSys-2010-MoldvayBFS #clustering #graph #named #semantics #social: Tagmantic: a social recommender service based on semantic tag graphs and tag clusters (JM, IB, AF, MS), pp. 345–346.
RecSys-2010-Musto #modelling: Enhanced vector space models for content-based recommender systems (CM), pp. 361–364.
RecSys-2010-PizzatoRCKK #named #online: RECON: a reciprocal recommender for online dating (LASP, TR, TC, IK, JK), pp. 207–214.
RecSys-2010-PizzatoRCKYK #online: Reciprocal recommender system for online dating (LASP, TR, TC, IK, KY, JK), pp. 353–354.
RecSys-2010-Said #hybrid #identification: Identifying and utilizing contextual data in hybrid recommender systems (AS), pp. 365–368.
RecSys-2010-SandholmUAH: Global budgets for local recommendations (TS, HU, CA, BAH), pp. 13–20.
RecSys-2010-Schirru #enterprise #platform #social #social media #topic: Topic-based recommendations in enterprise social media sharing platforms (RS), pp. 369–372.
RecSys-2010-Servan-Schreiber: Recommendation analytics: the business view, and the business case (ESS), pp. 215–216.
RecSys-2010-Shani #tutorial: Tutorial on evaluating recommender systems (GS), p. 1.
RecSys-2010-VasukiNLD #network #using: Affiliation recommendation using auxiliary networks (VV, NN, ZL, ISD), pp. 103–110.
RecSys-2010-XieLW: Breaking out of the box of recommendations: from items to packages (MX, LVSL, PTW), pp. 151–158.
RecSys-2010-ZangerleGS #collaboration #information management: Recommending structure in collaborative semistructured information systems (EZ, WG, GS), pp. 261–264.
RecSys-2010-ZhaoZYZZF #social #what: Who is talking about what: social map-based recommendation for content-centric social websites (SZ, MXZ, QY, XZ, WZ, RF), pp. 143–150.
RecSys-2010-ZhengWZLY #case study #empirical #user study: Do clicks measure recommendation relevancy?: an empirical user study (HZ, DW, QZ, HL, TY), pp. 249–252.
SIGIR-2010-GuyZRCU #people #social #social media: Social media recommendation based on people and tags (IG, NZ, IR, DC, EU), pp. 194–201.
SIGIR-2010-HuangAH #classification #tool support: Medical search and classification tools for recommendation (XH, AA, QH), p. 707.
SIGIR-2010-Kawamae: Serendipitous recommendations via innovators (NK), pp. 218–225.
SIGIR-2010-LathiaHCA: Temporal diversity in recommender systems (NL, SH, LC, XA), pp. 210–217.
SIGIR-2010-WangLC10a #social #social media: User comments for news recommendation in social media (JW, QL, YPC), pp. 881–882.
SIGIR-2010-ZhengLLZ #composition: Flickr group recommendation based on tensor decomposition (NZ, QL, SL, LZ), pp. 737–738.
ASE-2010-ZhengZLX #generative #random #sequence #testing: Random unit-test generation with MUT-aware sequence recommendation (WZ, QZ, MRL, TX), pp. 293–296.
ICSE-2010-HolmesW: Customized awareness: recommending relevant external change events (RH, RJW), pp. 465–474.
ICSE-2010-Schroter: Failure preventing recommendations (AS), pp. 397–400.
SAC-2010-BallatoreMKB #adaptation #interactive #named: RecoMap: an interactive and adaptive map-based recommender (AB, GM, CK, MB), pp. 887–891.
SAC-2010-DuraoD #hybrid #personalisation: Extending a hybrid tag-based recommender system with personalization (FAD, PD), pp. 1723–1727.
SAC-2010-KimY #multi #personalisation: New theoretical findings in multiple personalized recommendations (YHK, YY), pp. 94–98.
SAC-2010-ShinKNNTO #named #ontology #using: ONTOMO: web-based ontology building system: ---instance recommendation using bootstrapping--- (IS, TK, HN, KN, YT, AO), pp. 1442–1443.
SAC-2010-SuYT #music #novel: A novel music recommender by discovering preferable perceptual-patterns from music pieces (JHS, HHY, VST), pp. 1924–1928.
ICST-2010-EngstromRW #empirical #evaluation #testing: An Empirical Evaluation of Regression Testing Based on Fix-Cache Recommendations (EE, PR, GW), pp. 75–78.
HT-2009-MahmoodR #adaptation: Improving recommender systems with adaptive conversational strategies (TM, FR), pp. 73–82.
HT-2009-SiersdorferS #folksonomy #social #web: Social recommender systems for web 2.0 folksonomies (SS, SS), pp. 261–270.
JCDL-2009-LiC #approach #graph #kernel #machine learning #predict: Recommendation as link prediction: a graph kernel-based machine learning approach (XL, HC), pp. 213–216.
JCDL-2009-MarshallRZ #assessment #education #library #self #standard: Dimensional standard alignment in K-12 digital libraries: assessment of self-found vs. recommended curriculum (BM, RFR, MZ), pp. 11–14.
JCDL-2009-YangWWZZ #named: CARES: a ranking-oriented CADAL recommender system (CY, BW, JW, YZ, LZ), pp. 203–212.
SIGMOD-2009-KoutrikaBG #flexibility #named: FlexRecs: expressing and combining flexible recommendations (GK, BB, HGM), pp. 745–758.
VLDB-2009-Amer-YahiaRCDY #performance #semantics: Group Recommendation: Semantics and Efficiency (SAY, SBR, AC, GD, CY), pp. 754–765.
VLDB-2009-El-HelwIZ #named #statistics: StatAdvisor: Recommending Statistical Views (AEH, IFI, CZ), pp. 1306–1317.
EDM-2009-NagataTSKM: Edu-mining for Book Recommendation for Pupils (RN, KT, KS, JK, KM), pp. 91–100.
EDM-2009-SacinASO #data mining #education #mining #using: Recommendation in Higher Education Using Data Mining Techniques (CVS, JBA, LS, AO), pp. 191–199.
ICSM-2009-MaSZS: Expert recommendation with usage expertise (DM, DS, TZ, JS), pp. 535–538.
MSR-2009-RastkarM #interactive #on the #question #what: On what basis to recommend: Changesets or interactions? (SR, GCM), pp. 155–158.
CHI-2009-ChenGDMG #network #people #social: Make new friends, but keep the old: recommending people on social networking sites (JC, WG, CD, MJM, IG), pp. 201–210.
CHI-2009-HansenG: Mixing it up: recommending collections of items (DLH, JG), pp. 1217–1226.
CHI-2009-RaoHNJ: My Dating Site Thinks I’m a Loser: effects of personal photos and presentation intervals on perceptions of recommender systems (SR, TH, CN, NJ), pp. 221–224.
CHI-2009-ReichlingW #automation #generative: Expert recommender systems in practice: evaluating semi-automatic profile generation (TR, VW), pp. 59–68.
OCSC-2009-SongNKE #using #word: A Proposed Movie Recommendation Method Using Emotional Word Selection (MS, HN, HGK, JE), pp. 525–534.
ICEIS-DISI-2009-Pitkaranta #information retrieval: Applying Information Retrieval for Market Basket Recommender Systems (TP), pp. 138–143.
ICEIS-J-2009-JerbiRTZ: Applying Recommendation Technology in OLAP Systems (HJ, FR, OT, GZ), pp. 220–233.
ICEIS-SAIC-2009-CiuffoI #case study #collaboration #information management #using: Using Grids to Support Information Filtering Systems — A Case Study of Running Collaborative Filtering Recommendations on gLite (LNC, EI), pp. 12–18.
ICEIS-SAIC-2009-DrumondGMA #implementation #multi: Implementation Issues of the Infonorma Multi-agent Recommender System (LD, RG, DM, GA), pp. 128–133.
CIKM-2009-AnastasakosHKR #approach #collaboration #graph #using: A collaborative filtering approach to ad recommendation using the query-ad click graph (TA, DH, SK, HR), pp. 1927–1930.
CIKM-2009-CaoCYX: Enhancing recommender systems under volatile userinterest drifts (HC, EC, JY, HX), pp. 1257–1266.
CIKM-2009-ShiWY #framework #named #semantics: Msuggest: a semantic recommender framework for traditional chinese medicine book search engine (SS, BW, YY), pp. 533–542.
CIKM-2009-SunCSSWL #learning: Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKM-2009-XinKDL #framework #multi #random #social: A social recommendation framework based on multi-scale continuous conditional random fields (XX, IK, HD, MRL), pp. 1247–1256.
ECIR-2009-MoshfeghiAPJ #collaboration #predict #rating #semantics: Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering (YM, DA, BP, JMJ), pp. 54–65.
ECIR-2009-PapadopoulosMKB #graph: Lexical Graphs for Improved Contextual Ad Recommendation (SP, FM, YK, BB), pp. 216–227.
KDD-2009-JamaliE #named #random #trust: TrustWalker: a random walk model for combining trust-based and item-based recommendation (MJ, ME), pp. 397–406.
KDD-2009-LiDJEL #random: Grocery shopping recommendations based on basket-sensitive random walk (ML, MBD, IHJ, WED, PJGL), pp. 1215–1224.
KDD-2009-McSherryM #privacy: Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders (FM, IM), pp. 627–636.
KDD-2009-OnumaTF #algorithm #named #novel: TANGENT: a novel, “Surprise me”, recommendation algorithm (KO, HT, CF), pp. 657–666.
KDD-2009-RendleMNS #learning #ranking: Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KEOD-2009-SantosS: Interpretation and Recommendation Tasks Supported by Ceres System (CPS, DRdS), pp. 464–467.
RecSys-2009-AbbassiALVY: Getting recommender systems to think outside the box (ZA, SAY, LVSL, SV, CY), pp. 285–288.
RecSys-2009-AmatriainPTO: Rate it again: increasing recommendation accuracy by user re-rating (XA, JMP, NT, NO), pp. 173–180.
RecSys-2009-AntonelliFGL #named: DynamicTV: a culture-aware recommender (FA, GF, MG, SL), pp. 257–260.
RecSys-2009-BaoBT: Stacking recommendation engines with additional meta-features (XB, LB, RT), pp. 109–116.
RecSys-2009-BaragliaCCFFPS #approach #query: Search shortcuts: a new approach to the recommendation of queries (RB, FC, VC, DF, VF, RP, FS), pp. 77–84.
RecSys-2009-BaskinK: Preference aggregation in group recommender systems for committee decision-making (JPB, SK), pp. 337–340.
RecSys-2009-BhattacharjeeGK #architecture #social: An incentive-based architecture for social recommendations (RB, AG, KK), pp. 229–232.
RecSys-2009-BroccoG #network: Team recommendation in open innovation networks (MB, GG), pp. 365–368.
RecSys-2009-CastagnosJP #process: Recommenders’ influence on buyers’ decision process (SC, NJ, PP), pp. 361–364.
RecSys-2009-Castro-HerreraCM #evolution #online: A recommender system for dynamically evolving online forums (CCH, JCH, BM), pp. 213–216.
RecSys-2009-Chen #adaptation #trade-off: Adaptive tradeoff explanations in conversational recommenders (LC), pp. 225–228.
RecSys-2009-ChengH #effectiveness #modelling #obfuscation: Effective diverse and obfuscated attacks on model-based recommender systems (ZC, NH), pp. 141–148.
RecSys-2009-ConryKR #problem: Recommender systems for the conference paper assignment problem (DC, YK, NR), pp. 357–360.
RecSys-2009-CremonesiT #analysis: Analysis of cold-start recommendations in IPTV systems (PC, RT), pp. 233–236.
RecSys-2009-FreyneJGG: Increasing engagement through early recommender intervention (JF, MJ, IG, WG), pp. 85–92.
RecSys-2009-GansnerHKV #clustering #visualisation: Putting recommendations on the map: visualizing clusters and relations (ERG, YH, SGK, CV), pp. 345–348.
RecSys-2009-GemmellRSCM #ambiguity #folksonomy: The impact of ambiguity and redundancy on tag recommendation in folksonomies (JG, MR, TS, LC, BM), pp. 45–52.
RecSys-2009-GivonL #predict: Predicting social-tags for cold start book recommendations (SG, VL), pp. 333–336.
RecSys-2009-Golbeck #social #trust #tutorial #using: Tutorial on using social trust for recommender systems (JG), pp. 425–426.
RecSys-2009-GreenLAMKHBM #generative: Generating transparent, steerable recommendations from textual descriptions of items (SJG, PL, JA, FM, SK, JH, JB, XWM), pp. 281–284.
RecSys-2009-GunawardanaM #approach #hybrid: A unified approach to building hybrid recommender systems (AG, CM), pp. 117–124.
RecSys-2009-GuyZCRUYO #personalisation #social: Personalized recommendation of social software items based on social relations (IG, NZ, DC, IR, EU, SY, SOK), pp. 53–60.
RecSys-2009-HelouSSG #process #ranking: The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities (SEH, CS, SS, DG), pp. 373–376.
RecSys-2009-HuP: Acceptance issues of personality-based recommender systems (RH, PP), pp. 221–224.
RecSys-2009-JamaliE #network #trust #using: Using a trust network to improve top-N recommendation (MJ, ME), pp. 181–188.
RecSys-2009-JannachH #case study #effectiveness #internet #mobile: A case study on the effectiveness of recommendations in the mobile internet (DJ, KH), pp. 205–208.
RecSys-2009-JaschkeEHS #testing: Testing and evaluating tag recommenders in a live system (RJ, FE, AH, GS), pp. 369–372.
RecSys-2009-KawamaeSY #personalisation: Personalized recommendation based on the personal innovator degree (NK, HS, TY), pp. 329–332.
RecSys-2009-KhezrzadehTW #power of: Harnessing the power of “favorites” lists for recommendation systems (MK, AT, WWW), pp. 289–292.
RecSys-2009-KnijnenburgW #adaptation #comprehension #elicitation #user satisfaction: Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system (BPK, MCW), pp. 381–384.
RecSys-2009-KrestelFN: Latent dirichlet allocation for tag recommendation (RK, PF, WN), pp. 61–68.
RecSys-2009-LousameS: View-based recommender systems (FPL, ES), pp. 389–392.
RecSys-2009-MaLK #learning #trust: Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSys-2009-MoghaddamJEH #feedback #named #trust #using: FeedbackTrust: using feedback effects in trust-based recommendation systems (SM, MJ, ME, JH), pp. 269–272.
RecSys-2009-NathansonBG: Donation dashboard: a recommender system for donation portfolios (TN, EB, KYG), pp. 253–256.
RecSys-2009-Nnadi #clustering #correlation #multi #set: Applying relevant set correlation clustering to multi-criteria recommender systems (NN), pp. 401–404.
RecSys-2009-OMahonyS #learning: Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
RecSys-2009-PannielloTGPP #comparison: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems (UP, AT, MG, CP, AP), pp. 265–268.
RecSys-2009-ParameswaranG: Recommendations with prerequisites (AGP, HGM), pp. 353–356.
RecSys-2009-ParkC: Pairwise preference regression for cold-start recommendation (STP, WC), pp. 21–28.
RecSys-2009-PhelanMS #realtime #topic #twitter #using: Using twitter to recommend real-time topical news (OP, KM, BS), pp. 385–388.
RecSys-2009-PilaszyT #metadata: Recommending new movies: even a few ratings are more valuable than metadata (IP, DT), pp. 93–100.
RecSys-2009-PudhiyaveetilGLE #concept: Conceptual recommender system for CiteSeerX (AKP, SG, HPL, JE), pp. 241–244.
RecSys-2009-PuZC: Critiquing recommenders for public taste products (PP, MZ, SC), pp. 249–252.
RecSys-2009-QasimOWHO #partial order: A partial-order based active cache for recommender systems (UQ, VO, YfBW, MEH, MTÖ), pp. 209–212.
RecSys-2009-QuerciaC #mobile #named #using: FriendSensing: recommending friends using mobile phones (DQ, LC), pp. 273–276.
RecSys-2009-Recio-GarciaJSD: Personality aware recommendations to groups (JARG, GJD, AASRG, BDA), pp. 325–328.
RecSys-2009-Schubert #knowledge-based #personalisation #query: Personalized query relaxations and repairs in knowledge-based recommendation (MS), pp. 409–412.
RecSys-2009-SemeraroLBG: Knowledge infusion into content-based recommender systems (GS, PL, PB, MdG), pp. 301–304.
RecSys-2009-SeyerlehnerFW #on the: On the limitations of browsing top-N recommender systems (KS, AF, GW), pp. 321–324.
RecSys-2009-SymeonidisNM #named: MoviExplain: a recommender system with explanations (PS, AN, YM), pp. 317–320.
RecSys-2009-Tolomei #mining #process #web: Search the web x.0: mining and recommending web-mediated processes (GT), pp. 417–420.
RecSys-2009-TsatsouMKD #analysis #framework #personalisation #semantics: A semantic framework for personalized ad recommendation based on advanced textual analysis (DT, FM, IK, PCD), pp. 217–220.
RecSys-2009-TylerZCZ #categorisation: Ordering innovators and laggards for product categorization and recommendation (SKT, SZ, YC, YZ), pp. 29–36.
RecSys-2009-UmyarovT #estimation #modelling #rating #using: Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models (AU, AT), pp. 37–44.
RecSys-2009-ViappianiB #set: Regret-based optimal recommendation sets in conversational recommender systems (PV, CB), pp. 101–108.
RecSys-2009-WedelRC #personalisation: Up close and personalized: a marketing view of recommendation systems (MW, RTR, TSC), pp. 3–4.
RecSys-2009-WeimerKB #matrix: Maximum margin matrix factorization for code recommendation (MW, AK, MB), pp. 309–312.
RecSys-2009-Zhang: Enhancing diversity in Top-N recommendation (MZ), pp. 397–400.
RecSys-2009-ZhouR #assessment: Assessment of conversation co-mentions as a resource for software module recommendation (DXZ, PR), pp. 133–140.
SIGIR-2009-GuanBMCW #graph #multi #personalisation #ranking #using: Personalized tag recommendation using graph-based ranking on multi-type interrelated objects (ZG, JB, QM, CC, CW), pp. 540–547.
SIGIR-2009-KonstasSJ #collaboration #network #on the #social: On social networks and collaborative recommendation (IK, VS, JMJ), pp. 195–202.
SIGIR-2009-MaKL #learning #social #trust: Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
ECOOP-2009-ZhongXZPM #api #mining #named: MAPO: Mining and Recommending API Usage Patterns (HZ, TX, LZ, JP, HM), pp. 318–343.
RE-2009-Castro-HerreraCM #elicitation #online #requirements: Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation (CCH, JCH, BM), pp. 37–46.
ASE-2009-HolmesRRW #automation #reuse: Automatically Recommending Triage Decisions for Pragmatic Reuse Tasks (RH, TR, MPR, RJW), pp. 397–408.
ESEC-FSE-2009-AshokJLRSV #debugging #named: DebugAdvisor: a recommender system for debugging (BA, JMJ, HL, SKR, GS, VV), pp. 373–382.
ICSE-2009-DagenaisR #analysis #api #evolution #named: SemDiff: Analysis and recommendation support for API evolution (BD, MPR), pp. 599–602.
SAC-2009-Castro-HerreraDCM #elicitation #requirements #scalability: A recommender system for requirements elicitation in large-scale software projects (CCH, CD, JCH, BM), pp. 1419–1426.
SAC-2009-dAciernoMP #library: A recommendation system for browsing digital libraries (Ad, VM, AP), pp. 1771–1778.
SAC-2009-PengC #collaboration #multi #named #trust: iTrustU: a blog recommender system based on multi-faceted trust and collaborative filtering (TCP, ScTC), pp. 1278–1285.
HT-2008-PanissonRS #framework #implementation #named #social: X-hinter: a framework for implementing social oriented recommender systems (AP, GR, RS), pp. 235–236.
SIGMOD-2008-Amer-YahiaGSY #social: From del.icio.us to x.qui.site: recommendations in social tagging sites (SAY, AG, JS, CY), pp. 1323–1326.
SIGMOD-2008-Konstan: Introduction to recommender systems (JAK), pp. 1373–1374.
VLDB-2008-ShaoCTYA #enterprise #named #problem: EasyTicket: a ticket routing recommendation engine for enterprise problem resolution (QS, YC, ST, XY, NA), pp. 1436–1439.
CSEET-2008-ShoemakerDIM #assurance: Integrating Secure Software Assurance Content with SE2004 Recommendations (DS, AD, JAI, NRM), pp. 59–66.
CHI-2008-BellottiBCDFIKNPPRRSW #mobile: Activity-based serendipitous recommendations with the Magitti mobile leisure guide (VB, JBB, EHhC, ND, JF, EI, THK, MWN, KP, BP, PR, MR, DJS, AW), pp. 1157–1166.
CHI-2008-ODonovanSGBH #interactive #named #visual notation: PeerChooser: visual interactive recommendation (JO, BS, BG, SB, TH), pp. 1085–1088.
ICEIS-AIDSS-2008-RokachMS #elicitation: Anytime AHP Method for Preferences Elicitation in Stereotype-Based Recommender System (LR, AM, AS), pp. 268–275.
ICEIS-HCI-2008-LucaB #design #implementation #navigation: Microformats Based Navigation Assistant — A Non-intrusive Recommender Agent: Design and Implementation (APL, SCB), pp. 54–61.
ICEIS-J-2008-LucaB08a #experience #user interface #web: Enhancing User Experience on the Web via Microformats-Based Recommendations (APL, SCB), pp. 321–333.
ICEIS-J-2008-WengXLN08a #estimation #performance: An Efficient Neighbourhood Estimation Technique for Making Recommendations (LTW, YX, YL, RN), pp. 253–264.
ICEIS-J-2008-WengXLN08b #quality #taxonomy: Improve Recommendation Quality with Item Taxonomic Information (LTW, YX, YL, RN), pp. 265–279.
ICEIS-SAIC-2008-Uchyigit08a: A Software Agent for Content Based Recommendations for the WWW (GU), pp. 178–183.
ICEIS-SAIC-2008-WengXLLN #taxonomy #web: Web Information Recommendation Making Based on Item Taxonomy (LTW, YX, YL, RN), pp. 20–28.
ICEIS-SAIC-2008-WengXLN #estimation #performance: Efficient Neighbourhood Estimation for Recommendation Making (LTW, YX, YL, RN), pp. 12–19.
CIKM-2008-ChiZGZ #personalisation #probability: Probabilistic polyadic factorization and its application to personalized recommendation (YC, SZ, YG, YZ), pp. 941–950.
CIKM-2008-GuoXBY #community: Tapping on the potential of q&a community by recommending answer providers (JG, SX, SB, YY), pp. 921–930.
CIKM-2008-MaYLK08a #matrix #named #probability #social #using: SoRec: social recommendation using probabilistic matrix factorization (HM, HY, MRL, IK), pp. 931–940.
CIKM-2008-VatturiGDMB #personalisation: Tag-based filtering for personalized bookmark recommendations (PKV, WG, CD, MJM, BB), pp. 1395–1396.
CIKM-2008-YatesJPCS #named #smarttech #specification: SHOPSMART: product recommendations through technical specifications and user reviews (AY, JJ, AMP, ADC, NS), pp. 1501–1502.
ECIR-2008-ValletHJ #evaluation #graph #using: Use of Implicit Graph for Recommending Relevant Videos: A Simulated Evaluation (DV, FH, JMJ), pp. 199–210.
KDD-2008-ChenZC #collaboration #community #personalisation: Combinational collaborative filtering for personalized community recommendation (WC, DZ, EYC), pp. 115–123.
KDD-2008-NguyenPS: A software system for buzz-based recommendations (HN, NP, NS), pp. 1093–1096.
RecSys-2008-AdomaviciusT: Context-aware recommender systems (GA, AT), pp. 335–336.
RecSys-2008-Baltrunas #information management: Exploiting contextual information in recommender systems (LB), pp. 295–298.
RecSys-2008-BogersB #using: Recommending scientific articles using citeulike (TB, AvdB), pp. 287–290.
RecSys-2008-Broder: Computational advertising and recommender systems (AZB), pp. 1–2.
RecSys-2008-BrodskyHW #framework #named: CARD: a decision-guidance framework and application for recommending composite alternatives (AB, SMH, JW), pp. 171–178.
RecSys-2008-BryanOC #collaboration #retrieval: Unsupervised retrieval of attack profiles in collaborative recommender systems (KB, MPO, PC), pp. 155–162.
RecSys-2008-Burke #robust: Robust recommender systems (RDB), pp. 331–332.
RecSys-2008-CelmaH #approach #novel: A new approach to evaluating novel recommendations (ÒC, PH), pp. 179–186.
RecSys-2008-ChenP #evaluation #interface: A cross-cultural user evaluation of product recommender interfaces (LC, PP), pp. 75–82.
RecSys-2008-DegemmisLSB #semantics: Integrating tags in a semantic content-based recommender (MD, PL, GS, PB), pp. 163–170.
RecSys-2008-DiasLLEL #case study #personalisation: The value of personalised recommender systems to e-business: a case study (MBD, DL, ML, WED, PJGL), pp. 291–294.
RecSys-2008-GargW #interactive #personalisation: Personalized, interactive tag recommendation for flickr (NG, IW), pp. 67–74.
RecSys-2008-GeyerDMMF #online #self #topic: Recommending topics for self-descriptions in online user profiles (WG, CD, DRM, MJM, JF), pp. 59–66.
RecSys-2008-GunawardanaM: Tied boltzmann machines for cold start recommendations (AG, CM), pp. 19–26.
RecSys-2008-KoutrikaIBG #flexibility: Flexible recommendations over rich data (GK, RI, BB, HGM), pp. 203–210.
RecSys-2008-KrishnanNNDK #online #predict: Who predicts better?: results from an online study comparing humans and an online recommender system (VK, PKN, MN, RTD, JAK), pp. 211–218.
RecSys-2008-Kwon #rating #using: Improving top-n recommendation techniques using rating variance (YK), pp. 307–310.
RecSys-2008-LakiotakiTM #analysis #multi #named: UTA-Rec: a recommender system based on multiple criteria analysis (KL, ST, NFM), pp. 219–226.
RecSys-2008-Lee #named #trust: PITTCULT: trust-based cultural event recommender (DHL), pp. 311–314.
RecSys-2008-LiWZZC #named #parallel #query: Pfp: parallel fp-growth for query recommendation (HL, YW, DZ, MZ, EYC), pp. 107–114.
RecSys-2008-OostendorpR #interface #reduction: Three recommender approaches to interface controls reduction (NO, PR), pp. 235–242.
RecSys-2008-ParkT #how: The long tail of recommender systems and how to leverage it (YJP, AT), pp. 11–18.
RecSys-2008-Recio-GarciaDG #prototype: Prototyping recommender systems in jcolibri (JARG, BDA, PAGC), pp. 243–250.
RecSys-2008-RendleS #kernel #matrix #modelling #scalability: Online-updating regularized kernel matrix factorization models for large-scale recommender systems (SR, LST), pp. 251–258.
RecSys-2008-ResnickS: The information cost of manipulation-resistance in recommender systems (PR, RS), pp. 147–154.
RecSys-2008-Sampaio #internet #network #performance #process: A network performance recommendation process for advanced internet applications users (LNS), pp. 315–318.
RecSys-2008-Santos #adaptation #lifecycle #standard: A recommender system to provide adaptive and inclusive standard-based support along the elearning life cycle (OCS), pp. 319–322.
RecSys-2008-ShaniCM #mining #web: Mining recommendations from the web (GS, DMC, CM), pp. 35–42.
RecSys-2008-ShepitsenGMB #clustering #personalisation #social #using: Personalized recommendation in social tagging systems using hierarchical clustering (AS, JG, BM, RDB), pp. 259–266.
RecSys-2008-SymeonidisNM #reduction: Tag recommendations based on tensor dimensionality reduction (PS, AN, YM), pp. 43–50.
RecSys-2008-Teppan: Implications of psychological phenomenons for recommender systems (ECT), pp. 323–326.
RecSys-2008-Umyarov #performance #predict: Leveraging aggregate ratings for improving predictive performance of recommender systems (AU), pp. 327–330.
RecSys-2008-WuWC #analysis #automation #incremental #probability #semantics: Incremental probabilistic latent semantic analysis for automatic question recommendation (HW, YW, XC), pp. 99–106.
RecSys-2008-XuJL #documentation #image #online #personalisation #video: Personalized online document, image and video recommendation via commodity eye-tracking (SX, HJ, FCML), pp. 83–90.
RecSys-2008-ZanardiC #ranking #social #using: Social ranking: uncovering relevant content using tag-based recommender systems (VZ, LC), pp. 51–58.
RecSys-2008-Zanker #collaboration #constraints: A collaborative constraint-based meta-level recommender (MZ), pp. 139–146.
RecSys-2008-ZhangH: Avoiding monotony: improving the diversity of recommendation lists (MZ, NH), pp. 123–130.
SEKE-2008-Jannach #development #knowledge-based: Knowledge-based System Development with Scripting Technology: A Recommender System Example (DJ), pp. 405–416.
SEKE-2008-LucasSM #classification #personalisation #towards #using: Comparing the Use of Traditional and Associative Classifiers towards Personalized Recommendations (JPL, SS, MNMG), pp. 607–612.
SIGIR-2008-CreceliusKMNPSW #social: Social recommendations at work (TC, MK, SM, TN, JXP, RS, GW), p. 884.
SIGIR-2008-SongZLZLLG #automation #realtime: Real-time automatic tag recommendation (YS, ZZ, HL, QZ, JL, WCL, CLG), pp. 515–522.
SIGIR-2008-ZhangZL #algorithm #rank #topic: A topical PageRank based algorithm for recommender systems (LZ, KZ, CL), pp. 713–714.
RE-2008-Castro-HerreraDCM #data mining #elicitation #mining #process #requirements #scalability #using: Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes (CCH, CD, JCH, BM), pp. 165–168.
ASE-2008-ZhangGC #analysis #automation #clustering: Automated Aspect Recommendation through Clustering-Based Fan-in Analysis (DZ, YG, XC), pp. 278–287.
ICSE-2008-DagenaisR #adaptation #evolution #framework: Recommending adaptive changes for framework evolution (BD, MPR), pp. 481–490.
SAC-2008-LathiaHC #community #correlation: The effect of correlation coefficients on communities of recommenders (NL, SH, LC), pp. 2000–2005.
SAC-2008-LohLSWO #keyword #representation #taxonomy: Comparing keywords and taxonomies in the representation of users profiles in a content-based recommender system (SL, FL, GS, LKW, JPMdO), pp. 2030–2034.
SAC-2008-TaghipourK #hybrid #web: A hybrid web recommender system based on Q-learning (NT, AAK), pp. 1164–1168.
SAC-2008-Tso-SutterMS #algorithm #collaboration: Tag-aware recommender systems by fusion of collaborative filtering algorithms (KHLTS, LBM, LST), pp. 1995–1999.
SAC-2008-VictorCTC #trust: Whom should I trust?: the impact of key figures on cold start recommendations (PV, CC, AT, MDC), pp. 2014–2018.
ASPLOS-2008-McCunePPRS #execution #how: How low can you go?: recommendations for hardware-supported minimal TCB code execution (JMM, BP, AP, MKR, AS), pp. 14–25.
ECDL-2007-Neumann #interactive: Motivating and Supporting User Interaction with Recommender Systems (AWN), pp. 428–439.
HT-2007-BradshawL: Annotation consensus: implications for passage recommendation in scientific literature (SB, ML), pp. 209–216.
JCDL-2007-PohlRJ #library: Recommending related papers based on digital library access records (SP, FR, TJ), pp. 417–418.
JCDL-2007-SanchezAV #library #resource management: Induced tagging: promoting resource discovery and recommendation in digital libraries (JAS, AAP, OV), pp. 396–397.
JCDL-2007-ShimboIM #analysis #evaluation #kernel #metric #research: Evaluation of kernel-based link analysis measures on research paper recommendation (MS, TI, YM), pp. 354–355.
MSR-2007-MintoM: Recommending Emergent Teams (SM, GCM), p. 5.
HCI-MIE-2007-KoLKJL #case study #evaluation #personalisation #user satisfaction: A Study on User Satisfaction Evaluation About the Recommendation Techniques of a Personalized EPG System on Digital TV (SMK, YJL, MHK, YGJ, SWL), pp. 909–917.
HCI-MIE-2007-OkadaI #collaboration #evaluation: Evaluation of P2P Information Recommendation Based on Collaborative Filtering (HO, MI), pp. 449–458.
HIMI-MTT-2007-OrimoKMT #analysis #evaluation: Analysis and Evaluation of Recommendation Systems (EO, HK, TM, AT), pp. 144–152.
CAiSE-2007-StirnaPS #case study #enterprise #experience #modelling: Participative Enterprise Modeling: Experiences and Recommendations (JS, AP, KS), pp. 546–560.
ICEIS-EIS-2007-SandkuhlOSSK #case study #experience #industrial #ontology: Ontology Construction in Practice — Experiences and Recommendations from Industrial Cases (KS, AÖ, AVS, NS, AK), pp. 250–256.
ICEIS-HCI-2007-MelguizoBDBB #memory management #what: What a Proactive Recommendation System Needs — Relevance, Non-Intrusiveness, and a New Long-Term Memory (MCPM, TB, AD, LB, AvdB), pp. 86–91.
ICEIS-HCI-2007-UchyigitCC #2d #named #user interface: KEXPLORATOR — A 2D Map Exploration User Interface for Recommender Systems (GU, KLC, DC), pp. 223–228.
ICEIS-SAIC-2007-DrumondGL #case study #modelling #specification: A Case Study on the Application of the MAAEM Methodology for the Specification Modeling of Recommender Systems in the Legal Domain (LD, RG, AL), pp. 155–160.
ICEIS-SAIC-2007-LazanasKK #hybrid #policy #web #web service: Applying Hybrid Recommendation Policies through Agent-Invoked Web Services in E-Markets (AL, NIK, VK), pp. 161–166.
ECIR-2007-CastagnosB #community #distributed #personalisation: Personalized Communities in a Distributed Recommender System (SC, AB), pp. 343–355.
KDD-2007-BellKV #modelling #multi #scalability: Modeling relationships at multiple scales to improve accuracy of large recommender systems (RMB, YK, CV), pp. 95–104.
KDD-2007-DingSJL #framework #kernel #learning #using: A learning framework using Green’s function and kernel regularization with application to recommender system (CHQD, RJ, TL, HDS), pp. 260–269.
KDD-2007-Schickel-ZuberF #clustering #learning #using: Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
RecSys-2007-BogersB #algorithm #information retrieval: Comparing and evaluating information retrieval algorithms for news recommendation (TB, AvdB), pp. 141–144.
RecSys-2007-BridgeR #editing #query: Supporting product selection with query editing recommendations (DGB, FR), pp. 65–72.
RecSys-2007-ChenP #evaluation #hybrid: The evaluation of a hybrid critiquing system with preference-based recommendations organization (LC, PP), pp. 169–172.
RecSys-2007-Donaldson #hybrid #music: A hybrid social-acoustic recommendation system for popular music (JD), pp. 187–190.
RecSys-2007-FreyneFC #data access #social #towards: Toward the exploitation of social access patterns for recommendation (JF, RF, MC), pp. 179–182.
RecSys-2007-HarperSF #clustering #social: Supporting social recommendations with activity-balanced clustering (FMH, SS, DF), pp. 165–168.
RecSys-2007-LeinoR: Case amazon: ratings and reviews as part of recommendations (JL, KJR), pp. 137–140.
RecSys-2007-LiDEL #probability: A probabilistic model for item-based recommender systems (ML, MBD, WED, PJGL), pp. 129–132.
RecSys-2007-Lorenzi #approach #knowledge-based #maintenance #multi: A multiagent knowledge-based recommender approach with truth maintenance (FL), pp. 195–198.
RecSys-2007-MassaA #trust: Trust-aware recommender systems (PM, PA), pp. 17–24.
RecSys-2007-McCarthy #challenge #physics: The challenges of recommending digital selves in physical spaces (JFM), pp. 185–186.
RecSys-2007-NathansonBG #adaptation #clustering #using: Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering (TN, EB, KYG), pp. 149–152.
RecSys-2007-NguyenDB #induction #rule-based: Improving new user recommendations with rule-based induction on cold user data (ATN, ND, CB), pp. 121–128.
RecSys-2007-NguyenR #evaluation #game studies #interactive #mobile: Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations (QNN, FR), pp. 81–88.
RecSys-2007-OMahonyS #online: A recommender system for on-line course enrolment: an initial study (MPO, BS), pp. 133–136.
RecSys-2007-PronkVPT #classification #naive bayes: Incorporating user control into recommender systems based on naive bayesian classification (VP, WFJV, AP, MT), pp. 73–80.
RecSys-2007-ResnickS: The influence limiter: provably manipulation-resistant recommender systems (PR, RS), pp. 25–32.
RecSys-2007-SandvigMB #collaboration #mining #robust: Robustness of collaborative recommendation based on association rule mining (JJS, BM, RDB), pp. 105–112.
RecSys-2007-TaghipourKG #approach #learning #web: Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSys-2007-TiemannP #hybrid #learning #music #towards: Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
RecSys-2007-Tintarev: Explanations of recommendations (NT), pp. 203–206.
RecSys-2007-TintarevM #design #effectiveness: Effective explanations of recommendations: user-centered design (NT, JM), pp. 153–156.
RecSys-2007-UmyarovT: Leveraging aggregate ratings for better recommendations (AU, AT), pp. 161–164.
RecSys-2007-ViappianiPF #adaptation: Conversational recommenders with adaptive suggestions (PV, PP, BF), pp. 89–96.
RecSys-2007-WebsterV: The keepup recommender system (AW, JV), pp. 173–176.
RecSys-2007-WinterboerM: Evaluating information presentation strategies for spoken recommendations (AW, JDM), pp. 157–160.
RecSys-2007-ZhangP #algorithm #collaboration #predict #recursion: A recursive prediction algorithm for collaborative filtering recommender systems (JZ, PP), pp. 57–64.
SIGIR-2007-MeiYHYYL #named #online #video: VideoReach: an online video recommendation system (TM, BY, XSH, LY, SQY, SL), pp. 767–768.
SIGIR-2007-StrohmanCJ: Recommending citations for academic papers (TS, WBC, DJ), pp. 705–706.
SIGIR-2007-ZhangK #modelling #performance: Efficient bayesian hierarchical user modeling for recommendation system (YZ, JK), pp. 47–54.
ESEC-FSE-2007-SaulFDB #random: Recommending random walks (ZMS, VF, PTD, CB), pp. 15–24.
SAC-2007-BirukouBDGKM #approach #development #named: IC-service: a service-oriented approach to the development of recommendation systems (AB, EB, VD, PG, NK, AM), pp. 1683–1688.
SAC-2007-RuffoS #peer-to-peer: Evaluating peer-to-peer recommender systems that exploit spontaneous affinities (GR, RS), pp. 1574–1578.
CHI-2006-BonhardHMS #similarity #using: Accounting for taste: using profile similarity to improve recommender systems (PB, CH, JDM, MAS), pp. 1057–1066.
CSCW-2006-McNeeKK #research: Don’t look stupid: avoiding pitfalls when recommending research papers (SMM, NK, JAK), pp. 171–180.
ICEIS-SAIC-2006-LazanasKP #framework #platform #transaction: Providing Recommendations in an Agent-Based Transportation Transactions Management Platform (AL, NIK, YP), pp. 87–92.
KDD-2006-BurkeMWB #classification #collaboration #detection: Classification features for attack detection in collaborative recommender systems (RDB, BM, CW, RB), pp. 542–547.
KDD-2006-IwataSY: Recommendation method for extending subscription periods (TI, KS, TY), pp. 574–579.
KDD-2006-ParkPMGD #robust: Naïve filterbots for robust cold-start recommendations (STP, DP, OM, NG, DD), pp. 699–705.
KDD-2006-ZhangCFM #detection: Attack detection in time series for recommender systems (SZ, AC, JF, FM), pp. 809–814.
SEKE-2006-CazellaA #architecture #data mining #mining #multi #research: An architecture based on multi-agent system and data mining for recommending research papers and researchers (SCC, LOCA), pp. 67–72.
SIGIR-2006-SongTLS #data flow #personalisation: Personalized recommendation driven by information flow (XS, BLT, CYL, MTS), pp. 509–516.
SIGIR-2006-ZhangOFM #analysis #linear: Analysis of a low-dimensional linear model under recommendation attacks (SZ, YO, JF, FM), pp. 517–524.
SAC-2006-BaragliaLOSS #privacy #web: A privacy preserving web recommender system (RB, CL, SO, MS, FS), pp. 559–563.
SAC-2006-HessSS #documentation #personalisation: Trust-enhanced visibility for personalized document recommendations (CH, KS, CS), pp. 1865–1869.
SAC-2006-NikovskiK #induction #personalisation: Induction of compact decision trees for personalized recommendation (DN, VK), pp. 575–581.
SAC-2006-RothF #information management: Trust-decisions on the base of maximal information of recommended direct-trust (UR, VF), pp. 1898–1901.
SAC-2006-ZankerG: Recommendation-based browsing assistance for corporate knowledge portals (MZ, SG), pp. 1116–1117.
SIGMOD-2005-ConsensBTM #benchmark #metric: Goals and Benchmarks for Autonomic Configuration Recommenders (MPC, DB, AMT, LM), pp. 239–250.
ICEIS-v4-2005-FoussFKPS #web: Web Recommendation System Based on a Markov-Chainmodel (FF, SF, MK, AP, MS), pp. 56–63.
CIKM-2005-HanK: Feature-based recommendation system (EHH, GK), pp. 446–452.
KDD-2005-JinZM #collaboration #web: A maximum entropy web recommendation system: combining collaborative and content features (XJ, YZ, BM), pp. 612–617.
SEKE-2005-FelfernigG #development #effectiveness #knowledge-based #process: AI Technologies Supporting Effective Development Processes for Knowledge-based Recommender Applications (AF, SG), pp. 372–379.
SEKE-2005-LienT #network #web: A Web Pages Recommender with Bayesian Networks (CCL, HLT), pp. 82–87.
SEKE-2005-TsunodaKOMM #collaboration #java #named: Javawock: A Java Class Recommender System Based on Collaborative Filtering (MT, TK, NO, AM, KiM), pp. 491–497.
SIGIR-2005-CanoKW #music: An industrial-strength content-based music recommendation system (PC, MK, NW), p. 673.
ESEC-FSE-2005-HolmesWM: Strathcona example recommendation tool (RH, RJW, GCM), pp. 237–240.
ICSE-2005-HolmesM #source code #using: Using structural context to recommend source code examples (RH, GCM), pp. 117–125.
SAC-2005-AvesaniMT: A trust-enhanced recommender system application: Moleskiing (PA, PM, RT), pp. 1589–1593.
SAC-2005-Moloney #distributed #network #pervasive #simulation: Simulation of a distributed recommendation system for pervasive networks (SM), pp. 1577–1581.
ECDL-2004-TangM #documentation: Laws of Attraction: In Search of Document Value-ness for Recommendation (TYT, GIM), pp. 269–280.
VLDB-2004-ThorR #adaptation #named: AWESOME — A Data Warehouse-based System for Adaptive Website Recommendations (AT, ER), pp. 384–395.
ICEIS-v2-2004-BaykalAP #automation #reasoning: Automated Product Recommendation by Employing Case-Based Reasoning Agents (MÖB, RA, FP), pp. 515–518.
ICEIS-v3-2004-MontanerLR #evaluation: Evaluation of Recommender Systems Through Simulated Users (MM, BL, JLdlR), pp. 303–308.
ICEIS-v4-2004-DegemmisLSCLG #collaboration #hybrid: A Hybrid Collaborative Recommender System Based on User Profiles (MD, PL, GS, MFC, OL, SG), pp. 162–169.
ICEIS-v4-2004-KaracapilidisL #framework #online: A Recommendation Based Framework for Online Product Configuration (NIK, TL), pp. 303–308.
ICEIS-v4-2004-LohGLBRSAP #chat #library #web: Analyzing Web Chat Messages for Recommending Items from a Digital Library (SL, RSG, DL, TB, RR, GS, LA, TP), pp. 41–48.
ICEIS-v5-2004-GonzalezLR #modelling: Managing Emotions in Smart User Models for Recommender Systems (GG, BL, JLdlR), pp. 187–194.
ICEIS-v5-2004-PluAVM #social #social media: A Contact Recommender System for a Mediated Social Media (MP, LA, LV, JCM), pp. 107–114.
CIKM-2004-JungHWH #named: SERF: integrating human recommendations with search (SJ, KH, JW, JLH), pp. 571–580.
CIKM-2004-ZieglerLS: Taxonomy-driven computation of product recommendations (CNZ, GL, LST), pp. 406–415.
KDD-2004-AliS #architecture #collaboration #distributed #named #using: TiVo: making show recommendations using a distributed collaborative filtering architecture (KA, WvS), pp. 394–401.
SIGIR-2004-LiKGO #music: A music recommender based on audio features (QL, BMK, DG, DwO), pp. 532–533.
SIGIR-2004-UpstillR #web: Exploiting hyperlink recommendation evidence in navigational web search (TU, SER), pp. 576–577.
SAC-2004-StraubH #mobile: An anonymous bonus point system for mobile commerce based on word-of-mouth recommendation (TS, AH), pp. 766–773.
ECDL-2003-Geyer-SchulzNT #library #robust: Others Also Use: A Robust Recommender System for Scientific Libraries (AGS, AWN, AT), pp. 113–125.
HT-2003-MacedoTCP #automation #case study #experience #web: Automatically sharing web experiences through a hyperdocument recommender system (AAM, KNT, JACG, MdGCP), pp. 48–56.
CHI-2003-CosleyLAKR #how #interface: Is seeing believing?: how recommender system interfaces affect users’ opinions (DC, SKL, IA, JAK, JR), pp. 585–592.
CHI-2003-McDonald #collaboration #comparative #evaluation #network #social: Recommending collaboration with social networks: a comparative evaluation (DWM), pp. 593–600.
ICEIS-v2-2003-WangRLC #mining #online #realtime #web: Mining Web Usage Data for Real-Time Online Recommendation (MW, SJR, SYL, JKYC), pp. 575–578.
ICEIS-v4-2003-LiKM #architecture #automation #e-commerce #multi: Multi-Agent Architecture for Automatic Recommendation System in E-Commerce (QL, RK, YM), pp. 265–270.
ECIR-2003-TianC #collaboration #learning #rating #similarity: Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
KDD-2003-Kamishima #collaboration #order: Nantonac collaborative filtering: recommendation based on order responses (TK), pp. 583–588.
ICSE-2003-CubranicM #development #named: Hipikat: Recommending Pertinent Software Development Artifacts (DC, GCM), pp. 408–418.
JCDL-2002-HuangCOC #graph #library: A graph-based recommender system for digital library (ZH, WC, THO, HC), pp. 65–73.
VLDB-2002-CosleyLP #framework #named #testing #using: REFEREE: An Open Framework for Practical Testing of Recommender Systems using ResearchIndex (DC, SL, DMP), pp. 35–46.
CSCW-2002-McNeeACGLRKR #on the #research: On the recommending of citations for research papers (SMM, IA, DC, PG, SKL, AMR, JAK, JR), pp. 116–125.
CAiSE-2002-PapadopoulosP #community #semantics: The Role of Semantic Relevance in Dynamic User Community Management and the Formulation of Recommendations (NP, DP), pp. 200–215.
CIKM-2002-SchaferKR #integration: Meta-recommendation systems: user-controlled integration of diverse recommendations (JBS, JAK, JR), pp. 43–51.
SIGIR-2002-ScheinPUP #metric: Methods and metrics for cold-start recommendations (AIS, AP, LHU, DMP), pp. 253–260.
STOC-2002-DrineasKR: Competitive recommendation systems (PD, IK, PR), pp. 82–90.
ECDL-2001-KimK #documentation #modelling #web: Dynamic Models of Expert Groups to Recommend Web Documents (DK, SWK), pp. 275–286.
JCDL-2001-GeislerMG #library #nondeterminism: Developing recommendation services for a digital library with uncertain and changing data (GG, DM, SG), pp. 199–200.
CIKM-2001-ChenC #music: A Music Recommendation System Based on Music Data Grouping and User Interests (HCC, ALPC), pp. 231–238.
CIKM-2001-Karypis #algorithm #evaluation: Evaluation of Item-Based Top-N Recommendation Algorithms (GK), pp. 247–254.
ICML-2001-Lee #collaboration #learning: Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
KDD-2001-Riedl #community: Recommender systems in commerce and community (JR), p. 15.
DL-2000-MooneyR #categorisation #learning #using: Content-based book recommending using learning for text categorization (RJM, LR), pp. 195–204.
ECDL-2000-BollenR #adaptation #approach #evaluation #implementation #library: An Adaptive Systems Approach to the Implementation and Evaluation of Digital Library Recommendation Systems (JB, LMR), pp. 356–359.
CHI-2000-WoodruffGPCC: Enhancing a digital book with a reading recommender (AW, RG, JEP, EHhC, SKC), pp. 153–160.
CSCW-2000-HerlockerKR #collaboration: Explaining collaborative filtering recommendations (JLH, JAK, JR), pp. 241–250.
CSCW-2000-McDonaldA #architecture #flexibility: Expertise recommender: a flexible recommendation system and architecture (DWM, MSA), pp. 231–240.
CIKM-2000-LavrenkoSLOJA #modelling: Language Models for Financial News Recommendation (VL, MDS, DL, PO, DJ, JA), pp. 389–396.
KDD-2000-KittsFV #independence #named #performance: Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities (BK, DF, MV), pp. 437–446.
DL-1999-Rocha #adaptation: TalkMine and the Adaptive Recommendation Project (LMR), pp. 242–243.
CSEET-1999-Tockey: Recommended Skills and Knowledge for Software Engineers (SRT), pp. 168–176.
HCI-CCAD-1999-TrousseJK #approach #behaviour #similarity #using: Using user behaviour similarity for recommendation computation: the broadway approach (BT, MJ, RK), pp. 85–89.
ITiCSE-WGR-1997-JoyceKGKKLLSW #design #guidelines #repository: Developing laboratories for the SIGCSE computing laboratory repository: guidelines, recommendations, and sample labs (report of the ITiCSE 1997 working group on designing laboratory materials for computing courses) (DTJ, DK, JGP, EBK, WK, CL, KL, ES, RAW), pp. 1–12.
CHI-1995-HillSRF #community: Recommending and Evaluating Choices in a Virtual Community of Use (WCH, LS, MR, GWF), pp. 194–201.
AdaEurope-1994-Gale #ada #development: Recommendations and Proposals for an Ada Strategy in the Space Software Development Environment (LPG), pp. 175–203.
SEI-1992-MedairosCCK #re-engineering: Software Engineering Course Projects: Failures and Recommendations (SM, KWC, JSC, MK), pp. 324–338.
CHI-1992-WhartonBJF #case study #experience #user interface: Applying cognitive walkthroughs to more complex user interfaces: experiences, issues, and recommendations (CW, JB, RJ, MF), pp. 381–388.
SIGIR-1983-Somers #analysis #file system #user interface: The User View of File Management: Recommendations for a User Interface Based in an Analysis of UNIX File System Use (PS), p. 161.