BibSLEIGH
BibSLEIGH corpus
BibSLEIGH tags
BibSLEIGH bundles
BibSLEIGH people
CC-BY
Open Knowledge
XHTML 1.0 W3C Rec
CSS 2.1 W3C CanRec
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Used together with:
system (381)
base (223)
user (120)
social (117)
use (104)

Stem recommend$ (all stems)

1152 papers:

HTHT-2015-HaKK #on the #recommendation
On Recommending Newly Published Academic Papers (JH, SHK, SWK), pp. 329–330.
HTHT-2015-LeeHKGH #on the #recommendation
On Recommending Job Openings (YCL, JH, SWK, SG, JYH), pp. 331–332.
HTHT-2015-Orellana-Rodriguez #mining #recommendation
Mining Affective Context in Short Films for Emotion-Aware Recommendation (COR, EDA, WN), pp. 185–194.
HTHT-2015-PeraN #recommendation
Analyzing Book-Related Features to Recommend Books for Emergent Readers (MSP, YKN), pp. 221–230.
SIGMODSIGMOD-2015-HuangCZJX #named #realtime #recommendation
TencentRec: Real-time Stream Recommendation in Practice (YH, BC, WZ, JJ, YX), pp. 227–238.
SIGMODSIGMOD-2015-RoyLL #recommendation
From Group Recommendations to Group Formation (SBR, LVSL, RL), pp. 1603–1616.
SIGMODSIGMOD-2015-ZhouCZCHW #community #online #recommendation #video
Online Video Recommendation in Sharing Community (XZ, LC, YZ, LC, GH, CW), pp. 1645–1656.
VLDBVLDB-2015-ChenGXJC #distributed #image #named #recommendation #retrieval
I2RS: A Distributed Geo-Textual Image Retrieval and Recommendation System (LC, YG, ZX, CSJ, GC), pp. 1884–1895.
VLDBVLDB-2015-GuerraouiKPT #difference #named #privacy #recommendation
D2P: Distance-Based Differential Privacy in Recommenders (RG, AMK, RP, MT), pp. 862–873.
VLDBVLDB-2015-VartakRMPP #data-driven #named #performance #recommendation #visual notation #visualisation
SEEDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics (MV, SR, SM, AGP, NP), pp. 2182–2193.
ICPCICPC-2015-AmintabarHG #development #exception #ide #named #recommendation
ExceptionTracer: a solution recommender for exceptions in an integrated development environment (VA, AH, MG), pp. 299–302.
ICSMEICSME-2015-AsaduzzamanRMS #api #parametricity #recommendation
Exploring API method parameter recommendations (MA, CKR, SM, KAS), pp. 271–280.
ICSMEICSME-2015-AsaduzzamanRS #api #named #parametricity #recommendation
PARC: Recommending API methods parameters (MA, CKR, KAS), pp. 330–332.
ICSMEICSME-2015-XiaLWY #analysis #bibliography #recommendation
Who should review this change?: Putting text and file location analyses together for more accurate recommendations (XX, DL, XW, XY), pp. 261–270.
MSRMSR-2015-WangMG #api #developer #recommendation
Recommending Posts concerning API Issues in Developer Q&A Sites (WW, HM, MWG), pp. 224–234.
SANERSANER-2015-ThongtanunamTKY #approach #bibliography #code review #perspective #recommendation
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.
SCAMSCAM-2015-RahmanRK #crowdsourcing #recommendation #source code #using
Recommending insightful comments for source code using crowdsourced knowledge (MMR, CKR, IK), pp. 81–90.
CHICHI-2015-LoeppH0 #algorithm #information management #interactive #recommendation
Blended Recommending: Integrating Interactive Information Filtering and Algorithmic Recommender Techniques (BL, KH, JZ), pp. 975–984.
CSCWCSCW-2015-ChangHT #recommendation #using
Using Groups of Items to Bootstrap New Users in Recommender Systems (SC, FMH, LGT), pp. 1258–1269.
HCIDUXU-IXD-2015-WalterKWAB #adaptation #question #recommendation #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.
HCIHIMI-IKC-2015-BrunsVGZS #personalisation #recommendation #visual notation #what
What Should I Read Next? A Personalized Visual Publication Recommender System (SB, ACV, CG, MZ, US), pp. 89–100.
HCIHIMI-IKC-2015-Kaewkiriya #design #framework #recommendation #student
Design of Framework for Students Recommendation System in Information Technology Skills (TK), pp. 109–117.
HCIHIMI-IKC-2015-VerstocktSB #recommendation
Map-Based Linking of Geographic User and Content Profiles for Hyperlocal Content Recommendation (SV, VS, KB), pp. 53–63.
HCILCT-2015-Iitaka #online #recommendation
Recommendation Engine for an Online Drill System (TI), pp. 238–248.
HCILCT-2015-RodriguezOD #hybrid #learning #recommendation #repository #student
A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories (PAR, DAO, NDD), pp. 291–300.
HCILCT-2015-Sirisaengtaksin #approach #design #education #mobile #online #recommendation #research
A Notification and Recommender Mobile App for Educational Online Discussion: A Design Research Approach (KS, LO, NA), pp. 325–336.
ICEISICEIS-v1-2015-Kozmina #empirical #recommendation
An Empirical Study of Recommendations in OLAP Reporting Tool (NK), pp. 303–312.
ICEISICEIS-v2-2015-CeredaN #adaptation #automaton #recommendation
A Recommendation Engine based on Adaptive Automata (PRMC, JJN), pp. 594–601.
ICEISICEIS-v2-2015-SmirnovP #architecture #hybrid #network #peer-to-peer #privacy #recommendation
Privacy-preserving Hybrid Peer-to-Peer Recommendation System Architecture — Locality-Sensitive Hashing in Structured Overlay Network (AVS, AP), pp. 532–542.
ECIRECIR-2015-HopfgartnerB #realtime #recommendation
Join the Living Lab: Evaluating News Recommendations in Real-Time (FH, TB), pp. 826–829.
ECIRECIR-2015-SchedlHFT #algorithm #music #on the #recommendation
On the Influence of User Characteristics on Music Recommendation Algorithms (MS, DH, KF, MT), pp. 339–345.
ECIRECIR-2015-ValcarcePB #case study #modelling #recommendation
A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems (DV, JP, AB), pp. 346–351.
ECIRECIR-2015-WangHS0W0 #network #problem #recommendation #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.
KDDKDD-2015-BerkovskyF #personalisation #recommendation #web
Web Personalization and Recommender Systems (SB, JF), pp. 2307–2308.
KDDKDD-2015-FrenoSJA #modelling #ranking #recommendation
One-Pass Ranking Models for Low-Latency Product Recommendations (AF, MS, RJ, CA), pp. 1789–1798.
KDDKDD-2015-GrbovicRDBSBS #e-commerce #recommendation #scalability
E-commerce in Your Inbox: Product Recommendations at Scale (MG, VR, ND, NB, JS, VB, DS), pp. 1809–1818.
KDDKDD-2015-HolleczekAYJAGL #agile #metric #recommendation
Traffic Measurement and Route Recommendation System for Mass Rapid Transit (MRT) (TH, DTA, SY, YJ, SA, HLG, SL, ASN), pp. 1859–1868.
KDDKDD-2015-HsiehLZ #big data #quality #recommendation
Inferring Air Quality for Station Location Recommendation Based on Urban Big Data (HPH, SDL, YZ), pp. 437–446.
KDDKDD-2015-JiangZZY #e-commerce #predict #recommendation
Life-stage Prediction for Product Recommendation in E-commerce (PJ, YZ, YZ, QY), pp. 1879–1888.
KDDKDD-2015-Kawamae #realtime #recommendation
Real Time Recommendations from Connoisseurs (NK), pp. 537–546.
KDDKDD-2015-QianCMSL #named #recommendation
SCRAM: A Sharing Considered Route Assignment Mechanism for Fair Taxi Route Recommendations (SQ, JC, FLM, IS, ML), pp. 955–964.
KDDKDD-2015-SongMT #network #performance #recommendation
Efficient Latent Link Recommendation in Signed Networks (DS, DAM, DT), pp. 1105–1114.
KDDKDD-2015-WangWY #collaboration #learning #recommendation
Collaborative Deep Learning for Recommender Systems (HW, NW, DYY), pp. 1235–1244.
KDDKDD-2015-WangYCSSZ #generative #named #recommendation
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation (WW, HY, LC, YS, SWS, XZ), pp. 1255–1264.
KDDKDD-2015-ZhangW #recommendation
A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation (WZ, JW), pp. 1455–1464.
KDDKDD-2015-ZhongJYYZL #recommendation
Stock Constrained Recommendation in Tmall (WZ, RJ, CY, XY, QZ, QL), pp. 2287–2296.
KDDKDD-2015-ZhongLSR #recommendation #scalability
Building Discriminative User Profiles for Large-scale Content Recommendation (EZ, NL, YS, SR), pp. 2277–2286.
RecSysRecSys-2015-Abel #recommendation
We Know Where You Should Work Next Summer: Job Recommendations (FA), p. 230.
RecSysRecSys-2015-AghdamHMB #adaptation #markov #modelling #recommendation #using
Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models (MHA, NH, BM, RDB), pp. 241–244.
RecSysRecSys-2015-AharonAADGS #named #recommendation
ExcUseMe: Asking Users to Help in Item Cold-Start Recommendations (MA, OA, NAE, DDC, SG, OS), pp. 83–90.
RecSysRecSys-2015-BanksRS #game studies #recommendation #using
The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data (SB, RR, BS), pp. 305–308.
RecSysRecSys-2015-BansalDB #profiling #recommendation
Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (TB, MKD, CB), pp. 195–202.
RecSysRecSys-2015-BarjastehFMER #recommendation
Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.
RecSysRecSys-2015-BetzalelSR #exclamation #quote #recommendation
“Please, Not Now!”: A Model for Timing Recommendations (NDB, BS, LR), pp. 297–300.
RecSysRecSys-2015-BistaffaFCR #recommendation #scalability #social
Recommending Fair Payments for Large-Scale Social Ridesharing (FB, AF, GC, SDR), pp. 139–146.
RecSysRecSys-2015-Bourke #multi #recommendation
The Application of Recommender Systems in a Multi Site, Multi Domain Environment (SB), p. 229.
RecSysRecSys-2015-ChaneyBE #network #personalisation #probability #recommendation #social #using
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (AJBC, DMB, TER), pp. 43–50.
RecSysRecSys-2015-ChristoffelPNB #random #recommendation #scalability
Blockbusters and Wallflowers: Accurate, Diverse, and Scalable Recommendations with Random Walks (FC, BP, CN, AB), pp. 163–170.
RecSysRecSys-2015-DalyBS #recommendation
Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates (ED, MB, FS), pp. 253–256.
RecSysRecSys-2015-Das #recommendation
Making Meaningful Restaurant Recommendations At OpenTable (SD), p. 235.
RecSysRecSys-2015-EkstrandKHK #algorithm #case study #recommendation
Letting Users Choose Recommender Algorithms: An Experimental Study (MDE, DK, FMH, JAK), pp. 11–18.
RecSysRecSys-2015-ElsweilerH #automation #recommendation #towards
Towards Automatic Meal Plan Recommendations for Balanced Nutrition (DE, MH), pp. 313–316.
RecSysRecSys-2015-ForsatiBMER #algorithm #named #performance #recommendation #trust
PushTrust: An Efficient Recommendation Algorithm by Leveraging Trust and Distrust Relations (RF, IB, FM, AHE, HR), pp. 51–58.
RecSysRecSys-2015-GeRM #recommendation
Health-aware Food Recommender System (MG, FR, DM), pp. 333–334.
RecSysRecSys-2015-Geuens #behaviour #hybrid #recommendation
Factorization Machines for Hybrid Recommendation Systems Based on Behavioral, Product, and Customer Data (SG), pp. 379–382.
RecSysRecSys-2015-GriesnerAN #matrix #recommendation #towards
POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences (JBG, TA, HN), pp. 301–304.
RecSysRecSys-2015-Guardia-Sebaoun #modelling #performance #recommendation
Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems (ÉGS, VG, PG), pp. 281–284.
RecSysRecSys-2015-Guy #personalisation #recommendation
The Role of User Location in Personalized Search and Recommendation (IG), p. 236.
RecSysRecSys-2015-HarperXKCCT #recommendation
Putting Users in Control of their Recommendations (FMH, FX, HK, KC, SC, LGT), pp. 3–10.
RecSysRecSys-2015-HarveyE #automation #personalisation #recommendation
Automated Recommendation of Healthy, Personalised Meal Plans (MH, DE), pp. 327–328.
RecSysRecSys-2015-HopfgartnerKHT #realtime #recommendation
Real-time Recommendation of Streamed Data (FH, BK, TH, RT), pp. 361–362.
RecSysRecSys-2015-HuD #machine learning #recommendation #scalability
Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
RecSysRecSys-2015-JannachLJ #adaptation #evaluation #recommendation
Adaptation and Evaluation of Recommendations for Short-term Shopping Goals (DJ, LL, MJ), pp. 211–218.
RecSysRecSys-2015-KangDS #recommendation
Elsevier Journal Finder: Recommending Journals for your Paper (NK, MAD, RJAS), pp. 261–264.
RecSysRecSys-2015-KaragiannakisGS #automation #category theory #recommendation
OSMRec Tool for Automatic Recommendation of Categories on Spatial Entities in OpenStreetMap (NK, GG, DS, SA), pp. 337–338.
RecSysRecSys-2015-KoukiFFEG #flexibility #framework #hybrid #named #probability #recommendation
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems (PK, SF, JRF, ME, LG), pp. 99–106.
RecSysRecSys-2015-KowaldL #algorithm #case study #comparative #folksonomy #recommendation
Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study (DK, EL), pp. 265–268.
RecSysRecSys-2015-LerallutGR #realtime #recommendation #scalability
Large-Scale Real-Time Product Recommendation at Criteo (RL, DG, NLR), p. 232.
RecSysRecSys-2015-LimML #feedback #recommendation
Top-N Recommendation with Missing Implicit Feedback (DL, JM, GRGL), pp. 309–312.
RecSysRecSys-2015-LiuK #named #recommendation
Kibitz: End-to-End Recommendation System Builder (QL, DRK), pp. 335–336.
RecSysRecSys-2015-LiWTM #community #predict #rating #recommendation #social
Overlapping Community Regularization for Rating Prediction in Social Recommender Systems (HL, DW, WT, NM), pp. 27–34.
RecSysRecSys-2015-LuC #personalisation #recommendation
Exploiting Geo-Spatial Preference for Personalized Expert Recommendation (HL, JC), pp. 67–74.
RecSysRecSys-2015-Ludmann #data type #online #recommendation
Online Recommender Systems based on Data Stream Management Systems (CAL), pp. 391–394.
RecSysRecSys-2015-MacedoMS #network #recommendation #social
Context-Aware Event Recommendation in Event-based Social Networks (AQdM, LBM, RLTS), pp. 123–130.
RecSysRecSys-2015-MagnusonDM #process #recommendation #twitter #using
Event Recommendation using Twitter Activity (AM, VD, DM), pp. 331–332.
RecSysRecSys-2015-MaksaiGF #evaluation #metric #online #performance #predict #recommendation
Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics (AM, FG, BF), pp. 179–186.
RecSysRecSys-2015-MarinhoTP #algorithm #question #recommendation
Are Real-World Place Recommender Algorithms Useful in Virtual World Environments? (LBM, CT, DP), pp. 245–248.
RecSysRecSys-2015-MojsilovicV #enterprise #perspective #recommendation
Assessing Expertise in the Enterprise: The Recommender Point of View (AM, KRV), p. 231.
RecSysRecSys-2015-Nemeth #recommendation #scalability
Scaling Up Recommendation Services in Many Dimensions (BN), p. 233.
RecSysRecSys-2015-NeumannS #recommendation
Recommendations for Live TV (JN, HS), p. 228.
RecSysRecSys-2015-NovA #recommendation #social #symmetry
Asymmetric Recommendations: The Interacting Effects of Social Ratings? Direction and Strength on Users’ Ratings (ON, OA), pp. 249–252.
RecSysRecSys-2015-SaidB #evaluation #recommendation
Replicable Evaluation of Recommender Systems (AS, AB), pp. 363–364.
RecSysRecSys-2015-Salehi-AbariB #network #recommendation #social
Preference-oriented Social Networks: Group Recommendation and Inference (ASA, CB), pp. 35–42.
RecSysRecSys-2015-Santos #hybrid #recommendation
A Hybrid Recommendation System Based on Human Curiosity (AMdS), pp. 367–370.
RecSysRecSys-2015-SeminarioW #collaboration #recommendation
Nuke ’Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders (CES, DCW), pp. 293–296.
RecSysRecSys-2015-ShalomBRZA #matter #quality #recommendation
Data Quality Matters in Recommender Systems (OSS, SB, RR, EZ, AA), pp. 257–260.
RecSysRecSys-2015-SongCL #incremental #matrix #recommendation
Incremental Matrix Factorization via Feature Space Re-learning for Recommender System (QS, JC, HL), pp. 277–280.
RecSysRecSys-2015-SousaDBM #analysis #named #network #recommendation
CNARe: Co-authorship Networks Analysis and Recommendations (GAdS, MAD, MAB, MMM), pp. 329–330.
RecSysRecSys-2015-SteckZJ #interactive #recommendation #tutorial
Interactive Recommender Systems: Tutorial (HS, RvZ, CJ), pp. 359–360.
RecSysRecSys-2015-Unger #recommendation
Latent Context-Aware Recommender Systems (MU), pp. 383–386.
RecSysRecSys-2015-Valcarce #modelling #recommendation #statistics
Exploring Statistical Language Models for Recommender Systems (DV), pp. 375–378.
RecSysRecSys-2015-ValcarcePB #case study #modelling #recommendation
A Study of Priors for Relevance-Based Language Modelling of Recommender Systems (DV, JP, AB), pp. 237–240.
RecSysRecSys-2015-VerstrepenG #recommendation
Top-N Recommendation for Shared Accounts (KV, BG), pp. 59–66.
RecSysRecSys-2015-ZhaoZ0 #recommendation
Risk-Hedged Venture Capital Investment Recommendation (XZ, WZ, JW), pp. 75–82.
RecSysRecSys-2015-ZhaoZFT #e-commerce #personalisation #recommendation
E-commerce Recommendation with Personalized Promotion (QZ, YZ, DF, FT), pp. 219–226.
RecSysRecSys-2015-Zoeter #recommendation
Recommendations in Travel (OZ), p. 234.
SEKESEKE-2015-Colace0LLYC #adaptation #perspective #recommendation
An Adaptive Contextual Recommender System: a Slow Intelligence Perspective (FC, LG, SL, ML, DY, SKC), pp. 64–71.
SEKESEKE-2015-LiuXC #learning #recommendation
Context-aware Recommendation System with Anonymous User Profile Learning (YL, YX, MC), pp. 93–98.
SEKESEKE-2015-RamosCRSAP #analysis #architecture #recommendation
Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis (FBAR, AAMC, RRdS, GS, HOdA, AP), pp. 99–104.
SEKESEKE-2015-ZhaoSCZ #crowdsourcing #developer #effectiveness #recommendation #towards
Towards Effective Developer Recommendation in Software Crowdsourcing (SZ, BS, YC, HZ), pp. 326–329.
SIGIRSIGIR-2015-ChengS #music #named #recommendation
VenueMusic: A Venue-Aware Music Recommender System (ZC, JS), pp. 1029–1030.
SIGIRSIGIR-2015-ChenLZLS #approximate #matrix #named #recommendation #scalability
WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation (CC, DL, YZ, QL, LS), pp. 303–312.
SIGIRSIGIR-2015-GuoL #automation #generative #graph #music #recommendation
Automatic Feature Generation on Heterogeneous Graph for Music Recommendation (CG, XL), pp. 807–810.
SIGIRSIGIR-2015-GuyLDB #case study #enterprise #recommendation #social
Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream (IG, RL, TD, EB), pp. 665–674.
SIGIRSIGIR-2015-HaraSKF #recommendation
Reducing Hubness: A Cause of Vulnerability in Recommender Systems (KH, IS, KK, KF), pp. 815–818.
SIGIRSIGIR-2015-KneesS #bibliography #music #perspective #recommendation #retrieval #tutorial
Music Retrieval and Recommendation: A Tutorial Overview (PK, MS), pp. 1133–1136.
SIGIRSIGIR-2015-LiCLPK #named #ranking #recommendation
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (XL, GC, XL, TANP, SK), pp. 433–442.
SIGIRSIGIR-2015-LuZZW #personalisation #recommendation
Exploiting User and Business Attributes for Personalized Business Recommendation (KL, YZ, LZ, SW), pp. 891–894.
SIGIRSIGIR-2015-McAuleyTSH #recommendation
Image-Based Recommendations on Styles and Substitutes (JJM, CT, QS, AvdH), pp. 43–52.
SIGIRSIGIR-2015-ReinandaMR #aspect-oriented #mining #ranking #recommendation
Mining, Ranking and Recommending Entity Aspects (RR, EM, MdR), pp. 263–272.
SIGIRSIGIR-2015-SchedlH #music #recommendation
Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty (MS, DH), pp. 947–950.
SIGIRSIGIR-2015-ShokouhiG #query #ranking #recommendation
From Queries to Cards: Re-ranking Proactive Card Recommendations Based on Reactive Search History (MS, QG), pp. 695–704.
SIGIRSIGIR-2015-SunXZLGX #multi #personalisation #recommendation
Multi-source Information Fusion for Personalized Restaurant Recommendation (JS, YX, YZ, JL, CG, HX), pp. 983–986.
SIGIRSIGIR-2015-TangJLZL #personalisation #recommendation
Personalized Recommendation via Parameter-Free Contextual Bandits (LT, YJ, LL, CZ, TL), pp. 323–332.
SIGIRSIGIR-2015-VolkovsY #effectiveness #feedback #modelling #recommendation
Effective Latent Models for Binary Feedback in Recommender Systems (MV, GWY), pp. 313–322.
SIGIRSIGIR-2015-WangGLXWC #learning #recommendation #representation
Learning Hierarchical Representation Model for NextBasket Recommendation (PW, JG, YL, JX, SW, XC), pp. 403–412.
SIGIRSIGIR-2015-WangSWZSLL #cumulative #recommendation
An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation (JW, DS, QW, ZZ, LS, LL, CYL), pp. 635–644.
SIGIRSIGIR-2015-XuWW #collaboration #personalisation #ranking #recommendation #semantics
Personalized Semantic Ranking for Collaborative Recommendation (SX, SW, LW), pp. 971–974.
SIGIRSIGIR-2015-YaoSQWSH #recommendation #social #using
Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization (LY, QZS, YQ, XW, AS, QH), pp. 1007–1010.
SIGIRSIGIR-2015-ZhangC #category theory #correlation #named #recommendation #social
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (JDZ, CYC), pp. 443–452.
SIGIRSIGIR-2015-ZhangCQZL #multi #personalisation #recommendation #similarity
When Personalization Meets Conformity: Collective Similarity based Multi-Domain Recommendation (XZ, JC, SQ, ZZ, HL), pp. 1019–1022.
SACSAC-2015-AissiGSS #evaluation #framework #personalisation #query #recommendation
Personalized recommendation of SOLAP queries: theoretical framework and experimental evaluation (SA, MSG, TS, LBS), pp. 1008–1014.
SACSAC-2015-AliK #approach #effectiveness #recommendation
An effective approach to group recommendation based on belief propagation (IA, SWK), pp. 1148–1153.
SACSAC-2015-BurityE #approach #recommendation
A quantitative, evidence-based approach for recommending software modules (TB, GEdS), pp. 1449–1456.
SACSAC-2015-CamaraHJJ #graph #modelling #persuasion #recommendation #social #using
Using graph-based models in a persuasive social recommendation system (JPC, SH, JJ, VJ), pp. 189–194.
SACSAC-2015-CapelleMHFV #hybrid #recommendation #semantics
Bing-SF-IDF+: a hybrid semantics-driven news recommender (MC, MM, FH, FF, DV), pp. 732–739.
SACSAC-2015-Chaudhary #experience #recommendation
Experience in item based recommender system (AC), pp. 1112–1114.
SACSAC-2015-DominguesSBMPR #metadata #multi #personalisation #ranking #recommendation
Applying multi-view based metadata in personalized ranking for recommender systems (MAD, CVS, FMMB, MGM, MGCP, SOR), pp. 1105–1107.
SACSAC-2015-HsiehNKC #approximate #performance #query #recommendation
Efficient approximate thompson sampling for search query recommendation (CCH, JN, TK, JC), pp. 740–746.
SACSAC-2015-LommatzschA #realtime #recommendation
Real-time recommendations for user-item streams (AL, SA), pp. 1039–1046.
SACSAC-2015-MatuszykVSJG #incremental #matrix #recommendation
Forgetting methods for incremental matrix factorization in recommender systems (PM, JV, MS, AMJ, JG), pp. 947–953.
SACSAC-2015-PaivaBSIJ #behaviour #learning #recommendation #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.
SACSAC-2015-RodriguesJD #recommendation #using
Accelerating recommender systems using GPUs (AVR, AJ, ID), pp. 879–884.
SACSAC-2015-Zheng #algorithm #recommendation
Improve general contextual slim recommendation algorithms by factorizing contexts (YZ), pp. 929–930.
ESEC-FSEESEC-FSE-2015-LinPXZZ #interactive #recommendation
Clone-based and interactive recommendation for modifying pasted code (YL, XP, ZX, DZ, WZ), pp. 520–531.
ESEC-FSEESEC-FSE-2015-PhamSS #automation #developer #recommendation
Automatically recommending test code examples to inexperienced developers (RP, YS, KS), pp. 890–893.
ICSEICSE-v2-2015-Beyer #api #developer #mobile #named #recommendation
DIETs: Recommender Systems for Mobile API Developers (SB), pp. 859–862.
ECSAECSA-2014-HeroldM #architecture #consistency #recommendation #refactoring
Recommending Refactorings to Re-establish Architectural Consistency (SH, MM), pp. 390–397.
ASEASE-2014-BavotaPTPOC #recommendation #refactoring
Recommending refactorings based on team co-maintenance patterns (GB, SP, NT, MDP, RO, GC), pp. 337–342.
ASEASE-2014-MkaouerKBDC #interactive #optimisation #recommendation #refactoring #using
Recommendation system for software refactoring using innovization and interactive dynamic optimization (MWM, MK, SB, KD, MÓC), pp. 331–336.
HTHT-2014-KowaldLT #benchmark #framework #metric #named #recommendation #standard #towards
TagRec: towards a standardized tag recommender benchmarking framework (DK, EL, CT), pp. 305–307.
HTHT-2014-LacicKT #named #online #recommendation #scalability #social
SocRecM: a scalable social recommender engine for online marketplaces (EL, DK, CT), pp. 308–310.
HTHT-2014-QuerciaSA #recommendation
The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city (DQ, RS, LMA), pp. 116–125.
HTHT-2014-TrevisiolCB #personalisation #recommendation
Buon appetito: recommending personalized menus (MT, LC, RABY), pp. 327–329.
VLDBVLDB-2014-DaiQJWW #personalisation #recommendation
A Personalized Recommendation System for NetEase Dating Site (CD, FQ, WJ, ZW, ZW), pp. 1760–1765.
VLDBVLDB-2014-GuptaSGGZLL #detection #graph #online #realtime #recommendation #scalability #twitter
Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs (PG, VS, AG, SG, VZ, QL, JL), pp. 1379–1380.
VLDBVLDB-2014-LuCLL #recommendation
Show Me the Money: Dynamic Recommendations for Revenue Maximization (WL, SC, KL, LVSL), pp. 1785–1796.
VLDBVLDB-2014-WangLHCSWLT #named #realtime #recommendation
R3: A Real-Time Route Recommendation System (HW, GL, HH, SC, BS, HW, WSL, KLT), pp. 1549–1552.
VLDBVLDB-2014-ZhangJSR #big data #recommendation #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.
SANERCSMR-WCRE-2014-GeSDM #how #query #recommendation
How the Sando search tool recommends queries (XG, DCS, KD, ERMH), pp. 425–428.
SANERCSMR-WCRE-2014-KashiwabaraOIHYI #mining #recommendation #using
Recommending verbs for rename method using association rule mining (YK, YO, TI, YH, TY, KI), pp. 323–327.
SANERCSMR-WCRE-2014-RahmanYR #exception #fault #ide #programming #recommendation #towards
Towards a context-aware IDE-based meta search engine for recommendation about programming errors and exceptions (MMR, SY, CKR), pp. 194–203.
ICPCICPC-2014-GhafariGMT #mining #recommendation #testing
Mining unit tests for code recommendation (MG, CG, AM, GT), pp. 142–145.
ICPCICPC-2014-SilvaTV #automation #recommendation #refactoring
Recommending automated extract method refactorings (DS, RT, MTV), pp. 146–156.
ICPCICPC-2014-SteidlE #fault #maintenance #recommendation #refactoring
Prioritizing maintainability defects based on refactoring recommendations (DS, SE), pp. 168–176.
ICSMEICSME-2014-PonzanelliBPOL #named #recommendation #self
Prompter: A Self-Confident Recommender System (LP, GB, MDP, RO, ML), pp. 577–580.
ICSMEICSME-2014-WangG #design #recommendation #refactoring #using
Recommending Clones for Refactoring Using Design, Context, and History (WW, MWG), pp. 331–340.
ICSMEICSME-2014-WangLVS #named #recommendation
EnTagRec: An Enhanced Tag Recommendation System for Software Information Sites (SW, DL, BV, AS), pp. 291–300.
ICSMEICSME-2014-YuWYL #git #recommendation
Reviewer Recommender of Pull-Requests in GitHub (YY, HW, GY, CXL), pp. 609–612.
SCAMSCAM-2014-RahmanR14a #exception #on the #recommendation
On the Use of Context in Recommending Exception Handling Code Examples (MMR, CKR), pp. 285–294.
CHICHI-2014-HongA #modelling #performance #predict #recommendation #user interface
Novice use of a predictive human performance modeling tool to produce UI recommendations (KWH, RSA), pp. 2251–2254.
CHICHI-2014-LoeppHZ #collaboration #elicitation #recommendation
Choice-based preference elicitation for collaborative filtering recommender systems (BL, TH, JZ), pp. 3085–3094.
CHICHI-2014-NorvalAH #network #recommendation #social #what
What’s on your mind?: investigating recommendations for inclusive social networking and older adults (CN, JLA, VLH), pp. 3923–3932.
HCIHCI-AIMT-2014-LackeyBM #communication #interactive #recommendation #requirements
Recommended Considerations for Human-Robot Interaction Communication Requirements (SJL, DJB, SGM), pp. 663–674.
HCIHCI-AS-2014-Lopez-OrnelasAZ #recommendation
A Geo-collaborative Recommendation Tool to Help Urban Mobility (ÉLO, RAM, JSZH), pp. 466–472.
HCIHCI-AS-2014-ZiesemerMS #exclamation #gamification #recommendation
Just Rate It! Gamification as Part of Recommendation (AdCAZ, LM, MSS), pp. 786–796.
HCIHCI-TMT-2014-SousaB #recommendation #statistics
Recommender System to Support Chart Constructions with Statistical Data (TAFdS, SDJB), pp. 631–642.
HCIHIMI-AS-2014-AsikisL #recommendation #research
Operations Research and Recommender Systems (TA, GL), pp. 579–589.
HCIHIMI-DE-2014-GombosK #dataset #query #recommendation
SPARQL Query Writing with Recommendations Based on Datasets (GG, AK), pp. 310–319.
HCILCT-TRE-2014-BraunhoferEGR #learning #mobile #recommendation
Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems (MB, ME, MG, FR), pp. 105–116.
ICEISICEIS-v1-2014-TitoRSFTS #information management #named #recommendation
RecRoute — A Bus Route Recommendation System Based on Users’ Contextual Information (AdOT, ARRR, LMdS, LAVF, PRT, ACS), pp. 357–366.
ICEISICEIS-v2-2014-BorattoC #clustering #collaboration #recommendation #using
Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System (LB, SC), pp. 564–572.
ICEISICEIS-v2-2014-PanfilenkoEML #impact analysis #independence #model transformation #recommendation #requirements
Recommendations for Impact Analysis of Model Transformations — From the Requirements Model to the Platform-independent Model (DVP, AE, CM, PL), pp. 428–434.
CIKMCIKM-2014-AllahoL #latency #online #recommendation
Increasing the Responsiveness of Recommended Expert Collaborators for Online Open Projects (MYA, WCL), pp. 749–758.
CIKMCIKM-2014-DahimeneCM #named #network #recommendation #social
RecLand: A Recommender System for Social Networks (RD, CC, CdM), pp. 2063–2065.
CIKMCIKM-2014-DeveaudAMMO #named #personalisation #recommendation
SmartVenues: Recommending Popular and Personalised Venues in a City (RD, MDA, JM, CM, IO), pp. 2078–2080.
CIKMCIKM-2014-LiuWSM #recommendation
Exploiting Geographical Neighborhood Characteristics for Location Recommendation (YL, WW, AS, CM), pp. 739–748.
CIKMCIKM-2014-LiuYGS #feedback #graph #pseudo #ranking #recommendation
Meta-Path-Based Ranking with Pseudo Relevance Feedback on Heterogeneous Graph for Citation Recommendation (XL, YY, CG, YS), pp. 121–130.
CIKMCIKM-2014-MahdabiC #mining #network #recommendation #retrieval
Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation (PM, FC), pp. 1659–1668.
CIKMCIKM-2014-MaLG #collaboration #community #recommendation #trust
Improving Recommendation Accuracy by Combining Trust Communities and Collaborative Filtering (XM, HL, ZG), pp. 1951–1954.
CIKMCIKM-2014-NtoutsiSRK #clustering #difference #quote #recommendation
“Strength Lies in Differences”: Diversifying Friends for Recommendations through Subspace Clustering (EN, KS, KR, HPK), pp. 729–738.
CIKMCIKM-2014-ShiKBLH #learning #named #recommendation
CARS2: Learning Context-aware Representations for Context-aware Recommendations (YS, AK, LB, ML, AH), pp. 291–300.
CIKMCIKM-2014-VlachosFMKV #clustering #quality #recommendation
Improving Co-Cluster Quality with Application to Product Recommendations (MV, FF, CM, ATK, VGV), pp. 679–688.
CIKMCIKM-2014-WangGL #modelling #personalisation #recommendation #transaction
Modeling Retail Transaction Data for Personalized Shopping Recommendation (PW, JG, YL), pp. 1979–1982.
CIKMCIKM-2014-WangJDY #adaptation #recommendation #social
User Interests Imbalance Exploration in Social Recommendation: A Fitness Adaptation (TW, XJ, XD, XY), pp. 281–290.
CIKMCIKM-2014-WangPX #collaboration #matrix #named #recommendation
HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation (XW, WP, CX), pp. 769–778.
CIKMCIKM-2014-YangSR #recommendation
Constrained Question Recommendation in MOOCs via Submodularity (DY, JS, CPR), pp. 1987–1990.
CIKMCIKM-2014-YuanCS #graph #recommendation
Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences (QY, GC, AS), pp. 659–668.
CIKMCIKM-2014-ZhengMB #recommendation
Deviation-Based Contextual SLIM Recommenders (YZ, BM, RDB), pp. 271–280.
CIKMCIKM-2014-ZhongPXYM #adaptation #collaboration #learning #recommendation
Adaptive Pairwise Preference Learning for Collaborative Recommendation with Implicit Feedbacks (HZ, WP, CX, ZY, ZM), pp. 1999–2002.
ECIRECIR-2014-BreussT #interactive #learning #recommendation #social #social media
Learning from User Interactions for Recommending Content in Social Media (MB, MT), pp. 598–604.
ECIRECIR-2014-BrilhanteMNPR #named #recommendation
TripBuilder: A Tool for Recommending Sightseeing Tours (IRB, JAFdM, FMN, RP, CR), pp. 771–774.
ECIRECIR-2014-HofmannSBR #bias #evaluation #recommendation
Effects of Position Bias on Click-Based Recommender Evaluation (KH, AS, AB, MdR), pp. 624–630.
ECIRECIR-2014-Lommatzsch #realtime #recommendation #using
Real-Time News Recommendation Using Context-Aware Ensembles (AL), pp. 51–62.
ECIRECIR-2014-RikitianskiiHC #personalisation #recommendation
A Personalised Recommendation System for Context-Aware Suggestions (AR, MH, FC), pp. 63–74.
ECIRECIR-2014-ZhangZWS #network #recommendation
Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks (CZ, XZ, KW, JS), pp. 39–50.
ICPRICPR-2014-DominguesMMSR #information management #recommendation #topic #using
Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems (MAD, MGM, RMM, CVS, SOR), pp. 3606–3611.
ICPRICPR-2014-ManzatoDMR #feedback #personalisation #ranking #recommendation #topic
Improving Personalized Ranking in Recommender Systems with Topic Hierarchies and Implicit Feedback (MGM, MAD, RMM, SOR), pp. 3696–3701.
KDDKDD-2014-AmatriainM #problem #recommendation #tutorial
The recommender problem revisited: morning tutorial (XA, BM), p. 1971.
KDDKDD-2014-DiaoQWSJW #aspect-oriented #modelling #recommendation #sentiment
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (QD, MQ, CYW, AJS, JJ, CW), pp. 193–202.
KDDKDD-2014-JagadeeshPBDS #image #recommendation #scalability #visual notation
Large scale visual recommendations from street fashion images (VJ, RP, AB, WD, NS), pp. 1925–1934.
KDDKDD-2014-LeeLTS #modelling #recommendation #scalability
Modeling impression discounting in large-scale recommender systems (PL, LVSL, MT, SS), pp. 1837–1846.
KDDKDD-2014-LianZXSCR #matrix #modelling #named #recommendation
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (DL, CZ, XX, GS, EC, YR), pp. 831–840.
KDDKDD-2014-LiL #quality #recommendation
Matching users and items across domains to improve the recommendation quality (CYL, SDL), pp. 801–810.
KDDKDD-2014-LuIBL #recommendation #social
Optimal recommendations under attraction, aversion, and social influence (WL, SI, SB, LVSL), pp. 811–820.
KDDKDD-2014-QuZLLX #effectiveness #recommendation
A cost-effective recommender system for taxi drivers (MQ, HZ, JL, GL, HX), pp. 45–54.
KDDKDD-2014-RenLYKGWH #clustering #effectiveness #named #recommendation
ClusCite: effective citation recommendation by information network-based clustering (XR, JL, XY, UK, QG, LW, JH), pp. 821–830.
KDDKDD-2014-TangTL #future of #recommendation #social #social media
Recommendation in social media: recent advances and new frontiers (JT, JT, HL), p. 1977.
KDDKDD-2014-YuanCL #generative #named #recommendation
COM: a generative model for group recommendation (QY, GC, CYL), pp. 163–172.
KDDKDD-2014-ZhaoGHJWL #microblog #recommendation #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.
KDDKDD-2014-ZhuXGC #mobile #privacy #recommendation #security
Mobile app recommendations with security and privacy awareness (HZ, HX, YG, EC), pp. 951–960.
KDIRKDIR-2014-FukumotoSSM #collaboration #recommendation
Incorporating Guest Preferences into Collaborative Filtering for Hotel Recommendation (FF, HS, YS, SM), pp. 22–30.
KDIRKDIR-2014-HasnaMDP #recommendation #sentiment
Sentiment Polarity Extension for Context-Sensitive Recommender Systems (OLH, FCM, MD, RP), pp. 126–137.
KDIRKDIR-2014-MeguebliKDP14a #personalisation #recommendation
Stories Around You — A Two-Stage Personalized News Recommendation (YM, MK, BLD, FP), pp. 473–479.
KDIRKDIR-2014-SaiaBC #modelling #recommendation #semantics
Semantic Coherence-based User Profile Modeling in the Recommender Systems Context (RS, LB, SC), pp. 154–161.
KDIRKDIR-2014-TasciC #recommendation
A Media Tracking and News Recommendation System (ST, IC), pp. 53–60.
KDIRKDIR-2014-UtkuA #mobile #recommendation
A Mobile Location-Aware Recommendation System (SU, CEA), pp. 176–183.
KEODKEOD-2014-AliE #collaboration #recommendation #semantics
Semantic-based Collaborative Filtering for Enhancing Recommendation (GA, AE), pp. 176–185.
KEODKEOD-2014-TarakciC #recommendation #using
Using Hypergraph-based User Profile in a Recommendation System (HT, NKC), pp. 27–35.
KMISKMIS-2014-WangABN #recommendation #semantics #towards
Towards a Recommender System from Semantic Traces for Decision Aid (NW, MHA, JPAB, EN), pp. 274–279.
MLDMMLDM-2014-DzubaB #mining #music #recommendation
Mining Users Playbacks History for Music Recommendations (AD, DB), pp. 422–430.
RecSysRecSys-2014-AdamopoulosT14a #bias #collaboration #on the #probability #recommendation
On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems (PA, AT), pp. 153–160.
RecSysRecSys-2014-Aiolli #feedback #optimisation #recommendation
Convex AUC optimization for top-N recommendation with implicit feedback (FA), pp. 293–296.
RecSysRecSys-2014-Amatriain #problem #recommendation #revisited
The recommender problem revisited (XA), pp. 397–398.
RecSysRecSys-2014-BachrachFGKKNP #recommendation #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.
RecSysRecSys-2014-BadenesBCGHMNPSSXYZ #automation #people #recommendation #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.
RecSysRecSys-2014-Ben-ShimonTFH #as a service #configuration management #monitoring #recommendation
Configuring and monitoring recommender system as a service (DBS, AT, MF, JH), pp. 363–364.
RecSysRecSys-2014-BhagatWIT #learning #matrix #recommendation #using
Recommending with an agenda: active learning of private attributes using matrix factorization (SB, UW, SI, NT), pp. 65–72.
RecSysRecSys-2014-Braunhofer #recommendation
Hybridisation techniques for cold-starting context-aware recommender systems (MB), pp. 405–408.
RecSysRecSys-2014-BraunhoferCR #hybrid #recommendation
Switching hybrid for cold-starting context-aware recommender systems (MB, VC, FR), pp. 349–352.
RecSysRecSys-2014-CantadorC #recommendation #tutorial
Tutorial on cross-domain recommender systems (IC, PC), pp. 401–402.
RecSysRecSys-2014-Christakopoulou #independence #recommendation
Moving beyond linearity and independence in top-N recommender systems (EC), pp. 409–412.
RecSysRecSys-2014-CremonesiQ #question #recommendation
Cross-domain recommendations without overlapping data: myth or reality? (PC, MQ), pp. 297–300.
RecSysRecSys-2014-DalyBKM #multi #recommendation
Multi-criteria journey aware housing recommender system (EMD, AB, AK, RM), pp. 325–328.
RecSysRecSys-2014-DeryKRS #elicitation #recommendation
Preference elicitation for narrowing the recommended list for groups (LND, MK, LR, BS), pp. 333–336.
RecSysRecSys-2014-EkstrandHWK #algorithm #difference #recommendation
User perception of differences in recommender algorithms (MDE, FMH, MCW, JAK), pp. 161–168.
RecSysRecSys-2014-GaoTL #network #personalisation #recommendation #social
Personalized location recommendation on location-based social networks (HG, JT, HL), pp. 399–400.
RecSysRecSys-2014-GarcinFDABH #evaluation #online #recommendation
Offline and online evaluation of news recommender systems at swissinfo.ch (FG, BF, OD, AA, CB, AH), pp. 169–176.
RecSysRecSys-2014-GueyeAN #algorithm #recommendation
A parameter-free algorithm for an optimized tag recommendation list size (MG, TA, HN), pp. 233–240.
RecSysRecSys-2014-GuyG #recommendation #social #tutorial
Social recommender system tutorial (IG, WG), pp. 403–404.
RecSysRecSys-2014-HaririMB #adaptation #interactive #recommendation
Context adaptation in interactive recommender systems (NH, BM, RDB), pp. 41–48.
RecSysRecSys-2014-HarmanOAG #recommendation #trust
Dynamics of human trust in recommender systems (JLH, JO, TFA, CG), pp. 305–308.
RecSysRecSys-2014-JannachF #data mining #mining #modelling #process #recommendation
Recommendation-based modeling support for data mining processes (DJ, SF), pp. 337–340.
RecSysRecSys-2014-KellerR #e-commerce #framework #named #recommendation
Cosibon: an E-commerce like platform enabling bricks-and-mortar stores to use sophisticated product recommender systems (TK, MR), pp. 367–368.
RecSysRecSys-2014-KluverK #behaviour #recommendation
Evaluating recommender behavior for new users (DK, JAK), pp. 121–128.
RecSysRecSys-2014-KrishnanPFG #bias #learning #recommendation #social
A methodology for learning, analyzing, and mitigating social influence bias in recommender systems (SK, JP, MJF, KG), pp. 137–144.
RecSysRecSys-2014-LingLK #approach #recommendation
Ratings meet reviews, a combined approach to recommend (GL, MRL, IK), pp. 105–112.
RecSysRecSys-2014-Liu0L #recommendation
Recommending user generated item lists (YL, MX, LVSL), pp. 185–192.
RecSysRecSys-2014-LiuA #framework #recommendation #towards
Towards a dynamic top-N recommendation framework (XL, KA), pp. 217–224.
RecSysRecSys-2014-LoniS #library #named #recommendation
WrapRec: an easy extension of recommender system libraries (BL, AS), pp. 377–378.
RecSysRecSys-2014-Mayeku #personalisation #recommendation
Enhancing personalization and learner engagement through context-aware recommendation in TEL (BM), pp. 413–415.
RecSysRecSys-2014-Nguyen #lifecycle #recommendation
Improving recommender systems: user roles and lifecycles (TTN), pp. 417–420.
RecSysRecSys-2014-NoiaORTS #analysis #recommendation #towards
An analysis of users’ propensity toward diversity in recommendations (TDN, VCO, JR, PT, EDS), pp. 285–288.
RecSysRecSys-2014-PalovicsBKKF #online #recommendation
Exploiting temporal influence in online recommendation (RP, AAB, LK, TK, EF), pp. 273–280.
RecSysRecSys-2014-PedroK #collaboration #recommendation
Question recommendation for collaborative question answering systems with RankSLDA (JSP, AK), pp. 193–200.
RecSysRecSys-2014-PeraN14a #automation #recommendation
Automating readers’ advisory to make book recommendations for K-12 readers (MSP, YKN), pp. 9–16.
RecSysRecSys-2014-SaidB #benchmark #comparative #evaluation #framework #metric #recommendation
Comparative recommender system evaluation: benchmarking recommendation frameworks (AS, AB), pp. 129–136.
RecSysRecSys-2014-SaidB14a #evaluation #named #recommendation #tool support
Rival: a toolkit to foster reproducibility in recommender system evaluation (AS, AB), pp. 371–372.
RecSysRecSys-2014-SaidDLT #challenge #recommendation
Recommender systems challenge 2014 (AS, SD, BL, DT), pp. 387–388.
RecSysRecSys-2014-SaveskiM #learning #recommendation
Item cold-start recommendations: learning local collective embeddings (MS, AM), pp. 89–96.
RecSysRecSys-2014-SedhainSBXC #collaboration #recommendation #social
Social collaborative filtering for cold-start recommendations (SS, SS, DB, LX, JC), pp. 345–348.
RecSysRecSys-2014-SeminarioW #recommendation
Attacking item-based recommender systems with power items (CES, DCW), pp. 57–64.
RecSysRecSys-2014-SuiB #feedback #online #rank #recommendation
Clinical online recommendation with subgroup rank feedback (YS, JWB), pp. 289–292.
RecSysRecSys-2014-TangJLL #personalisation #recommendation
Ensemble contextual bandits for personalized recommendation (LT, YJ, LL, TL), pp. 73–80.
RecSysRecSys-2014-TrevisiolASJ #graph #recommendation
Cold-start news recommendation with domain-dependent browse graph (MT, LMA, RS, AJ), pp. 81–88.
RecSysRecSys-2014-Vahedian #hybrid #network #recommendation
Weighted hybrid recommendation for heterogeneous networks (FV), pp. 429–432.
RecSysRecSys-2014-VanchinathanNBK #process #recommendation
Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.
RecSysRecSys-2014-VargasBKC #recommendation
Coverage, redundancy and size-awareness in genre diversity for recommender systems (SV, LB, AK, PC), pp. 209–216.
RecSysRecSys-2014-VargasC #recommendation
Improving sales diversity by recommending users to items (SV, PC), pp. 145–152.
RecSysRecSys-2014-WaldnerV #exclamation #game studies #recommendation #timeline #twitter
Emphasize, don’t filter!: displaying recommendations in Twitter timelines (WW, JV), pp. 313–316.
RecSysRecSys-2014-XuPA #predict #ranking #recommendation
Controlled experimentation in recommendations, ranking & response prediction (YX, RP, JA), p. 389.
RecSysRecSys-2014-YangAR #constraints #online #recommendation
Question recommendation with constraints for massive open online courses (DY, DA, CPR), pp. 49–56.
RecSysRecSys-2014-Zhang #recommendation
Browser-oriented universal cross-site recommendation and explanation based on user browsing logs (YZ), pp. 433–436.
RecSysRecSys-2014-Zheng #algorithm #recommendation #similarity
Deviation-based and similarity-based contextual SLIM recommendation algorithms (YZ), pp. 437–440.
RecSysRecSys-2014-ZhengMB #algorithm #named #recommendation
CSLIM: contextual SLIM recommendation algorithms (YZ, BM, RDB), pp. 301–304.
SEKESEKE-2014-TianWHZG #feedback #recommendation #using #web #web service
Cold-Start Web Service Recommendation Using Implicit Feedback (GT, JW, KH, WZ, PG), pp. 371–376.
SIGIRSIGIR-2014-ChengSM #adaptation #music #named #personalisation #recommendation #social
Just-for-me: an adaptive personalization system for location-aware social music recommendation (ZC, JS, TM), pp. 1267–1268.
SIGIRSIGIR-2014-ChengYWL #behaviour #multi #recommendation
Group latent factor model for recommendation with multiple user behaviors (JC, TY, JW, HL), pp. 995–998.
SIGIRSIGIR-2014-ChenJZBZSY #category theory #recommendation
Does product recommendation meet its waterloo in unexplored categories?: no, price comes to help (JC, QJ, SZ, SB, LZ, ZS, YY), pp. 667–676.
SIGIRSIGIR-2014-GrausDTWR #communication #email #enterprise #graph #recommendation #using
Recipient recommendation in enterprises using communication graphs and email content (DG, DvD, MT, WW, MdR), pp. 1079–1082.
SIGIRSIGIR-2014-Ifada #modelling #personalisation #recommendation #topic #using
A tag-based personalized item recommendation system using tensor modeling and topic model approaches (NI), p. 1280.
SIGIRSIGIR-2014-LinSKC #modelling #recommendation
New and improved: modeling versions to improve app recommendation (JL, KS, MYK, TSC), pp. 647–656.
SIGIRSIGIR-2014-LivneGTDA #difference #named #recommendation #using
CiteSight: supporting contextual citation recommendation using differential search (AL, VG, JT, STD, EA), pp. 807–816.
SIGIRSIGIR-2014-LiWM #recommendation #social
A revisit to social network-based recommender systems (HL, DW, NM), pp. 1239–1242.
SIGIRSIGIR-2014-LuLMWXW #interactive #microblog #recommendation #topic
Computing and applying topic-level user interactions in microblog recommendation (XL, PL, HM, SW, AX, BW), pp. 843–846.
SIGIRSIGIR-2014-Ma #on the #recommendation #social
On measuring social friend interest similarities in recommender systems (HM), pp. 465–474.
SIGIRSIGIR-2014-NguyenKB #process #recommendation
Gaussian process factorization machines for context-aware recommendations (TVN, AK, LB), pp. 63–72.
SIGIRSIGIR-2014-RonenGKB #community #recommendation #social #social media
Recommending social media content to community owners (IR, IG, EK, MB), pp. 243–252.
SIGIRSIGIR-2014-SchedlVF #microblog #music #recommendation
User geospatial context for music recommendation in microblogs (MS, AV, KF), pp. 987–990.
SIGIRSIGIR-2014-SedhaiS #hashtag #recommendation #twitter
Hashtag recommendation for hyperlinked tweets (SS, AS), pp. 831–834.
SIGIRSIGIR-2014-TangWZ #recommendation
Cross-language context-aware citation recommendation in scientific articles (XT, XW, XZ), pp. 817–826.
SIGIRSIGIR-2014-Vargas #evaluation #information retrieval #recommendation
Novelty and diversity enhancement and evaluation in recommender systems and information retrieval (SV), p. 1281.
SIGIRSIGIR-2014-YaoHHZ #modelling #recommendation #trust
Modeling dual role preferences for trust-aware recommendation (WY, JH, GH, YZ), pp. 975–978.
SIGIRSIGIR-2014-YaoSNAL #internet #recommendation
Exploring recommendations in internet of things (LY, QZS, AHHN, HA, XL), pp. 855–858.
SIGIRSIGIR-2014-ZhangL0ZLM #analysis #modelling #recommendation #sentiment
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis (YZ, GL, MZ, YZ, YL, SM), pp. 83–92.
SIGIRSIGIR-2014-ZhangTZX #algorithm #recommendation
Addressing cold start in recommender systems: a semi-supervised co-training algorithm (MZ, JT, XZ, XX), pp. 73–82.
SIGIRSIGIR-2014-ZhangWRS #performance #recommendation
Preference preserving hashing for efficient recommendation (ZZ, QW, LR, LS), pp. 183–192.
SIGIRSIGIR-2014-ZhouKWAD #detection #recommendation
Detection of abnormal profiles on group attacks in recommender systems (WZ, YSK, JW, SA, GD), pp. 955–958.
SIGIRSIGIR-2014-ZhuHLT #recommendation
Bundle recommendation in ecommerce (TZ, PH, JL, LT), pp. 657–666.
SKYSKY-2014-ExmanN #network #performance #recommendation #social
Location-based Fast Recommendation Social Network (IE, EN), pp. 55–62.
SACSAC-2014-ChenCWD #recommendation #scalability
Instant expert hunting: building an answerer recommender system for a large scale Q&A website (TC, JC, HW, YD), pp. 260–265.
SACSAC-2014-ChenZTWS #modelling #recommendation
Comparing the staples in latent factor models for recommender systems (CC, LZ, AT, KW, SV), pp. 91–96.
SACSAC-2014-GuoZTBY #empirical #recommendation #trust
From ratings to trust: an empirical study of implicit trust in recommender systems (GG, JZ, DT, AB, NYS), pp. 248–253.
SACSAC-2014-HongHKK #mobile #music #recommendation #smarttech
Context-aware music recommendation in mobile smart devices (JH, WSH, JHK, SWK), pp. 1463–1468.
SACSAC-2014-LiuMHHSC #algorithm #hybrid #recommendation #twitter
A hybrid algorithm for recommendation twitter peers (JNKL, ZM, YXH, YLH, SCKS, VWSC), pp. 644–649.
SACSAC-2014-NgoPLS #image #named #query #recommendation
Recommend-Me: recommending query regions for image search (TDN, SP, DDL, SS), pp. 913–918.
SACSAC-2014-RolimBCCAPM #approach #multimodal #recommendation
A recommendation approach for digital TV systems based on multimodal features (RR, FB, AC, GC, HOdA, AP, AFM), pp. 289–291.
SACSAC-2014-ShangHHCK #personalisation #recommendation #towards
Beyond personalization and anonymity: towards a group-based recommender system (SS, YH, PH, PC, SRK), pp. 266–273.
SACSAC-2014-UnoI #music #named #recommendation
MALL: a life log based music recommendation system and portable music player (AU, TI), pp. 939–944.
SACSAC-2014-WangMLG #recommendation #social
Recommendation based on weighted social trusts and item relationships (DW, JM, TL, LG), pp. 254–259.
SACSAC-2014-YangZL #algorithm #debugging #developer #effectiveness #multi #recommendation
Utilizing a multi-developer network-based developer recommendation algorithm to fix bugs effectively (GY, TZ, BL), pp. 1134–1139.
SACSAC-2014-ZhengBM #empirical #recommendation
Splitting approaches for context-aware recommendation: an empirical study (YZ, RDB, BM), pp. 274–279.
ASEASE-2013-ThungWLL #api #automation #feature model #recommendation
Automatic recommendation of API methods from feature requests (FT, SW, DL, JLL), pp. 290–300.
HTHT-2013-WuCH #recommendation #using
Using personality to adjust diversity in recommender systems (WW, LC, LH), pp. 225–229.
HTHT-2013-YangZYW #personalisation #recommendation #sentiment
A sentiment-enhanced personalized location recommendation system (DY, DZ, ZY, ZW), pp. 119–128.
SIGMODSIGMOD-2013-VartakM #named #recommendation
CHIC: a combination-based recommendation system (MV, SM), pp. 981–984.
VLDBVLDB-2013-ChenYYC #named #recommendation #twitter
TeRec: A Temporal Recommender System Over Tweet Stream (CC, HY, JY, BC), pp. 1254–1257.
VLDBVLDB-2013-SarwatAM #database #recommendation #relational
A RecDB in Action: Recommendation Made Easy in Relational Databases (MS, JLA, MFM), pp. 1242–1245.
ITiCSEITiCSE-WGR-2013-ShumbaFSTFTSABH #recommendation #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.
MSRMSR-2013-NaguibNBH #debugging #process #recommendation #using
Bug report assignee recommendation using activity profiles (HN, NN, BB, DH), pp. 22–30.
MSRMSR-2013-ShokripourAKZ #debugging #recommendation #why
Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendation (RS, JA, ZMK, SZ), pp. 2–11.
MSRMSR-2013-XiaLWZ #recommendation
Tag recommendation in software information sites (XX, DL, XW, BZ), pp. 287–296.
WCREWCRE-2013-SalesTMV #dependence #recommendation #refactoring #set #using
Recommending Move Method refactorings using dependency sets (VS, RT, LFM, MTV), pp. 232–241.
WCREWCRE-2013-ThungLL #automation #library #recommendation
Automated library recommendation (FT, DL, JLL), pp. 182–191.
WCREWCRE-2013-XiaLWZ #debugging #developer #recommendation
Accurate developer recommendation for bug resolution (XX, DL, XW, BZ), pp. 72–81.
ICALPICALP-v2-2013-BachrachP #big data #performance #pseudo #recommendation #sketching #using
Sketching for Big Data Recommender Systems Using Fast Pseudo-random Fingerprints (YB, EP), pp. 459–471.
CSCWCSCW-2013-SaidFJA #algorithm #collaboration #evaluation #recommendation
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm (AS, BF, BJJ, SA), pp. 1399–1408.
HCIHCI-AS-2013-BitontoLRR #collaboration #process #recommendation
Recommendation of Collaborative Activities in E-learning Environments (PDB, ML, TR, VR), pp. 484–492.
HCIHCI-UC-2013-Baguma #design #mobile #recommendation
Mobile Money Services in Uganda: Design Gaps and Recommendations (RB), pp. 249–258.
HCIHCI-UC-2013-BelliniBNP #network #recommendation
A Static and Dynamic Recommendations System for Best Practice Networks (PB, IB, PN, MP), pp. 259–268.
HCIHIMI-D-2013-IsogaiN #modelling #motivation #music #recommendation
Modeling of Music Recommendation Methods to Promote the User’s Singing Motivation — For Next-Generation Japanese Karaoke Systems (SI, MN), pp. 439–448.
HCIHIMI-D-2013-ShigeyoshiTSTIU #empirical #energy #recommendation #social
Social Experiment on Advisory Recommender System for Energy-Saving (HS, KT, RS, HT, SI, TU), pp. 545–554.
HCIHIMI-LCCB-2013-ShiTS #consistency #nondeterminism #online #recommendation
Timing and Basis of Online Product Recommendation: The Preference Inconsistency Paradox (AS, CHT, CLS), pp. 531–539.
HCIHIMI-LCCB-2013-WuN #interactive #process #recommendation
Integrating the Anchoring Process with Preference Stability for Interactive Movie Recommendations (ICW, YFN), pp. 639–648.
HCIOCSC-2013-Popescu13b #problem #recommendation
Group Recommender Systems as a Voting Problem (GP), pp. 412–421.
ICEISICEIS-v1-2013-Al-ShamriA #collaboration #correlation #recommendation
Fuzzy-weighted Pearson Correlation Coefficient for Collaborative Recommender Systems (MYHAS, NHAA), pp. 409–414.
ICEISICEIS-v1-2013-Kozmina #recommendation
Adding Recommendations to OLAP Reporting Tool (NK), pp. 169–176.
ICEISICEIS-v2-2013-DjuanaXLJC #ontology #problem #recommendation
An Ontology-based Method for Sparsity Problem in Tag Recommendation (ED, YX, YL, AJ, CC), pp. 467–474.
ICEISICEIS-v2-2013-KuCY #approach #recommendation
Effect of Product Type and Recommendation Approach on Consumers’ Intention to Purchase Recommended Products (YCK, CHC, CSY), pp. 475–480.
ICEISICEIS-v2-2013-PanfilenkoHEL #architecture #model transformation #recommendation
Model Transformation Recommendations for Service-Oriented Architectures (DVP, KH, BE, EL), pp. 248–256.
ICEISICEIS-v2-2013-SierraCVCV #education #re-engineering #recommendation
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.
ICEISICEIS-v2-2013-ZhangWSP #linked data #network #open data #recommendation #social
Event Recommendation in Social Networks with Linked Data Enablement (YZ, HW, VSS, VKP), pp. 371–379.
CIKMCIKM-2013-FerenceYL #network #recommendation #social
Location recommendation for out-of-town users in location-based social networks (GF, MY, WCL), pp. 721–726.
CIKMCIKM-2013-LiuLAM #mining #personalisation #recommendation
Personalized point-of-interest recommendation by mining users’ preference transition (XL, YL, KA, CM), pp. 733–738.
CIKMCIKM-2013-LiYZ #recommendation
Scientific articles recommendation (YL, MY, Z(Z), pp. 1147–1156.
CIKMCIKM-2013-ThostVS #query #recommendation
Query matching for report recommendation (VT, KV, DS), pp. 1391–1400.
CIKMCIKM-2013-ZhaoLHCH #network #recommendation #social
Community-based user recommendation in uni-directional social networks (GZ, MLL, WH, WC, HH), pp. 189–198.
ECIRECIR-2013-BelemMAG #recommendation
Exploiting Novelty and Diversity in Tag Recommendation (FB, EFM, JMA, MAG), pp. 380–391.
ECIRECIR-2013-Cleger-TamayoFHT #predict #quality #recommendation
Being Confident about the Quality of the Predictions in Recommender Systems (SCT, JMFL, JFH, NT), pp. 411–422.
ECIRECIR-2013-McParlaneMW #detection #recommendation #semantics
Detecting Friday Night Party Photos: Semantics for Tag Recommendation (PJM, YM, IW), pp. 756–759.
ECIRECIR-2013-ZhuGCLN #graph #query #recommendation
Recommending High Utility Query via Session-Flow Graph (XZ, JG, XC, YL, WN), pp. 642–655.
KDDKDD-2013-ChenHL #multi #recommendation
Making recommendations from multiple domains (WC, WH, MLL), pp. 892–900.
KDDKDD-2013-KabburNK #modelling #named #recommendation #similarity
FISM: factored item similarity models for top-N recommender systems (SK, XN, GK), pp. 659–667.
KDDKDD-2013-LiuFYX #learning #recommendation
Learning geographical preferences for point-of-interest recommendation (BL, YF, ZY, HX), pp. 1043–1051.
KDDKDD-2013-NiemannW #approach #collaboration #recommendation
A new collaborative filtering approach for increasing the aggregate diversity of recommender systems (KN, MW), pp. 955–963.
KDDKDD-2013-YinLLW #perspective #recommendation
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective (PY, PL, WCL, MW), pp. 989–997.
KDDKDD-2013-YinSCHC #named #recommendation
LCARS: a location-content-aware recommender system (HY, YS, BC, ZH, LC), pp. 221–229.
KDDKDD-2013-ZhangWF #recommendation
Combining latent factor model with location features for event-based group recommendation (WZ, JW, WF), pp. 910–918.
KDIRKDIR-KMIS-2013-BerkaniN #collaboration #learning #recommendation #semantics
Semantic Collaborative Filtering for Learning Objects Recommendation (LB, ON), pp. 52–63.
KDIRKDIR-KMIS-2013-NartTF #automation #personalisation #recommendation #using
Personalized Recommendation and Explanation by using Keyphrases Automatically extracted from Scientific Literature (DDN, CT, FF), pp. 96–103.
KDIRKDIR-KMIS-2013-OliveiraOC #process #recommendation #student
Recommending the Right Activities based on the Needs of each Student (EO, MGdO, PMC), pp. 183–190.
MLDMMLDM-2013-ChungJKL #identification #personalisation #recommendation
Personalized Expert-Based Recommender System: Training C-SVM for Personalized Expert Identification (YC, HWJ, JK, JHL), pp. 434–441.
RecSysRecSys-2013-Adamopoulos #predict #rating #recommendation
Beyond rating prediction accuracy: on new perspectives in recommender systems (PA), pp. 459–462.
RecSysRecSys-2013-AdamopoulosT #collaboration #predict #recommendation #using
Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems (PA, AT), pp. 351–354.
RecSysRecSys-2013-AharonABLABLRS #named #online #persistent #recommendation #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.
RecSysRecSys-2013-AhnPLL #graph #recommendation
A heterogeneous graph-based recommendation simulator (YA, SP, SL, SgL), pp. 471–472.
RecSysRecSys-2013-Aiolli #dataset #performance #recommendation #scalability
Efficient top-n recommendation for very large scale binary rated datasets (FA), pp. 273–280.
RecSysRecSys-2013-AlanaziB #markov #modelling #recommendation #using
A people-to-people content-based reciprocal recommender using hidden markov models (AA, MB), pp. 303–306.
RecSysRecSys-2013-AzariaHKEWN #recommendation
Movie recommender system for profit maximization (AA, AH, SK, AE, OW, IN), pp. 121–128.
RecSysRecSys-2013-BabasCT #personalisation #recommendation #what
You are what you consume: a bayesian method for personalized recommendations (KB, GC, ET), pp. 221–228.
RecSysRecSys-2013-BelemSAG #recommendation #topic
Topic diversity in tag recommendation (FB, RLTS, JMA, MAG), pp. 141–148.
RecSysRecSys-2013-Ben-Shimon #algorithm #recommendation
Anytime algorithms for top-N recommenders (DBS), pp. 463–466.
RecSysRecSys-2013-BlancoR #feedback #recommendation
Acquiring user profiles from implicit feedback in a conversational recommender system (HB, FR), pp. 307–310.
RecSysRecSys-2013-BlankRS #graph #keyword #recommendation
Leveraging the citation graph to recommend keywords (IB, LR, GS), pp. 359–362.
RecSysRecSys-2013-BugaychenkoD #network #personalisation #recommendation #social
Musical recommendations and personalization in a social network (DB, AD), pp. 367–370.
RecSysRecSys-2013-ChowJKS #data analysis #difference #recommendation
Differential data analysis for recommender systems (RC, HJ, BPK, GS), pp. 323–326.
RecSysRecSys-2013-CremonesiGQ #recommendation
Evaluating top-n recommendations “when the best are gone” (PC, FG, MQ), pp. 339–342.
RecSysRecSys-2013-DoerfelJ #analysis #evaluation #recommendation
An analysis of tag-recommender evaluation procedures (SD, RJ), pp. 343–346.
RecSysRecSys-2013-DongOSMS #recommendation #sentiment
Sentimental product recommendation (RD, MPO, MS, KM, BS), pp. 411–414.
RecSysRecSys-2013-Dooms #generative #hybrid #personalisation #recommendation
Dynamic generation of personalized hybrid recommender systems (SD), pp. 443–446.
RecSysRecSys-2013-DzyaburaT #how #recommendation
Not by search alone: how recommendations complement search results (DD, AT), pp. 371–374.
RecSysRecSys-2013-Ester #network #recommendation #social
Recommendation in social networks (ME), pp. 491–492.
RecSysRecSys-2013-GaoTHL #network #recommendation #social
Exploring temporal effects for location recommendation on location-based social networks (HG, JT, XH, HL), pp. 93–100.
RecSysRecSys-2013-GarcinDF #personalisation #recommendation
Personalized news recommendation with context trees (FG, CD, BF), pp. 105–112.
RecSysRecSys-2013-GarcinF #framework #personalisation #recommendation
PEN RecSys: a personalized news recommender systems framework (FG, BF), pp. 469–470.
RecSysRecSys-2013-GraschFR #interactive #named #recommendation #speech #towards
ReComment: towards critiquing-based recommendation with speech interaction (PG, AF, FR), pp. 157–164.
RecSysRecSys-2013-Guo #recommendation #similarity #trust
Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems (GG), pp. 451–454.
RecSysRecSys-2013-GuoZTY #e-commerce #recommendation
Prior ratings: a new information source for recommender systems in e-commerce (GG, JZ, DT, NYS), pp. 383–386.
RecSysRecSys-2013-HammarKN #e-commerce #recommendation #using
Using maximum coverage to optimize recommendation systems in e-commerce (MH, RK, BJN), pp. 265–272.
RecSysRecSys-2013-HaririMB #recommendation
Query-driven context aware recommendation (NH, BM, RDB), pp. 9–16.
RecSysRecSys-2013-HuE #modelling #online #recommendation #social #social media #topic
Spatial topic modeling in online social media for location recommendation (BH, ME), pp. 25–32.
RecSysRecSys-2013-HuY #learning #process #recommendation
Interview process learning for top-n recommendation (FH, YY), pp. 331–334.
RecSysRecSys-2013-KaminskasRS #hybrid #music #recommendation #using
Location-aware music recommendation using auto-tagging and hybrid matching (MK, FR, MS), pp. 17–24.
RecSysRecSys-2013-KaratzoglouBS #learning #rank #recommendation
Learning to rank for recommender systems (AK, LB, YS), pp. 493–494.
RecSysRecSys-2013-KhroufT #hybrid #linked data #open data #recommendation #using
Hybrid event recommendation using linked data and user diversity (HK, RT), pp. 185–192.
RecSysRecSys-2013-KoenigsteinK #recommendation #scalability #towards
Towards scalable and accurate item-oriented recommendations (NK, YK), pp. 419–422.
RecSysRecSys-2013-KoenigsteinP #embedded #feature model #matrix #recommendation
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection (NK, UP), pp. 129–136.
RecSysRecSys-2013-KrestelS #recommendation #topic
Recommending patents based on latent topics (RK, PS), pp. 395–398.
RecSysRecSys-2013-LacerdaVZ #interactive #recommendation
Exploratory and interactive daily deals recommendation (AL, AV, NZ), pp. 439–442.
RecSysRecSys-2013-MouraoRKM #hybrid #recommendation
Exploiting non-content preference attributes through hybrid recommendation method (FM, LCdR, JAK, WMJ), pp. 177–184.
RecSysRecSys-2013-NewellM #design #evaluation #recommendation
Design and evaluation of a client-side recommender system (CN, LM), pp. 473–474.
RecSysRecSys-2013-NguyenKWHEWR #experience #rating #recommendation #user interface
Rating support interfaces to improve user experience and recommender accuracy (TTN, DK, TYW, PMH, MDE, MCW, JR), pp. 149–156.
RecSysRecSys-2013-OstuniNSM #feedback #linked data #open data #recommendation
Top-N recommendations from implicit feedback leveraging linked open data (VCO, TDN, EDS, RM), pp. 85–92.
RecSysRecSys-2013-PanCCY #personalisation #recommendation #social
Diffusion-aware personalized social update recommendation (YP, FC, KC, YY), pp. 69–76.
RecSysRecSys-2013-PeraN #personalisation #recommendation #what
What to read next?: making personalized book recommendations for K-12 users (MSP, YKN), pp. 113–120.
RecSysRecSys-2013-PessemierDM13a #recommendation
A food recommender for patients in a care facility (TDP, SD, LM), pp. 209–212.
RecSysRecSys-2013-PizzatoB #network #people #recommendation #social
Beyond friendship: the art, science and applications of recommending people to people in social networks (LAP, AB), pp. 495–496.
RecSysRecSys-2013-PuF #comprehension #matrix #recommendation #relational
Understanding and improving relational matrix factorization in recommender systems (LP, BF), pp. 41–48.
RecSysRecSys-2013-RonenKZN #collaboration #recommendation
Selecting content-based features for collaborative filtering recommenders (RR, NK, EZ, NN), pp. 407–410.
RecSysRecSys-2013-RonenKZSYH #named #recommendation
Sage: recommender engine as a cloud service (RR, NK, EZ, MS, RY, NHW), pp. 475–476.
RecSysRecSys-2013-SaayaRSS #challenge #recommendation #web
The curated web: a recommendation challenge (ZS, RR, MS, BS), pp. 101–104.
RecSysRecSys-2013-SavirBS #recommendation
Recommending improved configurations for complex objects with an application in travel planning (AS, RIB, GS), pp. 391–394.
RecSysRecSys-2013-Seminario #collaboration #recommendation #robust
Accuracy and robustness impacts of power user attacks on collaborative recommender systems (CES), pp. 447–450.
RecSysRecSys-2013-SharmaY #community #learning #recommendation
Pairwise learning in recommendation: experiments with community recommendation on linkedin (AS, BY), pp. 193–200.
RecSysRecSys-2013-Shi #approach #graph #recommendation #similarity
Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach (LS), pp. 57–64.
RecSysRecSys-2013-SilbermannBR #recommendation
Sample selection for MCMC-based recommender systems (TS, IB, SR), pp. 403–406.
RecSysRecSys-2013-Steck #evaluation #predict #ranking #recommendation
Evaluation of recommendations: rating-prediction and ranking (HS), pp. 213–220.
RecSysRecSys-2013-SuYCY #personalisation #ranking #recommendation
Set-oriented personalized ranking for diversified top-n recommendation (RS, LY, KC, YY), pp. 415–418.
RecSysRecSys-2013-TaghaviBS #recommendation
Agent-based computational investing recommender system (MT, KB, ES), pp. 455–458.
RecSysRecSys-2013-TianJ #graph #recommendation #using
Recommending scientific articles using bi-relational graph-based iterative RWR (GT, LJ), pp. 399–402.
RecSysRecSys-2013-VahabiALBL #orthogonal #query #recommendation
Orthogonal query recommendation (HV, MA, DL, RABY, ALO), pp. 33–40.
RecSysRecSys-2013-WangHZL #collaboration #multi #on the fly #online #recommendation
Online multi-task collaborative filtering for on-the-fly recommender systems (JW, SCHH, PZ, ZL), pp. 237–244.
RecSysRecSys-2013-WestonYW #learning #rank #recommendation #statistics
Learning to rank recommendations with the k-order statistic loss (JW, HY, RJW), pp. 245–248.
RecSysRecSys-2013-WilsonS #collaboration #recommendation
When power users attack: assessing impacts in collaborative recommender systems (DCW, CES), pp. 427–430.
RecSysRecSys-2013-WuLCHLCH #online #personalisation #recommendation
Personalized next-song recommendation in online karaokes (XW, QL, EC, LH, JL, CC, GH), pp. 137–140.
RecSysRecSys-2013-XuBATMK #recommendation
Catch-up TV recommendations: show old favourites and find new ones (MX, SB, SA, ST, AM, IK), pp. 285–294.
RecSysRecSys-2013-YuRSSKGNH #feedback #network #recommendation
Recommendation in heterogeneous information networks with implicit user feedback (XY, XR, YS, BS, UK, QG, BN, JH), pp. 347–350.
RecSysRecSys-2013-ZhangP #recommendation #social #social media
Recommending branded products from social media (YZ, MP), pp. 77–84.
RecSysRecSys-2013-ZhangSKH #artificial reality #recommendation #using
Improving augmented reality using recommender systems (ZZ, SS, SRK, PH), pp. 173–176.
SEKESEKE-2013-DuHCLH #incremental #named #personalisation #recommendation
ABEY: an Incremental Personalized Method Based on Attribute Entropy for Recommender Systems (S) (XD, TH, ZC, JL, CH), pp. 318–321.
SEKESEKE-2013-WangWTZ #evaluation #named #network #recommendation #trust
STERS: A System for Service Trustworthiness Evaluation and Recommendation based on the Trust Network (S) (YW, JW, YT, JZ), pp. 322–325.
SIGIRSIGIR-2013-BalogR #classification #cumulative #ranking #recommendation
Cumulative citation recommendation: classification vs. ranking (KB, HR), pp. 941–944.
SIGIRSIGIR-2013-Belem #recommendation
Beyond relevance: on novelty and diversity in tag recommendation (FB), p. 1140.
SIGIRSIGIR-2013-ChenHL #modelling #recommendation
Modeling user’s receptiveness over time for recommendation (WC, WH, MLL), pp. 373–382.
SIGIRSIGIR-2013-ChenHL13a #feedback #recommendation
Tagcloud-based explanation with feedback for recommender systems (WC, WH, MLL), pp. 945–948.
SIGIRSIGIR-2013-FanLGLC #collaboration #recommendation
Collaborative factorization for recommender systems (CF, YL, JG, ZL, XC), pp. 949–953.
SIGIRSIGIR-2013-FeildA #query #recommendation
Task-aware query recommendation (HAF, JA), pp. 83–92.
SIGIRSIGIR-2013-KarkaliPV #realtime #recommendation
Match the news: a firefox extension for real-time news recommendation (MK, DP, MV), pp. 1117–1118.
SIGIRSIGIR-2013-LinSKC #modelling #recommendation #twitter
Addressing cold-start in app recommendation: latent user models constructed from twitter followers (JL, KS, MYK, TSC), pp. 283–292.
SIGIRSIGIR-2013-LukeSM #framework #recommendation
A framework for specific term recommendation systems (TL, PS, PM), pp. 1093–1094.
SIGIRSIGIR-2013-Ma #case study #recommendation #social
An experimental study on implicit social recommendation (HM), pp. 73–82.
SIGIRSIGIR-2013-McParlaneMJ #on the #recommendation
On contextual photo tag recommendation (PJM, YM, JMJ), pp. 965–968.
SIGIRSIGIR-2013-RoitmanCME #modelling #recommendation
Modeling the uniqueness of the user preferences for recommendation systems (HR, DC, YM, IE), pp. 777–780.
SIGIRSIGIR-2013-SappelliVK #personalisation #recommendation #using
Recommending personalized touristic sights using google places (MS, SV, WK), pp. 781–784.
SIGIRSIGIR-2013-SchallerHE #distributed #recommendation #visitor
RecSys for distributed events: investigating the influence of recommendations on visitor plans (RS, MH, DE), pp. 953–956.
SIGIRSIGIR-2013-SchedlS #hybrid #music #recommendation #retrieval
Hybrid retrieval approaches to geospatial music recommendation (MS, DS), pp. 793–796.
SIGIRSIGIR-2013-ShenWYC #multi #recommendation
Multimedia recommendation: technology and techniques (JS, MW, SY, PC), p. 1131.
SIGIRSIGIR-2013-ShouML00H #recommendation
Competence-based song recommendation (LS, KM, XL, KC, GC, TH), pp. 423–432.
SIGIRSIGIR-2013-SonKP #analysis #locality #recommendation #semantics
A location-based news article recommendation with explicit localized semantic analysis (JWS, AYK, SBP), pp. 293–302.
SIGIRSIGIR-2013-Wang0 #e-commerce #recommendation
Opportunity model for e-commerce recommendation: right product; right time (JW, YZ), pp. 303–312.
SIGIRSIGIR-2013-WanLGFC #recommendation #social #social media
Informational friend recommendation in social media (SW, YL, JG, CF, XC), pp. 1045–1048.
SIGIRSIGIR-2013-YuanCMSM #recommendation
Time-aware point-of-interest recommendation (QY, GC, ZM, AS, NMT), pp. 363–372.
SKYSKY-2013-ExmanK #anti #network #recommendation #social
An Anti-Turing Test: Social Network Friends’ Recommendations (IE, AK), pp. 55–61.
SKYSKY-2013-Gomes #how #ontology #recommendation #representation #using
Representing Knowledge using Ontologies: How Search, Browse and Recommendation Can Be Performed (PG), pp. 1–3.
MODELSMoDELS-2013-KuschkeMR #modelling #process #recommendation
Recommending Auto-completions for Software Modeling Activities (TK, PM, PR), pp. 170–186.
MODELSMoDELS-2013-KuschkeMR #modelling #process #recommendation
Recommending Auto-completions for Software Modeling Activities (TK, PM, PR), pp. 170–186.
SACSAC-2013-BlancoR #query #recommendation
Inferring user utility for query revision recommendation (HB, FR), pp. 245–252.
SACSAC-2013-CapelleHHF #recommendation #semantics #using
Semantic news recommendation using wordnet and bing similarities (MC, FH, AH, FF), pp. 296–302.
SACSAC-2013-CeccarelliGLNP #query #recommendation #semantics
When entities meet query recommender systems: semantic search shortcuts (DC, SG, CL, FMN, RP), pp. 933–938.
SACSAC-2013-ChenNKX #recommendation
Users segmentations for recommendation (LC, RN, SK, YX), pp. 279–280.
SACSAC-2013-HayashiIN #recommendation #visual notation
A visual analytics tool for system logs adopting variable recommendation and feature-based filtering (AH, TI, SN), pp. 996–998.
SACSAC-2013-KoutrouliT #recommendation
Credible recommendation exchange mechanism for P2P reputation systems (EK, AT), pp. 1943–1948.
SACSAC-2013-LeeKP #hybrid #recommendation
A tour recommendation service for electric vehicles based on a hybrid orienteering model (JL, SWK, GLP), pp. 1652–1654.
SACSAC-2013-LommatzschKA #hybrid #learning #modelling #recommendation #semantics
Learning hybrid recommender models for heterogeneous semantic data (AL, BK, SA), pp. 275–276.
SACSAC-2013-Manzato #feedback #metadata #recommendation
gSVD++: supporting implicit feedback on recommender systems with metadata awareness (MGM), pp. 908–913.
SACSAC-2013-RokachSSCS #recommendation
Recommending insurance riders (LR, GS, BS, EC, GS), pp. 253–260.
SACSAC-2013-ZengC #data fusion #matrix #recommendation #semistructured data
Heterogeneous data fusion via matrix factorization for augmenting item, group and friend recommendations (WZ, LC), pp. 237–244.
SACSAC-2013-ZhangL #algorithm #debugging #developer #hybrid #recommendation
A hybrid bug triage algorithm for developer recommendation (TZ, BL), pp. 1088–1094.
ICSEICSE-2013-Balachandran #automation #code review #quality #recommendation #static analysis #using
Reducing human effort and improving quality in peer code reviews using automatic static analysis and reviewer recommendation (VB), pp. 931–940.
ICSEICSE-2013-LeeKS #named #recommendation #visual notation
NavClus: a graphical recommender for assisting code exploration (SL, SK, MS), pp. 1315–1318.
ICSEICSE-2013-SawadskyMJ #named #recommendation #web
Reverb: recommending code-related web pages (NS, GCM, RJ), pp. 812–821.
HTHT-2012-DellschaftS #quality #recommendation
Measuring the influence of tag recommenders on the indexing quality in tagging systems (KD, SS), pp. 73–82.
PODSPODS-2012-DengFG #complexity #on the #problem #recommendation
On the complexity of package recommendation problems (TD, WF, FG), pp. 261–272.
SIGMODSIGMOD-2012-BarbosaMLO #named #network #recommendation #visualisation #web
VRRC: web based tool for visualization and recommendation on co-authorship network (abstract only) (EMB, MMM, GRL, JPMdO), p. 865.
SIGMODSIGMOD-2012-PavlidisMCBBRYMHR #recommendation #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.
VLDBVLDB-2012-KanagalAPJYP #behaviour #learning #recommendation #taxonomy #using
Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior (BK, AA, SP, VJ, JY, LGP), pp. 956–967.
VLDBVLDB-2012-YinCLYC #recommendation
Challenging the Long Tail Recommendation (HY, BC, JL, JY, CC), pp. 896–907.
CSMRCSMR-2012-HeinemannBHH #api #recommendation
Identifier-Based Context-Dependent API Method Recommendation (LH, VB, MH, BH), pp. 31–40.
CSMRCSMR-2012-TerraVCB #architecture #recommendation #refactoring
Recommending Refactorings to Reverse Software Architecture Erosion (RT, MTV, KC, RdSB), pp. 335–340.
WCREWCRE-2012-SteidlHJ #analysis #network #recommendation #using
Using Network Analysis for Recommendation of Central Software Classes (DS, BH, EJ), pp. 93–102.
CHICHI-2012-BauerCGSWWK #mobile #named #recommendation
ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display (JSB, SC, BG, JWS, EW, NFW, JAK), pp. 1401–1410.
CHICHI-2012-NowakN #behaviour #monitoring #online #recommendation
Effects of behavior monitoring and perceived system benefit in online recommender systems (MN, CN), pp. 2243–2246.
CHICHI-2012-OganWBRCLC #case study #collaboration #design #recommendation
Collaboration in cognitive tutor use in latin America: field study and design recommendations (AO, EW, RSJdB, GRM, MJC, TL, AMJBdC), pp. 1381–1390.
CHICHI-2012-PiorkowskiFSBBJBS #empirical #information management #recommendation
Reactive information foraging: an empirical investigation of theory-based recommender systems for programmers (DP, SDF, CS, CB, MMB, BEJ, RKEB, CS), pp. 1471–1480.
CSCWCSCW-2012-PriedhorskyPST #algorithm #evaluation #personalisation #recommendation
Recommending routes in the context of bicycling: algorithms, evaluation, and the value of personalization (RP, DP, SS, LGT), pp. 979–988.
ICEISICEIS-J-2012-GeJG #analysis #recommendation
Bringing Diversity to Recommendation Lists — An Analysis of the Placement of Diverse Items (MG, DJ, FG), pp. 293–305.
ICEISICEIS-v2-2012-GeJGH #recommendation
Effects of the Placement of Diverse Items in Recommendation Lists (MG, DJ, FG, MH), pp. 201–208.
CIKMCIKM-2012-AntonellisSD #recommendation
Dynamic covering for recommendation systems (IA, ADS, SD), pp. 26–34.
CIKMCIKM-2012-BaeK #classification #effectiveness #recommendation
An effective category classification method based on a language model for question category recommendation on a cQA service (KB, YK), pp. 2255–2258.
CIKMCIKM-2012-BambaSGBF #concept #recommendation #scalability #using
The twitaholic next door.: scalable friend recommender system using a concept-sensitive hash function (PB, JS, CG, NB, JF), pp. 2275–2278.
CIKMCIKM-2012-BlancoCLPS #exclamation #recommendation #why
You should read this! let me explain you why: explaining news recommendations to users (RB, DC, CL, RP, FS), pp. 1995–1999.
CIKMCIKM-2012-CaoYDWW #graph #modelling #process #recommendation #workflow
Graph-based workflow recommendation: on improving business process modeling (BC, JY, SD, DW, ZW), pp. 1527–1531.
CIKMCIKM-2012-CheungSM #recommendation #social #synthesis #using
Using program synthesis for social recommendations (AC, ASL, SM), pp. 1732–1736.
CIKMCIKM-2012-Diaz-AvilesDGSN #online #recommendation #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.
CIKMCIKM-2012-DuttingHW #recommendation #trust
Maximizing revenue from strategic recommendations under decaying trust (PD, MH, IW), pp. 2283–2286.
CIKMCIKM-2012-FangS #approach #feedback #learning #recommendation
A latent pairwise preference learning approach for recommendation from implicit feedback (YF, LS), pp. 2567–2570.
CIKMCIKM-2012-GaoZLH #clustering #recommendation #twitter
Twitter hyperlink recommendation with user-tweet-hyperlink three-way clustering (DG, RZ, WL, YH), pp. 2535–2538.
CIKMCIKM-2012-GuoMCJ #learning #recommendation #social
Learning to recommend with social relation ensemble (LG, JM, ZC, HJ), pp. 2599–2602.
CIKMCIKM-2012-HaKKFP #recommendation
Top-N recommendation through belief propagation (JH, SHK, SWK, CF, SP), pp. 2343–2346.
CIKMCIKM-2012-HuangKCMGR #recommendation
Recommending citations: translating papers into references (WH, SK, CC, PM, CLG, LR), pp. 1910–1914.
CIKMCIKM-2012-HwangLKL #on the #performance #recommendation #using
On using category experts for improving the performance and accuracy in recommender systems (WSH, HJL, SWK, ML), pp. 2355–2358.
CIKMCIKM-2012-JiangCLYWZY #recommendation #social
Social contextual recommendation (MJ, PC, RL, QY, FW, WZ, SY), pp. 45–54.
CIKMCIKM-2012-JiangCWYZY #multi #recommendation #relational #social
Social recommendation across multiple relational domains (MJ, PC, FW, QY, WZ, SY), pp. 1422–1431.
CIKMCIKM-2012-KaratzoglouBCB #mobile #recommendation
Climbing the app wall: enabling mobile app discovery through context-aware recommendations (AK, LB, KC, MB), pp. 2527–2530.
CIKMCIKM-2012-KoenigsteinRS #framework #matrix #performance #recommendation #retrieval
Efficient retrieval of recommendations in a matrix factorization framework (NK, PR, YS), pp. 535–544.
CIKMCIKM-2012-LeePKL #graph #named #novel #ranking #recommendation
PathRank: a novel node ranking measure on a heterogeneous graph for recommender systems (SL, SP, MK, SgL), pp. 1637–1641.
CIKMCIKM-2012-LiangXTC #recommendation #topic
Time-aware topic recommendation based on micro-blogs (HL, YX, DT, PC), pp. 1657–1661.
CIKMCIKM-2012-LiKBCL #approach #named #probability #query #recommendation
DQR: a probabilistic approach to diversified query recommendation (RL, BK, BB, RC, EL), pp. 16–25.
CIKMCIKM-2012-LiL #framework #named #recommendation
MEET: a generalized framework for reciprocal recommender systems (LL, TL), pp. 35–44.
CIKMCIKM-2012-LinXLHL #named #personalisation #recommendation #social
PRemiSE: personalized news recommendation via implicit social experts (CL, RX, LL, ZH, TL), pp. 1607–1611.
CIKMCIKM-2012-LiuTYL #recommendation
Exploring personal impact for group recommendation (XL, YT, MY, WCL), pp. 674–683.
CIKMCIKM-2012-MahajanRTM #algorithm #named #recommendation
LogUCB: an explore-exploit algorithm for comments recommendation (DKM, RR, CT, AM), pp. 6–15.
CIKMCIKM-2012-MeleBG #graph #recommendation
The early-adopter graph and its application to web-page recommendation (IM, FB, AG), pp. 1682–1686.
CIKMCIKM-2012-OliveiraGBBAZG #automation #query #recommendation
Automatic query expansion based on tag recommendation (VCdO, GdCMG, FB, WCB, JMA, NZ, MAG), pp. 1985–1989.
CIKMCIKM-2012-PapaioannouROA #assessment #distributed #effectiveness #recommendation #web
A decentralized recommender system for effective web credibility assessment (TGP, JER, AO, KA), pp. 704–713.
CIKMCIKM-2012-RedaPTPS #named #recommendation
Metaphor: a system for related search recommendations (AR, YP, MT, CP, SS), pp. 664–673.
CIKMCIKM-2012-SunWGM #hybrid #learning #rank #recommendation
Learning to rank for hybrid recommendation (JS, SW, BJG, JM), pp. 2239–2242.
CIKMCIKM-2012-TangZLW #mining #quality #recommendation
Incorporating occupancy into frequent pattern mining for high quality pattern recommendation (LT, LZ, PL, MW), pp. 75–84.
CIKMCIKM-2012-TorresHWS #query #recommendation
Query recommendation for children (SDT, DH, IW, PS), pp. 2010–2014.
CIKMCIKM-2012-WanKC #recommendation #social
Location-sensitive resources recommendation in social tagging systems (CW, BK, DWC), pp. 1960–1964.
CIKMCIKM-2012-ZhangLZW #recommendation
Relation regularized subspace recommending for related scientific articles (QZ, JL, ZZ, LW), pp. 2503–2506.
CIKMCIKM-2012-ZhuGCL #behaviour #mining #query #recommendation
More than relevance: high utility query recommendation by mining users’ search behaviors (XZ, JG, XC, YL), pp. 1814–1818.
ECIRECIR-2012-YanZ #approach #community #recommendation
A New Approach to Answerer Recommendation in Community Question Answering Services (ZY, JZ), pp. 121–132.
ICMLICML-2012-PurushothamL #collaboration #matrix #recommendation #social #topic
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (SP, YL), p. 92.
KDDKDD-2012-0002L #named #recommendation
RecMax: exploiting recommender systems for fun and profit (AG, LVSL), pp. 1294–1302.
KDDKDD-2012-BawabMC #personalisation #query #recommendation #topic
Finding trending local topics in search queries for personalization of a recommendation system (ZAB, GHM, JFC), pp. 397–405.
KDDKDD-2012-FengW #personalisation #recommendation #social
Incorporating heterogeneous information for personalized tag recommendation in social tagging systems (WF, JW), pp. 1276–1284.
KDDKDD-2012-Posse #lessons learnt #network #recommendation #scalability #social
Key lessons learned building recommender systems for large-scale social networks (CP), p. 587.
KDDKDD-2012-ShenJ #learning #recommendation #social
Learning personal + social latent factor model for social recommendation (YS, RJ), pp. 1303–1311.
KDDKDD-2012-ShiA #dataset #mobile #recommendation
GetJar mobile application recommendations with very sparse datasets (KS, KA), pp. 204–212.
KDDKDD-2012-ShiZKYLW #named #network #recommendation #semantics
HeteRecom: a semantic-based recommendation systemin heterogeneous networks (CS, CZ, XK, PSY, GL, BW), pp. 1552–1555.
KDDKDD-2012-TangWSS #collaboration #recommendation
Cross-domain collaboration recommendation (JT, SW, JS, HS), pp. 1285–1293.
KDDKDD-2012-WuWCT #detection #hybrid #named #recommendation
HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation (ZW, JW, JC, DT), pp. 985–993.
KDDKDD-2012-YangSL #network #online #recommendation #social
Circle-based recommendation in online social networks (XY, HS, YL), pp. 1267–1275.
KDIRKDIR-2012-BarbieriBCMR #modelling #probability #recommendation #sequence
Probabilistic Sequence Modeling for Recommender Systems (NB, AB, MC, GM, ER), pp. 75–84.
KDIRKDIR-2012-DinsoreanuMHP #approach #recommendation
A Unified Approach for Context-sensitive Recommendations (MD, FCM, OLH, RP), pp. 85–94.
KDIRKDIR-2012-FormosoFCC #performance #recommendation #using
Using Neighborhood Pre-computation to Increase Recommendation Efficiency (VF, DF, FC, VC), pp. 333–335.
KDIRKDIR-2012-FukumotoMM #analysis #collaboration #recommendation #sentiment
Collaborative Filtering based on Sentiment Analysis of Guest Reviews for Hotel Recommendation (FF, CM, SM), pp. 193–198.
KDIRKDIR-2012-LuongHGH #network #recommendation #social
Exploiting Social Networks for Publication Venue Recommendations (HPL, TH, SG, KH), pp. 239–245.
KEODKEOD-2012-AbdelazzizN #ontology #recommendation #using
Enhancing the Results of Recommender Systems using Implicit Ontology Relations (LA, KN), pp. 5–14.
KEODKEOD-2012-LuC #clustering #documentation #order #recommendation
Bringing Order to Legal Documents — An Issue-based Recommendation System Via Cluster Association (QL, JGC), pp. 76–88.
KMISKMIS-2012-SmirnovKS #collaboration #ontology #recommendation
Ontology Matching in Context-driven Collaborative Recommending Systems (AVS, AK, NS), pp. 139–144.
RecSysRecSys-2012-AharonKLK #elicitation #personalisation #recommendation
Dynamic personalized recommendation of comment-eliciting stories (MA, AK, RL, YK), pp. 209–212.
RecSysRecSys-2012-Amatriain #recommendation
Building industrial-scale real-world recommender systems (XA), pp. 7–8.
RecSysRecSys-2012-AminYSBP #network #recommendation #social
Social referral: leveraging network connections to deliver recommendations (MSA, BY, SS, AB, CP), pp. 273–276.
RecSysRecSys-2012-AntunesCG #approach #development #recommendation
An approach to context-based recommendation in software development (BA, JC, PG), pp. 171–178.
RecSysRecSys-2012-BellufXG #case study #online #personalisation #recommendation #scalability
Case study on the business value impact of personalized recommendations on a large online retailer (TB, LX, RG), pp. 277–280.
RecSysRecSys-2012-BostandjievOH #hybrid #interactive #named #recommendation #visual notation
TasteWeights: a visual interactive hybrid recommender system (SB, JO, TH), pp. 35–42.
RecSysRecSys-2012-ChhabraR #named #recommendation
CubeThat: news article recommender (SC, PR), pp. 295–296.
RecSysRecSys-2012-Diaz-AvilesDSN #realtime #recommendation #social
Real-time top-n recommendation in social streams (EDA, LD, LST, WN), pp. 59–66.
RecSysRecSys-2012-Diaz-AvilesGN #rank #recommendation
Swarming to rank for recommender systems (EDA, MG, WN), pp. 229–232.
RecSysRecSys-2012-EkstrandR #algorithm #predict #recommendation
When recommenders fail: predicting recommender failure for algorithm selection and combination (MDE, JR), pp. 233–236.
RecSysRecSys-2012-GertnerLW #enterprise #recommendation
Recommenders for the enterprise: event, contact, and group (ASG, BL, JW), pp. 299–300.
RecSysRecSys-2012-HaririMB #music #recommendation #topic
Context-aware music recommendation based on latenttopic sequential patterns (NH, BM, RDB), pp. 131–138.
RecSysRecSys-2012-JiangJFZ #recommendation
Recommending academic papers via users’ reading purposes (YJ, AJ, YF, DZ), pp. 241–244.
RecSysRecSys-2012-KarimiFNS #learning #matrix #recommendation
Exploiting the characteristics of matrix factorization for active learning in recommender systems (RK, CF, AN, LST), pp. 317–320.
RecSysRecSys-2012-Knijnenburg #recommendation
Conducting user experiments in recommender systems (BPK), pp. 3–4.
RecSysRecSys-2012-KnijnenburgBOK #recommendation #social
Inspectability and control in social recommenders (BPK, SB, JO, AK), pp. 43–50.
RecSysRecSys-2012-KoenigsteinNPS #recommendation
The Xbox recommender system (NK, NN, UP, NS), pp. 281–284.
RecSysRecSys-2012-Landia #documentation #folksonomy #recommendation
Utilising document content for tag recommendation in folksonomies (NL), pp. 325–328.
RecSysRecSys-2012-Lempel #challenge #recommendation #web
Recommendation challenges in web media settings (RL), pp. 205–206.
RecSysRecSys-2012-LeviMDT #recommendation
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system (AL, OM, CD, NT), pp. 115–122.
RecSysRecSys-2012-LeviMDT12a #recommendation
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system demo (AL, OM, CD, NT), pp. 305–306.
RecSysRecSys-2012-LiuCCY #named #recommendation
Enlister: baidu’s recommender system for the biggest chinese Q&A website (QL, TC, JC, DY), pp. 285–288.
RecSysRecSys-2012-LiuXCGXBZ #recommendation
Influential seed items recommendation (QL, BX, EC, YG, HX, TB, YZ), pp. 245–248.
RecSysRecSys-2012-Manzato #recommendation
Discovering latent factors from movies genres for enhanced recommendation (MGM), pp. 249–252.
RecSysRecSys-2012-MolingBR #feedback #recommendation
Optimal radio channel recommendations with explicit and implicit feedback (OM, LB, FR), pp. 75–82.
RecSysRecSys-2012-Ninaus #heuristic #recommendation #requirements #using
Using group recommendation heuristics for the prioritization of requirements (GN), pp. 329–332.
RecSysRecSys-2012-NingK #linear #recommendation
Sparse linear methods with side information for top-n recommendations (XN, GK), pp. 155–162.
RecSysRecSys-2012-NoiaMOR #modelling #recommendation #web
Exploiting the web of data in model-based recommender systems (TDN, RM, VCO, DR), pp. 253–256.
RecSysRecSys-2012-NunesH #bibliography #perspective #recommendation
Personality-based recommender systems: an overview (MASNN, RH), pp. 5–6.
RecSysRecSys-2012-Parra #recommendation #visualisation
Beyond lists: studying the effect of different recommendation visualizations (DP), pp. 333–336.
RecSysRecSys-2012-PessemierDM #design #evaluation #recommendation
Design and evaluation of a group recommender system (TDP, SD, LM), pp. 225–228.
RecSysRecSys-2012-PraweshP #feedback #probability #recommendation
Probabilistic news recommender systems with feedback (SP, BP), pp. 257–260.
RecSysRecSys-2012-RibeiroLVZ #multi #recommendation
Pareto-efficient hybridization for multi-objective recommender systems (MTR, AL, AV, NZ), pp. 19–26.
RecSysRecSys-2012-RodriguezPZ #multi #optimisation #recommendation
Multiple objective optimization in recommender systems (MR, CP, EZ), pp. 11–18.
RecSysRecSys-2012-SaidTH #challenge #recommendation
The challenge of recommender systems challenges (AS, DT, AH), pp. 9–10.
RecSysRecSys-2012-SklarSH #realtime #recommendation
Recommending interesting events in real-time with foursquare check-ins (MS, BS, AH), pp. 311–312.
RecSysRecSys-2012-StrickrothP #community #network #quality #recommendation
High quality recommendations for small communities: the case of a regional parent network (SS, NP), pp. 107–114.
RecSysRecSys-2012-Wakeling #design #library #recommendation
The user-centered design of a recommender system for a universal library catalogue (SW), pp. 337–340.
RecSysRecSys-2012-WuGRR #microblog #recommendation
Making recommendations in a microblog to improve the impact of a focal user (SW, LG, WR, LR), pp. 265–268.
RecSysRecSys-2012-YangCZLY #feedback #mining #music #recommendation
Local implicit feedback mining for music recommendation (DY, TC, WZ, QL, YY), pp. 91–98.
RecSysRecSys-2012-YangSGL #network #on the #recommendation #social #using
On top-k recommendation using social networks (XY, HS, YG, YL), pp. 67–74.
RecSysRecSys-2012-Zanker #information management #recommendation
The influence of knowledgeable explanations on users’ perception of a recommender system (MZ), pp. 269–272.
RecSysRecSys-2012-ZelenikB #information management #recommendation
Reducing the sparsity of contextual information for recommender systems (DZ, MB), pp. 341–344.
RecSysRecSys-2012-ZhangTSWY #approach #image #recommendation #semantics
A semantic approach to recommending text advertisements for images (WZ, LT, XS, HW, YY), pp. 179–186.
SEKESEKE-2012-HuiLCDM #e-commerce #empirical #recommendation
An Empirical Study on Recommendation Methods for Vertical B2C E-commerce (CH, JL, ZC, XD, WM), pp. 139–142.
SEKESEKE-2012-RadulovicG #network #process #recommendation #semantics
Semantic Technology Recommendation Based on the Analytic Network Process (FR, RGC), pp. 611–616.
SEKESEKE-2012-SmithP #design pattern #recommendation
Dynamically recommending design patterns (SS, DRP), pp. 499–504.
SIGIRSIGIR-2012-AdeyanjuSAKRF #adaptation #concept #query #recommendation
Adaptation of the concept hierarchy model with search logs for query recommendation on intranets (IAA, DS, MDA, UK, ANDR, MF), pp. 5–14.
SIGIRSIGIR-2012-AgarwalCEW #online #personalisation #recommendation
Personalized click shaping through lagrangian duality for online recommendation (DA, BCC, PE, XW), pp. 485–494.
SIGIRSIGIR-2012-BonchiPSVV #performance #query #recommendation
Efficient query recommendations in the long tail via center-piece subgraphs (FB, RP, FS, HV, RV), pp. 345–354.
SIGIRSIGIR-2012-ChenCSX #music #named #recommendation
Pictune: situational music recommendation from geotagged pictures (KC, GC, LS, FX), p. 1011.
SIGIRSIGIR-2012-ChenCZJYY #collaboration #personalisation #recommendation #twitter
Collaborative personalized tweet recommendation (KC, TC, GZ, OJ, EY, YY), pp. 661–670.
SIGIRSIGIR-2012-Cleger-TamayoFH #recommendation
Explaining neighborhood-based recommendations (SCT, JMFL, JFH), pp. 1063–1064.
SIGIRSIGIR-2012-MaoLCCS #named #recommendation
myDJ: recommending karaoke songs from one’s own voice (KM, XL, KC, GC, LS), p. 1009.
SIGIRSIGIR-2012-PeraN #named #recommendation
BReK12: a book recommender for K-12 users (MSP, YKN), pp. 1037–1038.
SIGIRSIGIR-2012-QumsiyehN #multi #personalisation #predict #recommendation
Predicting the ratings of multimedia items for making personalized recommendations (RQ, YKN), pp. 475–484.
SIGIRSIGIR-2012-SaidJNPAS #case study #recommendation
Estimating the magic barrier of recommender systems: a user study (AS, BJJ, SN, TP, SA, CS), pp. 1061–1062.
SIGIRSIGIR-2012-ShiKBLHO #named #optimisation #recommendation
TFMAP: optimizing MAP for top-n context-aware recommendation (YS, AK, LB, ML, AH, NO), pp. 155–164.
SIGIRSIGIR-2012-ShiZWLH #adaptation #recommendation
Adaptive diversification of recommendation results via latent factor portfolio (YS, XZ, JW, ML, AH), pp. 175–184.
SIGIRSIGIR-2012-XuJW #community #recommendation
Dual role model for question recommendation in community question answering (FX, ZJ, BW), pp. 771–780.
SIGIRSIGIR-2012-YeLL #approach #generative #recommendation #social
Exploring social influence for recommendation: a generative model approach (MY, XL, WCL), pp. 671–680.
MODELSMoDELS-2012-MaraeeB #analysis #comparative #constraints #guidelines #modelling #recommendation #uml
Inter-association Constraints in UML2: Comparative Analysis, Usage Recommendations, and Modeling Guidelines (AM, MB), pp. 302–318.
MODELSMoDELS-2012-MaraeeB #analysis #comparative #constraints #guidelines #modelling #recommendation #uml
Inter-association Constraints in UML2: Comparative Analysis, Usage Recommendations, and Modeling Guidelines (AM, MB), pp. 302–318.
OOPSLAOOPSLA-2012-MusluBHEN #analysis #development #ide #recommendation
Speculative analysis of integrated development environment recommendations (KM, YB, RH, MDE, DN), pp. 669–682.
RERE-2012-Cleland-HuangMMA #recommendation #traceability
Breaking the big-bang practice of traceability: Pushing timely trace recommendations to project stakeholders (JCH, PM, MM, SA), pp. 231–240.
SACSAC-2012-ChenNX #collaboration #recommendation
A common neighbour based two-way collaborative recommendation method (LC, RN, YX), pp. 214–215.
SACSAC-2012-HijikataKN #recommendation #user satisfaction
The relation between user intervention and user satisfaction for information recommendation (YH, YK, SN), pp. 2002–2007.
SACSAC-2012-KuttyCN #modelling #recommendation #using
A people-to-people recommendation system using tensor space models (SK, LC, RN), pp. 187–192.
SACSAC-2012-LageDD #effectiveness #microblog #recommendation #towards
Towards effective group recommendations for microblogging users (RL, FAD, PD), pp. 923–928.
SACSAC-2012-ManzatoG #multi #recommendation
A multimedia recommender system based on enriched user profiles (MGM, RG), pp. 975–980.
SACSAC-2012-MirizziNSR #recommendation #semantics #web
Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations (RM, TDN, EDS, AR), pp. 403–405.
SACSAC-2012-TrabelsiMY #folksonomy #markov #modelling #named #recommendation
HMM-CARe: Hidden Markov Models for context-aware tag recommendation in folksonomies (CT, BM, SBY), pp. 957–961.
SACSAC-2012-WangZHHZWT #mining #mobile #recommendation
Context-aware role mining for mobile service recommendation (JW, CZ, CH, LH, LZ, RKW, JT), pp. 173–178.
FSEFSE-2012-Murphy-HillJM #developer #development #recommendation
Improving software developers’ fluency by recommending development environment commands (ERMH, RJ, GCM), p. 42.
ICSEICSE-2012-HuangLXW #mining #recommendation #repository #xml
Mining application repository to recommend XML configuration snippets (SH, YL, YX, WW), pp. 1451–1452.
ICSEICSE-2012-McMillanHPCM #agile #prototype #recommendation #source code
Recommending source code for use in rapid software prototypes (CM, NH, DP, JCH, BM), pp. 848–858.
ICSEICSE-2012-MusluBHEN #ide #recommendation
Improving IDE recommendations by considering global implications of existing recommendations (KM, YB, RH, MDE, DN), pp. 1349–1352.
ICSEICSE-2012-ZhangYZFZZO #api #automation #parametricity #recommendation
Automatic parameter recommendation for practical API usage (CZ, JY, YZ, JF, XZ, JZ, PO), pp. 826–836.
CBSECBSE-2011-MeloP #component #framework #open source #recommendation
A component-based open-source framework for general-purpose recommender systems (FMM, ÁRPJ), pp. 67–72.
ASEASE-2011-LozanoKM #named #recommendation #search-based #source code
Mendel: Source code recommendation based on a genetic metaphor (AL, AK, KM), pp. 384–387.
ASEASE-2011-WangFWLXY #api #effectiveness #java #named #recommendation #web
APIExample: An effective web search based usage example recommendation system for java APIs (LW, LF, LW, GL, BX, FY), pp. 592–595.
DocEngDocEng-2011-ChidlovskiiB #learning #metric #network #recommendation #social
Local metric learning for tag recommendation in social networks (BC, AB), pp. 205–208.
SIGMODSIGMOD-2011-ChandramouliLEM #named #realtime #recommendation
StreamRec: a real-time recommender system (BC, JJL, AE, MFM), pp. 1243–1246.
VLDBVLDB-2011-LevandoskiELEMR #architecture #benchmark #metric #named #performance #recommendation
RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures (JJL, MDE, ML, AE, MFM, JR), pp. 911–920.
VLDBVLDB-2011-MachanavajjhalaKS #personalisation #question #recommendation #social
Personalized Social Recommendations — Accurate or Private? (AM, AK, ADS), pp. 440–450.
ITiCSEITiCSE-2011-HarrachA #collaboration #learning #optimisation #process #recommendation #using
Optimizing collaborative learning processes by using recommendation systems (SH, MA), p. 389.
ICSMEICSM-2011-LeeK #clustering #recommendation
Clustering and recommending collections of code relevant to tasks (SL, SK), pp. 536–539.
WCREWCRE-2011-SurianLLTLF #collaboration #developer #network #people #recommendation
Recommending People in Developers’ Collaboration Network (DS, NL, DL, HT, EPL, CF), pp. 379–388.
CHICHI-2011-ChenNC #online #recommendation #social
Speak little and well: recommending conversations in online social streams (JC, RN, EHhC), pp. 217–226.
CHICHI-2011-John #design #modelling #performance #predict #recommendation #user interface #using
Using predictive human performance models to inspire and support UI design recommendations (BEJ), pp. 983–986.
CHICHI-2011-SchwindBH #recommendation
I will do it, but i don’t like it: user reactions to preference-inconsistent recommendations (CS, JB, FWH), pp. 349–352.
CSCWCSCW-2011-GuyURPJ #enterprise #recommendation
Do you want to know?: recommending strangers in the enterprise (IG, SU, IR, AP, MJ), pp. 285–294.
HCIDUXU-v1-2011-BaltrunasLPR #mobile #recommendation
Context-Aware Places of Interest Recommendations for Mobile Users (LB, BL, SP, FR), pp. 531–540.
HCIHCI-DDA-2011-KogaT #recommendation #topic #twitter #using
Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics (HK, TT), pp. 461–470.
HCIHCI-ITE-2011-KuramotoYMT #interactive #multi #recommendation
Recommendation System Based on Interaction with Multiple Agents for Users with Vague Intention (IK, AY, MM, YT), pp. 351–357.
HCIHCI-MIIE-2011-AiharaKT #behaviour #cost analysis #recommendation
Behavioral Cost-Based Recommendation Model for Wanderers in Town (KA, HK, HT), pp. 271–279.
HCIHCI-UA-2011-OlivierWP #game studies #music #named #recommendation
MusicTagger: Exploiting User Generated Game Data for Music Recommendation (HO, MW, NP), pp. 678–687.
HCIHIMI-v1-2011-BreyerNSBK #personalisation #recommendation
A Comprehensive Reference Model for Personalized Recommender Systems (MB, KN, CS, DB, AK), pp. 528–537.
HCIHIMI-v1-2011-KoCECCKH #recommendation
A Smart Movie Recommendation System (SKK, SMC, HSE, JWC, HC, LK, YSH), pp. 558–566.
HCIOCSC-2011-PujariK #approach #machine learning #predict #recommendation
A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
HCIOCSC-2011-PuseyM #collaboration #design #learning #recommendation #wiki
Assessments in Large- and Small-Scale Wiki Collaborative Learning Environments: Recommendations for Educators and Wiki Designers (PP, GM), pp. 60–68.
ICEISICEIS-v2-2011-ChenGC #enterprise #recommendation
Enterprise Knowledge Practice and Recommendation based on HOTP Model (BC, YG, DC), pp. 444–450.
ICEISICEIS-v2-2011-WangS #education #information management #recommendation
Application of Recommender Engine in Academic Degree and Postgraduate Education Knowledge Management System (XW, HS), pp. 455–458.
CIKMCIKM-2011-BoimMN #collaboration #recommendation #refinement
Diversification and refinement in collaborative filtering recommender (RB, TM, SN), pp. 739–744.
CIKMCIKM-2011-DongBHRC #optimisation #personalisation #recommendation
User action interpretation for personalized content optimization in recommender systems (AD, JB, XH, SR, YC), pp. 2129–2132.
CIKMCIKM-2011-DraidiPPV #named #recommendation
P2Prec: a social-based P2P recommendation system (FD, EP, DP, GV), pp. 2593–2596.
CIKMCIKM-2011-DrosouP #database #named #recommendation
ReDRIVE: result-driven database exploration through recommendations (MD, EP), pp. 1547–1552.
CIKMCIKM-2011-LiLSG #community #named #online #performance #privacy #recommendation #social
YANA: an efficient privacy-preserving recommender system for online social communities (DL, QL, LS, NG), pp. 2269–2272.
CIKMCIKM-2011-LuHSY #recommendation
Recommending citations with translation model (YL, JH, DS, HY), pp. 2017–2020.
CIKMCIKM-2011-MoghaddamJE #bibliography #online #personalisation #predict #quality #recommendation
Review recommendation: personalized prediction of the quality of online reviews (SM, MJ, ME), pp. 2249–2252.
CIKMCIKM-2011-PeraN #personalisation #recommendation
A personalized recommendation system on scholarly publications (MSP, YKN), pp. 2133–2136.
CIKMCIKM-2011-SeguelaS #hybrid #recommendation
A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi (JS, GS), pp. 2265–2268.
CIKMCIKM-2011-ShiehLW #recommendation
Recommendation in the end-to-end encrypted domain (JRS, CYL, JLW), pp. 915–924.
CIKMCIKM-2011-SongQF #recommendation #visualisation
Hierarchical tag visualization and application for tag recommendations (YS, BQ, UF), pp. 1331–1340.
CIKMCIKM-2011-WangHLCH #learning #recommendation
Learning to recommend questions based on public interest (JW, XH, ZL, WHC, BH), pp. 2029–2032.
CIKMCIKM-2011-YanGC #higher-order #learning #query #recommendation
Context-aware query recommendation by learning high-order relation in query logs (XY, JG, XC), pp. 2073–2076.
ECIRECIR-2011-ChilukaAP #approach #predict #recommendation #scalability
A Link Prediction Approach to Recommendations in Large-Scale User-Generated Content Systems (NC, NA, JAP), pp. 189–200.
ECIRECIR-2011-HannonMS #recommendation #twitter
Finding Useful Users on Twitter: Twittomender the Followee Recommender (JH, KM, BS), pp. 784–787.
ECIRECIR-2011-MoshfeghiJ #collaboration #recommendation
Role of Emotional Features in Collaborative Recommendation (YM, JMJ), pp. 738–742.
ECIRECIR-2011-PhelanMBS #recommendation #twitter #using
Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (OP, KM, MB, BS), pp. 448–459.
ECIRECIR-2011-ShiLH11a #how #question #recommendation #trust
How Far Are We in Trust-Aware Recommendation? (YS, ML, AH), pp. 704–707.
ECIRECIR-2011-WartenaW #recommendation #topic
Improving Tag-Based Recommendation by Topic Diversification (CW, MW), pp. 43–54.
KDDKDD-2011-AgarwalCL #locality #modelling #multi #recommendation
Localized factor models for multi-context recommendation (DA, BCC, BL), pp. 609–617.
KDDKDD-2011-DrorKMS #exclamation #recommendation
I want to answer; who has a question?: Yahoo! answers recommender system (GD, YK, YM, IS), pp. 1109–1117.
KDDKDD-2011-GeLXTC #cost analysis #recommendation
Cost-aware travel tour recommendation (YG, QL, HX, AT, JC), pp. 983–991.
KDDKDD-2011-PradelSDGRUFD #case study #recommendation
A case study in a recommender system based on purchase data (BP, SS, JD, SG, CR, NU, FFS, FDJ), pp. 377–385.
KDDKDD-2011-WangB #collaboration #modelling #recommendation #topic
Collaborative topic modeling for recommending scientific articles (CW, DMB), pp. 448–456.
KEODKEOD-2011-HusakovaC #multi #ontology #recommendation #simulation
Exploitation of Ontology-based Recommendation System with Multi-agent Simulations (MH, PC), pp. 433–436.
KMISKMIS-2011-Borchardt #recommendation #towards
Towards a Value-oriented KMS Recommendation for SME (UB), pp. 347–350.
RecSysRecSys-2011-Alam #clustering #recommendation #web
Intelligent web usage clustering based recommender system (SA), pp. 367–370.
RecSysRecSys-2011-BaltrunasLR #matrix #recommendation
Matrix factorization techniques for context aware recommendation (LB, BL, FR), pp. 301–304.
RecSysRecSys-2011-BarbieriCMO #approach #modelling #recommendation
Modeling item selection and relevance for accurate recommendations: a bayesian approach (NB, GC, GM, RO), pp. 21–28.
RecSysRecSys-2011-Bellogin #performance #predict #recommendation
Predicting performance in recommender systems (AB), pp. 371–374.
RecSysRecSys-2011-BelloginCC #algorithm #comparison #evaluation #recommendation
Precision-oriented evaluation of recommender systems: an algorithmic comparison (AB, PC, IC), pp. 333–336.
RecSysRecSys-2011-BourkeMS #people #recommendation #social
Power to the people: exploring neighbourhood formations in social recommender system (SB, KM, BS), pp. 337–340.
RecSysRecSys-2011-BraunhoferKR #mobile #music #recommendation
Recommending music for places of interest in a mobile travel guide (MB, MK, FR), pp. 253–256.
RecSysRecSys-2011-CamposDS #evaluation #matrix #predict #recommendation #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.
RecSysRecSys-2011-CelmaL #music #recommendation #revisited
Music recommendation and discovery revisited (ÒC, PL), pp. 7–8.
RecSysRecSys-2011-Chen #design #interactive #interface #recommendation #social
Interface and interaction design for group and social recommender systems (YC), pp. 363–366.
RecSysRecSys-2011-DalyG #effectiveness #recommendation #social #using
Effective event discovery: using location and social information for scoping event recommendations (EMD, WG), pp. 277–280.
RecSysRecSys-2011-DayanKBRSASF #benchmark #framework #metric #recommendation
Recommenders benchmark framework (AD, GK, NB, LR, BS, AA, RS, RF), pp. 353–354.
RecSysRecSys-2011-EkstrandLKR #ecosystem #recommendation #research
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit (MDE, ML, JAK, JR), pp. 133–140.
RecSysRecSys-2011-EkstrandLKR11a #composition #framework #named #recommendation
LensKit: a modular recommender framework (MDE, ML, JK, JR), pp. 349–350.
RecSysRecSys-2011-Faridani #analysis #canonical #correlation #recommendation #sentiment #using
Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search (SF), pp. 355–358.
RecSysRecSys-2011-ForbesZ #matrix #recommendation
Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation (PF, MZ), pp. 261–264.
RecSysRecSys-2011-GantnerRFS #library #named #recommendation
MyMediaLite: a free recommender system library (ZG, SR, CF, LST), pp. 305–308.
RecSysRecSys-2011-GorgoglionePT #behaviour #recommendation #trust
The effect of context-aware recommendations on customer purchasing behavior and trust (MG, UP, AT), pp. 85–92.
RecSysRecSys-2011-GuzziRB #interactive #multi #recommendation
Interactive multi-party critiquing for group recommendation (FG, FR, RDB), pp. 265–268.
RecSysRecSys-2011-Hurley #recommendation #robust
Robustness of recommender systems (NJH), pp. 9–10.
RecSysRecSys-2011-JamaliHE #network #probability #rating #recommendation #social
A generalized stochastic block model for recommendation in social rating networks (MJ, TH, ME), pp. 53–60.
RecSysRecSys-2011-KimE #personalisation #rank #recommendation
Personalized PageRank vectors for tag recommendations: inside FolkRank (HNK, AES), pp. 45–52.
RecSysRecSys-2011-KnijnenburgRW #how #interactive #recommendation
Each to his own: how different users call for different interaction methods in recommender systems (BPK, NJMR, MCW), pp. 141–148.
RecSysRecSys-2011-KnijnenburgWK #evaluation #recommendation
A pragmatic procedure to support the user-centric evaluation of recommender systems (BPK, MCW, AK), pp. 321–324.
RecSysRecSys-2011-KoenigsteinDK #exclamation #modelling #music #recommendation #taxonomy
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy (NK, GD, YK), pp. 165–172.
RecSysRecSys-2011-LeeL #analysis #behaviour #music #recommendation
My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation (KL, KL), pp. 213–220.
RecSysRecSys-2011-LeeSKLL #graph #multi #random #ranking #recommendation
Random walk based entity ranking on graph for multidimensional recommendation (SL, SiS, MK, DL, SgL), pp. 93–100.
RecSysRecSys-2011-LiuMLY #elicitation #rating #recommendation
Wisdom of the better few: cold start recommendation via representative based rating elicitation (NNL, XM, CL, QY), pp. 37–44.
RecSysRecSys-2011-LiuMX #multi #recommendation
Multi-criteria service recommendation based on user criteria preferences (LL, NM, DLX), pp. 77–84.
RecSysRecSys-2011-LiZL #integration #named #personalisation #recommendation
LOGO: a long-short user interest integration in personalized news recommendation (LL, LZ, TL), pp. 317–320.
RecSysRecSys-2011-Makrehchi #learning #recommendation #social #topic
Social link recommendation by learning hidden topics (MM), pp. 189–196.
RecSysRecSys-2011-PaparrizosCG #recommendation
Machine learned job recommendation (IKP, BBC, AG), pp. 325–328.
RecSysRecSys-2011-PizzatoS #collaboration #people #probability #recommendation
Stochastic matching and collaborative filtering to recommend people to people (LASP, CS), pp. 341–344.
RecSysRecSys-2011-PraweshP #recommendation
The “top N” news recommender: count distortion and manipulation resistance (SP, BP), pp. 237–244.
RecSysRecSys-2011-PuCH #evaluation #framework #recommendation
A user-centric evaluation framework for recommender systems (PP, LC, RH), pp. 157–164.
RecSysRecSys-2011-SabinC #named #online #recommendation
myMicSound: an online sound-based microphone recommendation system (ATS, CLC), pp. 351–352.
RecSysRecSys-2011-SaidBLH #challenge #recommendation
Challenge on context-aware movie recommendation: CAMRa2011 (AS, SB, EWDL, JH), pp. 385–386.
RecSysRecSys-2011-SekoYMM #behaviour #recommendation #representation #using
Group recommendation using feature space representing behavioral tendency and power balance among members (SS, TY, MM, SyM), pp. 101–108.
RecSysRecSys-2011-Steck #recommendation
Item popularity and recommendation accuracy (HS), pp. 125–132.
RecSysRecSys-2011-Sundaresan #recommendation
Recommender systems at the long tail (NS), pp. 1–6.
RecSysRecSys-2011-SymeonidisTM #multi #network #predict #rating #recommendation #social
Product recommendation and rating prediction based on multi-modal social networks (PS, ET, YM), pp. 61–68.
RecSysRecSys-2011-TayebiJEGF #named #recommendation
CrimeWalker: a recommendation model for suspect investigation (MAT, MJ, ME, UG, RF), pp. 173–180.
RecSysRecSys-2011-Tschersich #design #guidelines #mobile #recommendation
Design guidelines for mobile group recommender systems to handle inaccurate or missing location data (MT), pp. 359–362.
RecSysRecSys-2011-Tunkelang #recommendation
Recommendations as a conversation with the user (DT), pp. 11–12.
RecSysRecSys-2011-VargasC #metric #rank #recommendation
Rank and relevance in novelty and diversity metrics for recommender systems (SV, PC), pp. 109–116.
RecSysRecSys-2011-WangSS #e-commerce #recommendation
Utilizing related products for post-purchase recommendation in e-commerce (JW, BS, NS), pp. 329–332.
RecSysRecSys-2011-WoerndlHBG #mobile #recommendation
A model for proactivity in mobile, context-aware recommender systems (WW, JH, RB, DGV), pp. 273–276.
RecSysRecSys-2011-WuRR #monitoring #recommendation #social #social media
Recommendations in social media for brand monitoring (SW, WR, LR), pp. 345–348.
RecSysRecSys-2011-XinS #matrix #multi #probability #recommendation
Multi-value probabilistic matrix factorization for IP-TV recommendations (YX, HS), pp. 221–228.
RecSysRecSys-2011-YuanCZ #recommendation #social
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation (QY, LC, SZ), pp. 245–252.
RecSysRecSys-2011-YuPL #adaptation #recommendation #social
Adaptive social similarities for recommender systems (LY, RP, ZL), pp. 257–260.
RecSysRecSys-2011-Zhang #recommendation
Anchoring effects of recommender systems (JZ), pp. 375–378.
SEKESEKE-2011-CaiZWXS #approach #component #recommendation
Recommending Component by Citation: A Semi-supervised Approach for Determination (SC, YZ, LW, BX, WS), pp. 489–494.
SEKESEKE-2011-ZhangSPCM #architecture #design #quality #recommendation #towards
Towards Quality Based Solution Recommendation in Decision-Centric Architecture Design (LZ, YS, YP, XC, HM), pp. 776–781.
SIGIRSIGIR-2011-BelemMPAG #multi #recommendation
Associative tag recommendation exploiting multiple textual features (FB, EFM, TP, JMA, MAG), pp. 1033–1042.
SIGIRSIGIR-2011-BelloginCC #analysis #hybrid #network #recommendation #self #social
Self-adjusting hybrid recommenders based on social network analysis (AB, PC, IC), pp. 1147–1148.
SIGIRSIGIR-2011-ChenC #recommendation #web
Recommending ephemeral items at web scale (YC, JFC), pp. 1013–1022.
SIGIRSIGIR-2011-LeungLL #clustering #collaboration #framework #named #recommendation
CLR: a collaborative location recommendation framework based on co-clustering (KWTL, DLL, WCL), pp. 305–314.
SIGIRSIGIR-2011-LiDS #concept #recommendation #semantics #using
Semantic tag recommendation using concept model (CL, AD, AS), pp. 1159–1160.
SIGIRSIGIR-2011-LiLS #recommendation #social
Exploiting endorsement information and social influence for item recommendation (CTL, SDL, MKS), pp. 1131–1132.
SIGIRSIGIR-2011-LinDW #image #recommendation
Image annotation based on recommendation model (ZL, GD, JW), pp. 1097–1098.
SIGIRSIGIR-2011-LiWLKP #named #personalisation #recommendation #scalability
SCENE: a scalable two-stage personalized news recommendation system (LL, DW, TL, DK, BP), pp. 125–134.
SIGIRSIGIR-2011-MaLKL #modelling #probability #recommendation #web
Probabilistic factor models for web site recommendation (HM, CL, IK, MRL), pp. 265–274.
SIGIRSIGIR-2011-RendleGFS #performance #recommendation
Fast context-aware recommendations with factorization machines (SR, ZG, CF, LST), pp. 635–644.
SIGIRSIGIR-2011-RicciGBAGP #named #quality #recommendation #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.
SIGIRSIGIR-2011-ShiYGN #machine learning #network #recommendation #scalability #social
A large scale machine learning system for recommending heterogeneous content in social networks (YS, DY, AG, SN), pp. 1337–1338.
SIGIRSIGIR-2011-TangWB #recommendation
Recommending interesting activity-related local entities (JT, RWW, PB), pp. 1161–1162.
SIGIRSIGIR-2011-VargasCV #recommendation
Intent-oriented diversity in recommender systems (SV, PC, DV), pp. 1211–1212.
SIGIRSIGIR-2011-Wang #comprehension #information management #recommendation #using
Understanding and using contextual information in recommender systems (LW), pp. 1329–1330.
SIGIRSIGIR-2011-WangZ #e-commerce #recommendation
Utilizing marginal net utility for recommendation in e-commerce (JW, YZ), pp. 1003–1012.
SIGIRSIGIR-2011-WeiHL #analysis #framework #recommendation #semantics
A unified framework for recommendations based on quaternary semantic analysis (WC, WH, MLL), pp. 1023–1032.
SIGIRSIGIR-2011-YangLSZZ #collaboration #learning #recommendation #using
Collaborative competitive filtering: learning recommender using context of user choice (SHY, BL, AJS, HZ, ZZ), pp. 295–304.
SIGIRSIGIR-2011-YeYLL #collaboration #recommendation
Exploiting geographical influence for collaborative point-of-interest recommendation (MY, PY, WCL, DLL), pp. 325–334.
SIGIRSIGIR-2011-ZhouYZ #functional #matrix #recommendation
Functional matrix factorizations for cold-start recommendation (KZ, SHY, HZ), pp. 315–324.
ECOOPECOOP-2011-Duala-EkokoR #api #recommendation #using
Using Structure-Based Recommendations to Facilitate Discoverability in APIs (EDE, MPR), pp. 79–104.
ESEC-FSEESEC-FSE-2011-ZhengZL #api #recommendation #using #web
Cross-library API recommendation using web search engines (WZ, QZ, MRL), pp. 480–483.
ICSEICSE-2011-DumitruGHCMCM #mining #on-demand #recommendation
On-demand feature recommendations derived from mining public product descriptions (HD, MG, NH, JCH, BM, CCH, MM), pp. 181–190.
ICSEICSE-2011-NguyenNNN #evolution #recommendation
Aspect recommendation for evolving software (TTN, HVN, HAN, TNN), pp. 361–370.
ASEASE-2010-ZhengZLX #generative #random #recommendation #sequence #testing
Random unit-test generation with MUT-aware sequence recommendation (WZ, QZ, MRL, TX), pp. 293–296.
HTHT-2010-GasslerZTS #named #recommendation #using
SnoopyDB: narrowing the gap between structured and unstructured information using recommendations (WG, EZ, MT, GS), pp. 271–272.
HTHT-2010-GuoJ #collaboration #personalisation #recommendation #topic
Topic-based personalized recommendation for collaborative tagging system (YG, JBDJ), pp. 61–66.
HTHT-2010-LiangXLNT #personalisation #recommendation
Connecting users and items with weighted tags for personalized item recommendations (HL, YX, YL, RN, XT), pp. 51–60.
HTHT-2010-LiuFZ #recommendation #social
Speak the same language with your friends: augmenting tag recommenders with social relations (KL, BF, WZ), pp. 45–50.
SIGMODSIGMOD-2010-MoriczDB #named #recommendation
PYMK: friend recommendation at myspace (MM, YD, MB), pp. 999–1002.
SIGMODSIGMOD-2010-ParameswaranKBG #algorithm #mining #named #precedence #recommendation
Recsplorer: recommendation algorithms based on precedence mining (AGP, GK, BB, HGM), pp. 87–98.
VLDBVLDB-2010-AkbarnejadCEKMOPV #recommendation #sql
SQL QueRIE Recommendations (JA, GC, ME, SK, SM, DO, NP, JSVV), pp. 1597–1600.
CSEETCSEET-2010-GarousiM #education #recommendation #testing
Current State of the Software Testing Education in North American Academia and Some Recommendations for the New Educators (VG, AM), pp. 89–96.
CHICHI-2010-ChenNNBC #recommendation #twitter
Short and tweet: experiments on recommending content from information streams (JC, RN, LN, MSB, EHC), pp. 1185–1194.
CHICHI-2010-WalshG #game studies #named #recommendation
Curator: a game with a purpose for collection recommendation (GW, JG), pp. 2079–2082.
CIKMCIKM-2010-BelemMAGP #metric #quality #recommendation #web
Exploiting co-occurrence and information quality metrics to recommend tags in web 2.0 applications (FMB, EFM, JMdA, MAG, GLP), pp. 1793–1796.
CIKMCIKM-2010-CaiLLTL #recommendation
Recommendation based on object typicality (YC, HfL, QL, JT, JL), pp. 1529–1532.
CIKMCIKM-2010-Garcia-AlvaradoCO #query #recommendation
OLAP-based query recommendation (CGA, ZC, CO), pp. 1353–1356.
CIKMCIKM-2010-GemmellSMB #hybrid #recommendation #social
Hybrid tag recommendation for social annotation systems (JG, TS, BM, RDB), pp. 829–838.
CIKMCIKM-2010-GolbandiKL #on the #recommendation
On bootstrapping recommender systems (NG, YK, RL), pp. 1805–1808.
CIKMCIKM-2010-GuoCXS #approach #query #recommendation #social
A structured approach to query recommendation with social annotation data (JG, XC, GX, HS), pp. 619–628.
CIKMCIKM-2010-KurashimaIIF #recommendation #using
Travel route recommendation using geotags in photo sharing sites (TK, TI, GI, KF), pp. 579–588.
CIKMCIKM-2010-LiangXLN #folksonomy #personalisation #recommendation #taxonomy
Personalized recommender system based on item taxonomy and folksonomy (HL, YX, YL, RN), pp. 1641–1644.
CIKMCIKM-2010-MinkovCLTJ #collaboration #recommendation
Collaborative future event recommendation (EM, BC, JL, SJT, TSJ), pp. 819–828.
CIKMCIKM-2010-NakatsujiFTUFI #music #novel #recommendation
Classical music for rock fans?: novel recommendations for expanding user interests (MN, YF, AT, TU, KF, TI), pp. 949–958.
CIKMCIKM-2010-ParameswaranGU #recommendation
Evaluating, combining and generalizing recommendations with prerequisites (AGP, HGM, JDU), pp. 919–928.
CIKMCIKM-2010-PengZZW #collaboration #recommendation #social
Collaborative filtering in social tagging systems based on joint item-tag recommendations (JP, DDZ, HZ, FYW), pp. 809–818.
CIKMCIKM-2010-ShavittWW #peer-to-peer #recommendation #using
Building recommendation systems using peer-to-peer shared content (YS, EW, UW), pp. 1457–1460.
CIKMCIKM-2010-WartenaSW #keyword #recommendation
Selecting keywords for content based recommendation (CW, WS, MW), pp. 1533–1536.
CIKMCIKM-2010-ZhangZ #modelling #personalisation #recommendation
Discriminative factored prior models for personalized content-based recommendation (LZ, YZ), pp. 1569–1572.
CIKMCIKM-2010-ZhaoBCGWZ #concurrent #learning #online #recommendation #thread
Learning a user-thread alignment manifold for thread recommendation in online forum (JZ, JB, CC, ZG, CW, CZ), pp. 559–568.
ECIRECIR-2010-RedpathGMC #collaboration #recommendation
Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings (JR, DHG, SIM, LC), pp. 394–406.
KDDKDD-2010-AgarwalCE #learning #online #performance #recommendation
Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDDKDD-2010-GeXTXGP #energy #mobile #recommendation
An energy-efficient mobile recommender system (YG, HX, AT, KX, MG, MJP), pp. 899–908.
KDDKDD-2010-JahrerTL #predict #recommendation
Combining predictions for accurate recommender systems (MJ, AT, RAL), pp. 693–702.
KDDKDD-2010-Steck #random #recommendation #testing
Training and testing of recommender systems on data missing not at random (HS), pp. 713–722.
KDDKDD-2010-XiangYZCZYS #graph #recommendation
Temporal recommendation on graphs via long- and short-term preference fusion (LX, QY, SZ, LC, XZ, QY, JS), pp. 723–732.
KDIRKDIR-2010-ArmanoV #recommendation
A Unifying View of Contextual Advertising and Recommender Systems (GA, EV), pp. 463–466.
KDIRKDIR-2010-Quinteiro-GonzalezMHLMP #framework #recommendation
Recommendation System in an Audiovisual Delivery Platform (JMQG, EMJ, PHM, ALR, LMM, ASdP), pp. 379–382.
RecSysRecSys-2010-AdomaviciusZ #algorithm #on the #recommendation
On the stability of recommendation algorithms (GA, JZ), pp. 47–54.
RecSysRecSys-2010-AlbanesedMPP #modelling #problem #recommendation #social
Modeling recommendation as a social choice problem (MA, Ad, VM, FP, AP), pp. 329–332.
RecSysRecSys-2010-AydayF #online #recommendation
A belief propagation based recommender system for online services (EA, FF), pp. 217–220.
RecSysRecSys-2010-Baeza-Yates #predict #query #recommendation
Query intent prediction and recommendation (RABY), pp. 5–6.
RecSysRecSys-2010-BaglioniBBCFVP #lightweight #mobile #privacy #recommendation
A lightweight privacy preserving SMS-based recommendation system for mobile users (EB, LB, LB, UMC, LF, AV, GP), pp. 191–198.
RecSysRecSys-2010-Balakrishnan #on-demand #recommendation
On-demand set-based recommendations (SB), pp. 313–316.
RecSysRecSys-2010-BaltrunasMR #collaboration #rank #recommendation
Group recommendations with rank aggregation and collaborative filtering (LB, TM, FR), pp. 119–126.
RecSysRecSys-2010-BarrioR #collaboration #recommendation
Geolocated movie recommendations based on expert collaborative filtering (JBB, XAR), pp. 347–348.
RecSysRecSys-2010-BenchettaraKR #approach #collaboration #machine learning #predict #recommendation
A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
RecSysRecSys-2010-BerkovskyF #analysis #recommendation
Group-based recipe recommendations: analysis of data aggregation strategies (SB, JF), pp. 111–118.
RecSysRecSys-2010-BerkovskyFCB #algorithm #game studies #process #recommendation
Recommender algorithms in activity motivating games (SB, JF, MC, DB), pp. 175–182.
RecSysRecSys-2010-BollenKWG #comprehension #recommendation
Understanding choice overload in recommender systems (DGFMB, BPK, MCW, MPG), pp. 63–70.
RecSysRecSys-2010-Burke #algorithm #recommendation
Evaluating the dynamic properties of recommendation algorithms (RDB), pp. 225–228.
RecSysRecSys-2010-CantadorBV #recommendation #social
Content-based recommendation in social tagging systems (IC, AB, DV), pp. 237–240.
RecSysRecSys-2010-CastagnosJP #recommendation
Eye-tracking product recommenders’ usage (SC, NJ, PP), pp. 29–36.
RecSysRecSys-2010-CebrianPVA #music #recommendation
Music recommendations with temporal context awareness (TC, MP, PV, XA), pp. 349–352.
RecSysRecSys-2010-CremonesiKT #algorithm #performance #recommendation
Performance of recommender algorithms on top-n recommendation tasks (PC, YK, RT), pp. 39–46.
RecSysRecSys-2010-DalyGM #network #recommendation #social
The network effects of recommending social connections (EMD, WG, DRM), pp. 301–304.
RecSysRecSys-2010-DavidsonLLNVGGHLLS #recommendation #video
The YouTube video recommendation system (JD, BL, JL, PN, TVV, UG, SG, YH, ML, BL, DS), pp. 293–296.
RecSysRecSys-2010-DeryKRS #nondeterminism #recommendation
Iterative voting under uncertainty for group recommender systems (LND, MK, LR, BS), pp. 265–268.
RecSysRecSys-2010-EsparzaOS #on the #realtime #recommendation #web
On the real-time web as a source of recommendation knowledge (SGE, MPO, BS), pp. 305–308.
RecSysRecSys-2010-FreyneBDG #network #recommendation #social
Social networking feeds: recommending items of interest (JF, SB, EMD, WG), pp. 277–280.
RecSysRecSys-2010-GedikliJ #rating #recommendation
Recommending based on rating frequencies (FG, DJ), pp. 233–236.
RecSysRecSys-2010-GeDJ #recommendation
Beyond accuracy: evaluating recommender systems by coverage and serendipity (MG, CDB, DJ), pp. 257–260.
RecSysRecSys-2010-GuyJAMNNS #industrial #perspective #recommendation
Will recommenders kill search?: recommender systems — an industry perspective (IG, AJ, PA, PM, PN, CN, HS), pp. 7–12.
RecSysRecSys-2010-HammerKA #named #recommendation
MED-StyleR: METABO diabetes-lifestyle recommender (SH, JK, EA), pp. 285–288.
RecSysRecSys-2010-HannonBS #collaboration #recommendation #twitter #using
Recommending twitter users to follow using content and collaborative filtering approaches (JH, MB, BS), pp. 199–206.
RecSysRecSys-2010-Hu #design #recommendation
Design and user issues in personality-based recommender systems (RH), pp. 357–360.
RecSysRecSys-2010-ImK #personalisation #recommendation
Personalizing the settings for Cf-based recommender systems (II, BHK), pp. 245–248.
RecSysRecSys-2010-JamaliE #matrix #network #recommendation #social #trust
A matrix factorization technique with trust propagation for recommendation in social networks (MJ, ME), pp. 135–142.
RecSysRecSys-2010-JawaheerSK #feedback #music #online #recommendation
Characterisation of explicit feedback in an online music recommendation service (GJ, MS, PK), pp. 317–320.
RecSysRecSys-2010-KaragiannidisAZV #framework #named #recommendation
Hydra: an open framework for virtual-fusion of recommendation filters (SK, SA, CZ, AV), pp. 229–232.
RecSysRecSys-2010-KaratzoglouABO #collaboration #multi #recommendation
Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering (AK, XA, LB, NO), pp. 79–86.
RecSysRecSys-2010-LappasG #interactive #network #recommendation #social
Interactive recommendations in social endorsement networks (TL, DG), pp. 127–134.
RecSysRecSys-2010-LipczakM #learning #performance #recommendation
Learning in efficient tag recommendation (ML, EEM), pp. 167–174.
RecSysRecSys-2010-MarxHM #algorithm #comprehension #hybrid #recommendation
Increasing consumers’ understanding of recommender results: a preference-based hybrid algorithm with strong explanatory power (PM, THT, AM), pp. 297–300.
RecSysRecSys-2010-MoldvayBFS #clustering #graph #named #recommendation #semantics #social
Tagmantic: a social recommender service based on semantic tag graphs and tag clusters (JM, IB, AF, MS), pp. 345–346.
RecSysRecSys-2010-Musto #modelling #recommendation
Enhanced vector space models for content-based recommender systems (CM), pp. 361–364.
RecSysRecSys-2010-PizzatoRCKK #named #online #recommendation
RECON: a reciprocal recommender for online dating (LASP, TR, TC, IK, JK), pp. 207–214.
RecSysRecSys-2010-PizzatoRCKYK #online #recommendation
Reciprocal recommender system for online dating (LASP, TR, TC, IK, KY, JK), pp. 353–354.
RecSysRecSys-2010-Said #hybrid #identification #recommendation
Identifying and utilizing contextual data in hybrid recommender systems (AS), pp. 365–368.
RecSysRecSys-2010-SandholmUAH #recommendation
Global budgets for local recommendations (TS, HU, CA, BAH), pp. 13–20.
RecSysRecSys-2010-Schirru #enterprise #recommendation #social #social media #topic
Topic-based recommendations in enterprise social media sharing platforms (RS), pp. 369–372.
RecSysRecSys-2010-Servan-Schreiber #recommendation
Recommendation analytics: the business view, and the business case (ESS), pp. 215–216.
RecSysRecSys-2010-Shani #recommendation #tutorial
Tutorial on evaluating recommender systems (GS), p. 1.
RecSysRecSys-2010-VasukiNLD #network #recommendation #using
Affiliation recommendation using auxiliary networks (VV, NN, ZL, ISD), pp. 103–110.
RecSysRecSys-2010-XieLW #recommendation
Breaking out of the box of recommendations: from items to packages (MX, LVSL, PTW), pp. 151–158.
RecSysRecSys-2010-ZangerleGS #collaboration #information management #recommendation
Recommending structure in collaborative semistructured information systems (EZ, WG, GS), pp. 261–264.
RecSysRecSys-2010-ZhaoZYZZF #recommendation #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.
RecSysRecSys-2010-ZhengWZLY #case study #empirical #recommendation
Do clicks measure recommendation relevancy?: an empirical user study (HZ, DW, QZ, HL, TY), pp. 249–252.
SIGIRSIGIR-2010-GuyZRCU #people #recommendation #social #social media
Social media recommendation based on people and tags (IG, NZ, IR, DC, EU), pp. 194–201.
SIGIRSIGIR-2010-HuangAH #classification #recommendation #tool support
Medical search and classification tools for recommendation (XH, AA, QH), p. 707.
SIGIRSIGIR-2010-Kawamae #recommendation
Serendipitous recommendations via innovators (NK), pp. 218–225.
SIGIRSIGIR-2010-LathiaHCA #recommendation
Temporal diversity in recommender systems (NL, SH, LC, XA), pp. 210–217.
SIGIRSIGIR-2010-WangLC10a #recommendation #social #social media
User comments for news recommendation in social media (JW, QL, YPC), pp. 881–882.
SIGIRSIGIR-2010-ZhengLLZ #composition #recommendation
Flickr group recommendation based on tensor decomposition (NZ, QL, SL, LZ), pp. 737–738.
SACSAC-2010-BallatoreMKB #adaptation #interactive #named #recommendation
RecoMap: an interactive and adaptive map-based recommender (AB, GM, CK, MB), pp. 887–891.
SACSAC-2010-DuraoD #hybrid #personalisation #recommendation
Extending a hybrid tag-based recommender system with personalization (FAD, PD), pp. 1723–1727.
SACSAC-2010-KimY #multi #personalisation #recommendation
New theoretical findings in multiple personalized recommendations (YHK, YY), pp. 94–98.
SACSAC-2010-ShinKNNTO #named #ontology #recommendation #using
ONTOMO: web-based ontology building system: ---instance recommendation using bootstrapping--- (IS, TK, HN, KN, YT, AO), pp. 1442–1443.
SACSAC-2010-SuYT #music #novel #recommendation
A novel music recommender by discovering preferable perceptual-patterns from music pieces (JHS, HHY, VST), pp. 1924–1928.
ICSEICSE-2010-HolmesW #recommendation
Customized awareness: recommending relevant external change events (RH, RJW), pp. 465–474.
ICSEICSE-2010-Schroter #recommendation
Failure preventing recommendations (AS), pp. 397–400.
ICSTICST-2010-EngstromRW #empirical #evaluation #recommendation #testing
An Empirical Evaluation of Regression Testing Based on Fix-Cache Recommendations (EE, PR, GW), pp. 75–78.
ASEASE-2009-HolmesRRW #automation #recommendation #reuse
Automatically Recommending Triage Decisions for Pragmatic Reuse Tasks (RH, TR, MPR, RJW), pp. 397–408.
HTHT-2009-MahmoodR #adaptation #recommendation
Improving recommender systems with adaptive conversational strategies (TM, FR), pp. 73–82.
HTHT-2009-SiersdorferS #folksonomy #recommendation #social #web
Social recommender systems for web 2.0 folksonomies (SS, SS), pp. 261–270.
SIGMODSIGMOD-2009-KoutrikaBG #flexibility #named #recommendation
FlexRecs: expressing and combining flexible recommendations (GK, BB, HGM), pp. 745–758.
VLDBVLDB-2009-Amer-YahiaRCDY #performance #recommendation #semantics
Group Recommendation: Semantics and Efficiency (SAY, SBR, AC, GD, CY), pp. 754–765.
VLDBVLDB-2009-El-HelwIZ #named #recommendation #statistics
StatAdvisor: Recommending Statistical Views (AEH, IFI, CZ), pp. 1306–1317.
ICSMEICSM-2009-MaSZS #recommendation
Expert recommendation with usage expertise (DM, DS, TZ, JS), pp. 535–538.
MSRMSR-2009-RastkarM #interactive #on the #question #recommendation #what
On what basis to recommend: Changesets or interactions? (SR, GCM), pp. 155–158.
CHICHI-2009-ChenGDMG #network #people #recommendation #social
Make new friends, but keep the old: recommending people on social networking sites (JC, WG, CD, MJM, IG), pp. 201–210.
CHICHI-2009-HansenG #recommendation
Mixing it up: recommending collections of items (DLH, JG), pp. 1217–1226.
CHICHI-2009-RaoHNJ #recommendation
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.
CHICHI-2009-ReichlingW #automation #generative #recommendation
Expert recommender systems in practice: evaluating semi-automatic profile generation (TR, VW), pp. 59–68.
HCIOCSC-2009-SongNKE #recommendation #using #word
A Proposed Movie Recommendation Method Using Emotional Word Selection (MS, HN, HGK, JE), pp. 525–534.
ICEISICEIS-DISI-2009-Pitkaranta #information retrieval #recommendation
Applying Information Retrieval for Market Basket Recommender Systems (TP), pp. 138–143.
ICEISICEIS-J-2009-JerbiRTZ #recommendation
Applying Recommendation Technology in OLAP Systems (HJ, FR, OT, GZ), pp. 220–233.
ICEISICEIS-SAIC-2009-CiuffoI #case study #collaboration #information management #recommendation #using
Using Grids to Support Information Filtering Systems — A Case Study of Running Collaborative Filtering Recommendations on gLite (LNC, EI), pp. 12–18.
ICEISICEIS-SAIC-2009-DrumondGMA #implementation #multi #recommendation
Implementation Issues of the Infonorma Multi-agent Recommender System (LD, RG, DM, GA), pp. 128–133.
CIKMCIKM-2009-AnastasakosHKR #approach #collaboration #graph #recommendation #using
A collaborative filtering approach to ad recommendation using the query-ad click graph (TA, DH, SK, HR), pp. 1927–1930.
CIKMCIKM-2009-CaoCYX #recommendation
Enhancing recommender systems under volatile userinterest drifts (HC, EC, JY, HX), pp. 1257–1266.
CIKMCIKM-2009-ShiWY #framework #named #recommendation #semantics
Msuggest: a semantic recommender framework for traditional chinese medicine book search engine (SS, BW, YY), pp. 533–542.
CIKMCIKM-2009-SunCSSWL #learning #recommendation
Learning to recommend questions based on user ratings (KS, YC, XS, YIS, XW, CYL), pp. 751–758.
CIKMCIKM-2009-XinKDL #framework #multi #random #recommendation #social
A social recommendation framework based on multi-scale continuous conditional random fields (XX, IK, HD, MRL), pp. 1247–1256.
ECIRECIR-2009-MoshfeghiAPJ #collaboration #predict #rating #recommendation #semantics
Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering (YM, DA, BP, JMJ), pp. 54–65.
ECIRECIR-2009-PapadopoulosMKB #graph #recommendation
Lexical Graphs for Improved Contextual Ad Recommendation (SP, FM, YK, BB), pp. 216–227.
KDDKDD-2009-JamaliE #named #random #recommendation #trust
TrustWalker: a random walk model for combining trust-based and item-based recommendation (MJ, ME), pp. 397–406.
KDDKDD-2009-LiDJEL #random #recommendation
Grocery shopping recommendations based on basket-sensitive random walk (ML, MBD, IHJ, WED, PJGL), pp. 1215–1224.
KDDKDD-2009-McSherryM #privacy #recommendation
Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders (FM, IM), pp. 627–636.
KDDKDD-2009-OnumaTF #algorithm #named #novel #recommendation
TANGENT: a novel, “Surprise me”, recommendation algorithm (KO, HT, CF), pp. 657–666.
KDDKDD-2009-RendleMNS #learning #ranking #recommendation
Learning optimal ranking with tensor factorization for tag recommendation (SR, LBM, AN, LST), pp. 727–736.
KEODKEOD-2009-SantosS #recommendation
Interpretation and Recommendation Tasks Supported by Ceres System (CPS, DRdS), pp. 464–467.
RecSysRecSys-2009-AbbassiALVY #recommendation
Getting recommender systems to think outside the box (ZA, SAY, LVSL, SV, CY), pp. 285–288.
RecSysRecSys-2009-AmatriainPTO #recommendation
Rate it again: increasing recommendation accuracy by user re-rating (XA, JMP, NT, NO), pp. 173–180.
RecSysRecSys-2009-AntonelliFGL #named #recommendation
DynamicTV: a culture-aware recommender (FA, GF, MG, SL), pp. 257–260.
RecSysRecSys-2009-BaoBT #recommendation
Stacking recommendation engines with additional meta-features (XB, LB, RT), pp. 109–116.
RecSysRecSys-2009-BaragliaCCFFPS #approach #query #recommendation
Search shortcuts: a new approach to the recommendation of queries (RB, FC, VC, DF, VF, RP, FS), pp. 77–84.
RecSysRecSys-2009-BaskinK #recommendation
Preference aggregation in group recommender systems for committee decision-making (JPB, SK), pp. 337–340.
RecSysRecSys-2009-BhattacharjeeGK #architecture #recommendation #social
An incentive-based architecture for social recommendations (RB, AG, KK), pp. 229–232.
RecSysRecSys-2009-BroccoG #network #recommendation
Team recommendation in open innovation networks (MB, GG), pp. 365–368.
RecSysRecSys-2009-CastagnosJP #process #recommendation
Recommenders’ influence on buyers’ decision process (SC, NJ, PP), pp. 361–364.
RecSysRecSys-2009-Castro-HerreraCM #evolution #online #recommendation
A recommender system for dynamically evolving online forums (CCH, JCH, BM), pp. 213–216.
RecSysRecSys-2009-Chen #adaptation #recommendation #trade-off
Adaptive tradeoff explanations in conversational recommenders (LC), pp. 225–228.
RecSysRecSys-2009-ChengH #effectiveness #modelling #obfuscation #recommendation
Effective diverse and obfuscated attacks on model-based recommender systems (ZC, NH), pp. 141–148.
RecSysRecSys-2009-ConryKR #problem #recommendation
Recommender systems for the conference paper assignment problem (DC, YK, NR), pp. 357–360.
RecSysRecSys-2009-CremonesiT #analysis #recommendation
Analysis of cold-start recommendations in IPTV systems (PC, RT), pp. 233–236.
RecSysRecSys-2009-FreyneJGG #recommendation
Increasing engagement through early recommender intervention (JF, MJ, IG, WG), pp. 85–92.
RecSysRecSys-2009-GansnerHKV #clustering #recommendation #visualisation
Putting recommendations on the map: visualizing clusters and relations (ERG, YH, SGK, CV), pp. 345–348.
RecSysRecSys-2009-GemmellRSCM #ambiguity #folksonomy #recommendation
The impact of ambiguity and redundancy on tag recommendation in folksonomies (JG, MR, TS, LC, BM), pp. 45–52.
RecSysRecSys-2009-GivonL #predict #recommendation
Predicting social-tags for cold start book recommendations (SG, VL), pp. 333–336.
RecSysRecSys-2009-Golbeck #recommendation #social #trust #tutorial #using
Tutorial on using social trust for recommender systems (JG), pp. 425–426.
RecSysRecSys-2009-GreenLAMKHBM #generative #recommendation
Generating transparent, steerable recommendations from textual descriptions of items (SJG, PL, JA, FM, SK, JH, JB, XWM), pp. 281–284.
RecSysRecSys-2009-GunawardanaM #approach #hybrid #recommendation
A unified approach to building hybrid recommender systems (AG, CM), pp. 117–124.
RecSysRecSys-2009-GuyZCRUYO #personalisation #recommendation #social
Personalized recommendation of social software items based on social relations (IG, NZ, DC, IR, EU, SY, SOK), pp. 53–60.
RecSysRecSys-2009-HelouSSG #process #ranking #recommendation
The 3A contextual ranking system: simultaneously recommending actors, assets, and group activities (SEH, CS, SS, DG), pp. 373–376.
RecSysRecSys-2009-HuP #recommendation
Acceptance issues of personality-based recommender systems (RH, PP), pp. 221–224.
RecSysRecSys-2009-JamaliE #network #recommendation #trust #using
Using a trust network to improve top-N recommendation (MJ, ME), pp. 181–188.
RecSysRecSys-2009-JannachH #case study #effectiveness #internet #mobile #recommendation
A case study on the effectiveness of recommendations in the mobile internet (DJ, KH), pp. 205–208.
RecSysRecSys-2009-JaschkeEHS #recommendation #testing
Testing and evaluating tag recommenders in a live system (RJ, FE, AH, GS), pp. 369–372.
RecSysRecSys-2009-KawamaeSY #personalisation #recommendation
Personalized recommendation based on the personal innovator degree (NK, HS, TY), pp. 329–332.
RecSysRecSys-2009-KhezrzadehTW #power of #recommendation
Harnessing the power of “favorites” lists for recommendation systems (MK, AT, WWW), pp. 289–292.
RecSysRecSys-2009-KnijnenburgW #adaptation #comprehension #elicitation #recommendation #user satisfaction
Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system (BPK, MCW), pp. 381–384.
RecSysRecSys-2009-KrestelFN #recommendation
Latent dirichlet allocation for tag recommendation (RK, PF, WN), pp. 61–68.
RecSysRecSys-2009-LousameS #recommendation
View-based recommender systems (FPL, ES), pp. 389–392.
RecSysRecSys-2009-MaLK #learning #recommendation #trust
Learning to recommend with trust and distrust relationships (HM, MRL, IK), pp. 189–196.
RecSysRecSys-2009-MoghaddamJEH #feedback #named #recommendation #trust #using
FeedbackTrust: using feedback effects in trust-based recommendation systems (SM, MJ, ME, JH), pp. 269–272.
RecSysRecSys-2009-NathansonBG #recommendation
Donation dashboard: a recommender system for donation portfolios (TN, EB, KYG), pp. 253–256.
RecSysRecSys-2009-Nnadi #clustering #correlation #multi #recommendation #set
Applying relevant set correlation clustering to multi-criteria recommender systems (NN), pp. 401–404.
RecSysRecSys-2009-OMahonyS #learning #recommendation
Learning to recommend helpful hotel reviews (MPO, BS), pp. 305–308.
RecSysRecSys-2009-PannielloTGPP #comparison #recommendation
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems (UP, AT, MG, CP, AP), pp. 265–268.
RecSysRecSys-2009-ParameswaranG #recommendation
Recommendations with prerequisites (AGP, HGM), pp. 353–356.
RecSysRecSys-2009-ParkC #recommendation
Pairwise preference regression for cold-start recommendation (STP, WC), pp. 21–28.
RecSysRecSys-2009-PhelanMS #realtime #recommendation #topic #twitter #using
Using twitter to recommend real-time topical news (OP, KM, BS), pp. 385–388.
RecSysRecSys-2009-PilaszyT #metadata #recommendation
Recommending new movies: even a few ratings are more valuable than metadata (IP, DT), pp. 93–100.
RecSysRecSys-2009-PudhiyaveetilGLE #concept #recommendation
Conceptual recommender system for CiteSeerX (AKP, SG, HPL, JE), pp. 241–244.
RecSysRecSys-2009-PuZC #recommendation
Critiquing recommenders for public taste products (PP, MZ, SC), pp. 249–252.
RecSysRecSys-2009-QasimOWHO #partial order #recommendation
A partial-order based active cache for recommender systems (UQ, VO, YfBW, MEH, MTÖ), pp. 209–212.
RecSysRecSys-2009-QuerciaC #mobile #named #recommendation #using
FriendSensing: recommending friends using mobile phones (DQ, LC), pp. 273–276.
RecSysRecSys-2009-Recio-GarciaJSD #recommendation
Personality aware recommendations to groups (JARG, GJD, AASRG, BDA), pp. 325–328.
RecSysRecSys-2009-Schubert #knowledge-based #personalisation #query #recommendation
Personalized query relaxations and repairs in knowledge-based recommendation (MS), pp. 409–412.
RecSysRecSys-2009-SemeraroLBG #recommendation
Knowledge infusion into content-based recommender systems (GS, PL, PB, MdG), pp. 301–304.
RecSysRecSys-2009-SeyerlehnerFW #on the #recommendation
On the limitations of browsing top-N recommender systems (KS, AF, GW), pp. 321–324.
RecSysRecSys-2009-SymeonidisNM #named #recommendation
MoviExplain: a recommender system with explanations (PS, AN, YM), pp. 317–320.
RecSysRecSys-2009-Tolomei #mining #process #recommendation #web
Search the web x.0: mining and recommending web-mediated processes (GT), pp. 417–420.
RecSysRecSys-2009-TsatsouMKD #analysis #framework #personalisation #recommendation #semantics
A semantic framework for personalized ad recommendation based on advanced textual analysis (DT, FM, IK, PCD), pp. 217–220.
RecSysRecSys-2009-TylerZCZ #categorisation #recommendation
Ordering innovators and laggards for product categorization and recommendation (SKT, SZ, YC, YZ), pp. 29–36.
RecSysRecSys-2009-UmyarovT #estimation #modelling #rating #recommendation #using
Improving rating estimation in recommender systems using aggregation- and variance-based hierarchical models (AU, AT), pp. 37–44.
RecSysRecSys-2009-ViappianiB #recommendation #set
Regret-based optimal recommendation sets in conversational recommender systems (PV, CB), pp. 101–108.
RecSysRecSys-2009-WedelRC #personalisation #recommendation
Up close and personalized: a marketing view of recommendation systems (MW, RTR, TSC), pp. 3–4.
RecSysRecSys-2009-WeimerKB #matrix #recommendation
Maximum margin matrix factorization for code recommendation (MW, AK, MB), pp. 309–312.
RecSysRecSys-2009-Zhang #recommendation
Enhancing diversity in Top-N recommendation (MZ), pp. 397–400.
RecSysRecSys-2009-ZhouR #assessment #recommendation
Assessment of conversation co-mentions as a resource for software module recommendation (DXZ, PR), pp. 133–140.
SIGIRSIGIR-2009-GuanBMCW #graph #multi #personalisation #ranking #recommendation #using
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects (ZG, JB, QM, CC, CW), pp. 540–547.
SIGIRSIGIR-2009-KonstasSJ #collaboration #network #on the #recommendation #social
On social networks and collaborative recommendation (IK, VS, JMJ), pp. 195–202.
SIGIRSIGIR-2009-MaKL #learning #recommendation #social #trust
Learning to recommend with social trust ensemble (HM, IK, MRL), pp. 203–210.
ECOOPECOOP-2009-ZhongXZPM #api #mining #named #recommendation
MAPO: Mining and Recommending API Usage Patterns (HZ, TX, LZ, JP, HM), pp. 318–343.
RERE-2009-Castro-HerreraCM #elicitation #online #recommendation #requirements
Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation (CCH, JCH, BM), pp. 37–46.
SACSAC-2009-Castro-HerreraDCM #elicitation #recommendation #requirements #scalability
A recommender system for requirements elicitation in large-scale software projects (CCH, CD, JCH, BM), pp. 1419–1426.
SACSAC-2009-dAciernoMP #library #recommendation
A recommendation system for browsing digital libraries (Ad, VM, AP), pp. 1771–1778.
SACSAC-2009-PengC #collaboration #multi #named #recommendation #trust
iTrustU: a blog recommender system based on multi-faceted trust and collaborative filtering (TCP, ScTC), pp. 1278–1285.
ESEC-FSEESEC-FSE-2009-AshokJLRSV #debugging #named #recommendation
DebugAdvisor: a recommender system for debugging (BA, JMJ, HL, SKR, GS, VV), pp. 373–382.
ICSEICSE-2009-DagenaisR #analysis #api #evolution #named #recommendation
SemDiff: Analysis and recommendation support for API evolution (BD, MPR), pp. 599–602.
ASEASE-2008-ZhangGC #analysis #automation #clustering #recommendation
Automated Aspect Recommendation through Clustering-Based Fan-in Analysis (DZ, YG, XC), pp. 278–287.
HTHT-2008-PanissonRS #framework #implementation #named #recommendation #social
X-hinter: a framework for implementing social oriented recommender systems (AP, GR, RS), pp. 235–236.
SIGMODSIGMOD-2008-Amer-YahiaGSY #recommendation #social
From del.icio.us to x.qui.site: recommendations in social tagging sites (SAY, AG, JS, CY), pp. 1323–1326.
SIGMODSIGMOD-2008-Konstan #recommendation
Introduction to recommender systems (JAK), pp. 1373–1374.
VLDBVLDB-2008-ShaoCTYA #enterprise #named #problem #recommendation
EasyTicket: a ticket routing recommendation engine for enterprise problem resolution (QS, YC, ST, XY, NA), pp. 1436–1439.
CSEETCSEET-2008-ShoemakerDIM #assurance #recommendation
Integrating Secure Software Assurance Content with SE2004 Recommendations (DS, AD, JAI, NRM), pp. 59–66.
CHICHI-2008-BellottiBCDFIKNPPRRSW #mobile #recommendation
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.
CHICHI-2008-ODonovanSGBH #interactive #named #recommendation #visual notation
PeerChooser: visual interactive recommendation (JO, BS, BG, SB, TH), pp. 1085–1088.
ICEISICEIS-AIDSS-2008-RokachMS #elicitation #recommendation
Anytime AHP Method for Preferences Elicitation in Stereotype-Based Recommender System (LR, AM, AS), pp. 268–275.
ICEISICEIS-HCI-2008-LucaB #design #implementation #navigation #recommendation
Microformats Based Navigation Assistant — A Non-intrusive Recommender Agent: Design and Implementation (APL, SCB), pp. 54–61.
ICEISICEIS-J-2008-LucaB08a #experience #recommendation #user interface #web
Enhancing User Experience on the Web via Microformats-Based Recommendations (APL, SCB), pp. 321–333.
ICEISICEIS-J-2008-WengXLN08a #estimation #performance #recommendation
An Efficient Neighbourhood Estimation Technique for Making Recommendations (LTW, YX, YL, RN), pp. 253–264.
ICEISICEIS-J-2008-WengXLN08b #quality #recommendation #taxonomy
Improve Recommendation Quality with Item Taxonomic Information (LTW, YX, YL, RN), pp. 265–279.
ICEISICEIS-SAIC-2008-Uchyigit08a #recommendation
A Software Agent for Content Based Recommendations for the WWW (GU), pp. 178–183.
ICEISICEIS-SAIC-2008-WengXLLN #recommendation #taxonomy #web
Web Information Recommendation Making Based on Item Taxonomy (LTW, YX, YL, RN), pp. 20–28.
ICEISICEIS-SAIC-2008-WengXLN #estimation #performance #recommendation
Efficient Neighbourhood Estimation for Recommendation Making (LTW, YX, YL, RN), pp. 12–19.
CIKMCIKM-2008-ChiZGZ #personalisation #probability #recommendation
Probabilistic polyadic factorization and its application to personalized recommendation (YC, SZ, YG, YZ), pp. 941–950.
CIKMCIKM-2008-GuoXBY #community #recommendation
Tapping on the potential of q&a community by recommending answer providers (JG, SX, SB, YY), pp. 921–930.
CIKMCIKM-2008-MaYLK08a #matrix #named #probability #recommendation #social #using
SoRec: social recommendation using probabilistic matrix factorization (HM, HY, MRL, IK), pp. 931–940.
CIKMCIKM-2008-VatturiGDMB #personalisation #recommendation
Tag-based filtering for personalized bookmark recommendations (PKV, WG, CD, MJM, BB), pp. 1395–1396.
CIKMCIKM-2008-YatesJPCS #named #recommendation #smarttech #specification
SHOPSMART: product recommendations through technical specifications and user reviews (AY, JJ, AMP, ADC, NS), pp. 1501–1502.
ECIRECIR-2008-ValletHJ #evaluation #graph #recommendation
Use of Implicit Graph for Recommending Relevant Videos: A Simulated Evaluation (DV, FH, JMJ), pp. 199–210.
KDDKDD-2008-ChenZC #collaboration #community #personalisation #recommendation
Combinational collaborative filtering for personalized community recommendation (WC, DZ, EYC), pp. 115–123.
KDDKDD-2008-NguyenPS #recommendation
A software system for buzz-based recommendations (HN, NP, NS), pp. 1093–1096.
RecSysRecSys-2008-AdomaviciusT #recommendation
Context-aware recommender systems (GA, AT), pp. 335–336.
RecSysRecSys-2008-Baltrunas #information management #recommendation
Exploiting contextual information in recommender systems (LB), pp. 295–298.
RecSysRecSys-2008-BogersB #recommendation #using
Recommending scientific articles using citeulike (TB, AvdB), pp. 287–290.
RecSysRecSys-2008-Broder #recommendation
Computational advertising and recommender systems (AZB), pp. 1–2.
RecSysRecSys-2008-BrodskyHW #framework #named #recommendation
CARD: a decision-guidance framework and application for recommending composite alternatives (AB, SMH, JW), pp. 171–178.
RecSysRecSys-2008-BryanOC #collaboration #recommendation #retrieval
Unsupervised retrieval of attack profiles in collaborative recommender systems (KB, MPO, PC), pp. 155–162.
RecSysRecSys-2008-Burke #recommendation #robust
Robust recommender systems (RDB), pp. 331–332.
RecSysRecSys-2008-CelmaH #approach #novel #recommendation
A new approach to evaluating novel recommendations (ÒC, PH), pp. 179–186.
RecSysRecSys-2008-ChenP #evaluation #interface #recommendation
A cross-cultural user evaluation of product recommender interfaces (LC, PP), pp. 75–82.
RecSysRecSys-2008-DegemmisLSB #recommendation #semantics
Integrating tags in a semantic content-based recommender (MD, PL, GS, PB), pp. 163–170.
RecSysRecSys-2008-DiasLLEL #case study #personalisation #recommendation
The value of personalised recommender systems to e-business: a case study (MBD, DL, ML, WED, PJGL), pp. 291–294.
RecSysRecSys-2008-GargW #interactive #personalisation #recommendation
Personalized, interactive tag recommendation for flickr (NG, IW), pp. 67–74.
RecSysRecSys-2008-GeyerDMMF #online #recommendation #self #topic
Recommending topics for self-descriptions in online user profiles (WG, CD, DRM, MJM, JF), pp. 59–66.
RecSysRecSys-2008-GunawardanaM #recommendation
Tied boltzmann machines for cold start recommendations (AG, CM), pp. 19–26.
RecSysRecSys-2008-KoutrikaIBG #flexibility #recommendation
Flexible recommendations over rich data (GK, RI, BB, HGM), pp. 203–210.
RecSysRecSys-2008-KrishnanNNDK #online #predict #recommendation
Who predicts better?: results from an online study comparing humans and an online recommender system (VK, PKN, MN, RTD, JAK), pp. 211–218.
RecSysRecSys-2008-Kwon #rating #recommendation #using
Improving top-n recommendation techniques using rating variance (YK), pp. 307–310.
RecSysRecSys-2008-LakiotakiTM #analysis #multi #named #recommendation
UTA-Rec: a recommender system based on multiple criteria analysis (KL, ST, NFM), pp. 219–226.
RecSysRecSys-2008-Lee #named #recommendation #trust
PITTCULT: trust-based cultural event recommender (DHL), pp. 311–314.
RecSysRecSys-2008-LiWZZC #named #parallel #query #recommendation
Pfp: parallel fp-growth for query recommendation (HL, YW, DZ, MZ, EYC), pp. 107–114.
RecSysRecSys-2008-OostendorpR #interface #recommendation #reduction
Three recommender approaches to interface controls reduction (NO, PR), pp. 235–242.
RecSysRecSys-2008-ParkT #how #recommendation
The long tail of recommender systems and how to leverage it (YJP, AT), pp. 11–18.
RecSysRecSys-2008-Recio-GarciaDG #prototype #recommendation
Prototyping recommender systems in jcolibri (JARG, BDA, PAGC), pp. 243–250.
RecSysRecSys-2008-RendleS #kernel #matrix #modelling #recommendation #scalability
Online-updating regularized kernel matrix factorization models for large-scale recommender systems (SR, LST), pp. 251–258.
RecSysRecSys-2008-ResnickS #recommendation
The information cost of manipulation-resistance in recommender systems (PR, RS), pp. 147–154.
RecSysRecSys-2008-Sampaio #internet #network #performance #process #recommendation
A network performance recommendation process for advanced internet applications users (LNS), pp. 315–318.
RecSysRecSys-2008-Santos #adaptation #lifecycle #recommendation #standard
A recommender system to provide adaptive and inclusive standard-based support along the elearning life cycle (OCS), pp. 319–322.
RecSysRecSys-2008-ShaniCM #mining #recommendation #web
Mining recommendations from the web (GS, DMC, CM), pp. 35–42.
RecSysRecSys-2008-ShepitsenGMB #clustering #personalisation #recommendation #social #using
Personalized recommendation in social tagging systems using hierarchical clustering (AS, JG, BM, RDB), pp. 259–266.
RecSysRecSys-2008-SymeonidisNM #recommendation #reduction
Tag recommendations based on tensor dimensionality reduction (PS, AN, YM), pp. 43–50.
RecSysRecSys-2008-Teppan #recommendation
Implications of psychological phenomenons for recommender systems (ECT), pp. 323–326.
RecSysRecSys-2008-Umyarov #performance #predict #recommendation
Leveraging aggregate ratings for improving predictive performance of recommender systems (AU), pp. 327–330.
RecSysRecSys-2008-WuWC #analysis #automation #incremental #probability #recommendation #semantics
Incremental probabilistic latent semantic analysis for automatic question recommendation (HW, YW, XC), pp. 99–106.
RecSysRecSys-2008-XuJL #documentation #image #online #personalisation #recommendation #video
Personalized online document, image and video recommendation via commodity eye-tracking (SX, HJ, FCML), pp. 83–90.
RecSysRecSys-2008-ZanardiC #ranking #recommendation #social #using
Social ranking: uncovering relevant content using tag-based recommender systems (VZ, LC), pp. 51–58.
RecSysRecSys-2008-Zanker #collaboration #constraints #recommendation
A collaborative constraint-based meta-level recommender (MZ), pp. 139–146.
RecSysRecSys-2008-ZhangH #recommendation
Avoiding monotony: improving the diversity of recommendation lists (MZ, NH), pp. 123–130.
SEKESEKE-2008-Jannach #development #knowledge-based #recommendation
Knowledge-based System Development with Scripting Technology: A Recommender System Example (DJ), pp. 405–416.
SEKESEKE-2008-LucasSM #classification #personalisation #recommendation #towards
Comparing the Use of Traditional and Associative Classifiers towards Personalized Recommendations (JPL, SS, MNMG), pp. 607–612.
SIGIRSIGIR-2008-CreceliusKMNPSW #recommendation #social
Social recommendations at work (TC, MK, SM, TN, JXP, RS, GW), p. 884.
SIGIRSIGIR-2008-SongZLZLLG #automation #realtime #recommendation
Real-time automatic tag recommendation (YS, ZZ, HL, QZ, JL, WCL, CLG), pp. 515–522.
SIGIRSIGIR-2008-ZhangZL #algorithm #rank #recommendation #topic
A topical PageRank based algorithm for recommender systems (LZ, KZ, CL), pp. 713–714.
RERE-2008-Castro-HerreraDCM #data mining #elicitation #mining #process #recommendation #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.
SACSAC-2008-LathiaHC #community #correlation #recommendation
The effect of correlation coefficients on communities of recommenders (NL, SH, LC), pp. 2000–2005.
SACSAC-2008-LohLSWO #keyword #recommendation #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.
SACSAC-2008-TaghipourK #hybrid #recommendation #web
A hybrid web recommender system based on Q-learning (NT, AAK), pp. 1164–1168.
SACSAC-2008-Tso-SutterMS #algorithm #collaboration #recommendation
Tag-aware recommender systems by fusion of collaborative filtering algorithms (KHLTS, LBM, LST), pp. 1995–1999.
SACSAC-2008-VictorCTC #recommendation #trust
Whom should I trust?: the impact of key figures on cold start recommendations (PV, CC, AT, MDC), pp. 2014–2018.
ICSEICSE-2008-DagenaisR #adaptation #evolution #framework #recommendation
Recommending adaptive changes for framework evolution (BD, MPR), pp. 481–490.
ASPLOSASPLOS-2008-McCunePPRS #execution #how #recommendation
How low can you go?: recommendations for hardware-supported minimal TCB code execution (JMM, BP, AP, MKR, AS), pp. 14–25.
HTHT-2007-BradshawL #recommendation
Annotation consensus: implications for passage recommendation in scientific literature (SB, ML), pp. 209–216.
MSRMSR-2007-MintoM #recommendation
Recommending Emergent Teams (SM, GCM), p. 5.
HCIHCI-MIE-2007-KoLKJL #case study #evaluation #personalisation #recommendation #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.
HCIHCI-MIE-2007-OkadaI #collaboration #evaluation #recommendation
Evaluation of P2P Information Recommendation Based on Collaborative Filtering (HO, MI), pp. 449–458.
HCIHIMI-MTT-2007-OrimoKMT #analysis #evaluation #recommendation
Analysis and Evaluation of Recommendation Systems (EO, HK, TM, AT), pp. 144–152.
CAiSECAiSE-2007-StirnaPS #case study #enterprise #experience #modelling #recommendation
Participative Enterprise Modeling: Experiences and Recommendations (JS, AP, KS), pp. 546–560.
ICEISICEIS-EIS-2007-SandkuhlOSSK #case study #experience #industrial #ontology #recommendation
Ontology Construction in Practice — Experiences and Recommendations from Industrial Cases (KS, , AVS, NS, AK), pp. 250–256.
ICEISICEIS-HCI-2007-MelguizoBDBB #memory management #recommendation #what
What a Proactive Recommendation System Needs — Relevance, Non-Intrusiveness, and a New Long-Term Memory (MCPM, TB, AD, LB, AvdB), pp. 86–91.
ICEISICEIS-HCI-2007-UchyigitCC #2d #named #recommendation #user interface
KEXPLORATOR — A 2D Map Exploration User Interface for Recommender Systems (GU, KLC, DC), pp. 223–228.
ICEISICEIS-SAIC-2007-DrumondGL #case study #modelling #recommendation #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.
ICEISICEIS-SAIC-2007-LazanasKK #hybrid #policy #recommendation #web #web service
Applying Hybrid Recommendation Policies through Agent-Invoked Web Services in E-Markets (AL, NIK, VK), pp. 161–166.
ECIRECIR-2007-CastagnosB #community #distributed #personalisation #recommendation
Personalized Communities in a Distributed Recommender System (SC, AB), pp. 343–355.
KDDKDD-2007-BellKV #modelling #multi #recommendation #scalability
Modeling relationships at multiple scales to improve accuracy of large recommender systems (RMB, YK, CV), pp. 95–104.
KDDKDD-2007-DingSJL #framework #kernel #learning #recommendation #using
A learning framework using Green’s function and kernel regularization with application to recommender system (CHQD, RJ, TL, HDS), pp. 260–269.
KDDKDD-2007-Schickel-ZuberF #clustering #learning #recommendation #using
Using hierarchical clustering for learning theontologies used in recommendation systems (VSZ, BF), pp. 599–608.
RecSysRecSys-2007-BogersB #algorithm #information retrieval #recommendation
Comparing and evaluating information retrieval algorithms for news recommendation (TB, AvdB), pp. 141–144.
RecSysRecSys-2007-BridgeR #editing #query #recommendation
Supporting product selection with query editing recommendations (DGB, FR), pp. 65–72.
RecSysRecSys-2007-ChenP #evaluation #hybrid #recommendation
The evaluation of a hybrid critiquing system with preference-based recommendations organization (LC, PP), pp. 169–172.
RecSysRecSys-2007-Donaldson #hybrid #music #recommendation
A hybrid social-acoustic recommendation system for popular music (JD), pp. 187–190.
RecSysRecSys-2007-FreyneFC #data access #recommendation #social #towards
Toward the exploitation of social access patterns for recommendation (JF, RF, MC), pp. 179–182.
RecSysRecSys-2007-HarperSF #clustering #recommendation #social
Supporting social recommendations with activity-balanced clustering (FMH, SS, DF), pp. 165–168.
RecSysRecSys-2007-LeinoR #recommendation
Case amazon: ratings and reviews as part of recommendations (JL, KJR), pp. 137–140.
RecSysRecSys-2007-LiDEL #probability #recommendation
A probabilistic model for item-based recommender systems (ML, MBD, WED, PJGL), pp. 129–132.
RecSysRecSys-2007-Lorenzi #approach #knowledge-based #maintenance #multi #recommendation
A multiagent knowledge-based recommender approach with truth maintenance (FL), pp. 195–198.
RecSysRecSys-2007-MassaA #recommendation #trust
Trust-aware recommender systems (PM, PA), pp. 17–24.
RecSysRecSys-2007-McCarthy #challenge #physics #recommendation
The challenges of recommending digital selves in physical spaces (JFM), pp. 185–186.
RecSysRecSys-2007-NathansonBG #adaptation #clustering #recommendation #using
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering (TN, EB, KYG), pp. 149–152.
RecSysRecSys-2007-NguyenDB #induction #recommendation #rule-based
Improving new user recommendations with rule-based induction on cold user data (ATN, ND, CB), pp. 121–128.
RecSysRecSys-2007-NguyenR #evaluation #game studies #interactive #mobile #recommendation
Replaying live-user interactions in the off-line evaluation of critique-based mobile recommendations (QNN, FR), pp. 81–88.
RecSysRecSys-2007-OMahonyS #online #recommendation
A recommender system for on-line course enrolment: an initial study (MPO, BS), pp. 133–136.
RecSysRecSys-2007-PronkVPT #classification #naive bayes #recommendation
Incorporating user control into recommender systems based on naive bayesian classification (VP, WFJV, AP, MT), pp. 73–80.
RecSysRecSys-2007-ResnickS #recommendation
The influence limiter: provably manipulation-resistant recommender systems (PR, RS), pp. 25–32.
RecSysRecSys-2007-SandvigMB #collaboration #mining #recommendation #robust
Robustness of collaborative recommendation based on association rule mining (JJS, BM, RDB), pp. 105–112.
RecSysRecSys-2007-TaghipourKG #approach #learning #recommendation #web
Usage-based web recommendations: a reinforcement learning approach (NT, AAK, SSG), pp. 113–120.
RecSysRecSys-2007-TiemannP #hybrid #learning #music #recommendation #towards
Towards ensemble learning for hybrid music recommendation (MT, SP), pp. 177–178.
RecSysRecSys-2007-Tintarev #recommendation
Explanations of recommendations (NT), pp. 203–206.
RecSysRecSys-2007-TintarevM #design #effectiveness #recommendation
Effective explanations of recommendations: user-centered design (NT, JM), pp. 153–156.
RecSysRecSys-2007-UmyarovT #recommendation
Leveraging aggregate ratings for better recommendations (AU, AT), pp. 161–164.
RecSysRecSys-2007-ViappianiPF #adaptation #recommendation
Conversational recommenders with adaptive suggestions (PV, PP, BF), pp. 89–96.
RecSysRecSys-2007-WebsterV #recommendation
The keepup recommender system (AW, JV), pp. 173–176.
RecSysRecSys-2007-WinterboerM #recommendation
Evaluating information presentation strategies for spoken recommendations (AW, JDM), pp. 157–160.
RecSysRecSys-2007-ZhangP #algorithm #collaboration #predict #recommendation #recursion
A recursive prediction algorithm for collaborative filtering recommender systems (JZ, PP), pp. 57–64.
SIGIRSIGIR-2007-MeiYHYYL #named #online #recommendation #video
VideoReach: an online video recommendation system (TM, BY, XSH, LY, SQY, SL), pp. 767–768.
SIGIRSIGIR-2007-StrohmanCJ #recommendation
Recommending citations for academic papers (TS, WBC, DJ), pp. 705–706.
SIGIRSIGIR-2007-ZhangK #modelling #performance #recommendation
Efficient bayesian hierarchical user modeling for recommendation system (YZ, JK), pp. 47–54.
SACSAC-2007-BirukouBDGKM #approach #development #named #recommendation
IC-service: a service-oriented approach to the development of recommendation systems (AB, EB, VD, PG, NK, AM), pp. 1683–1688.
SACSAC-2007-RuffoS #peer-to-peer #recommendation
Evaluating peer-to-peer recommender systems that exploit spontaneous affinities (GR, RS), pp. 1574–1578.
ESEC-FSEESEC-FSE-2007-SaulFDB #random #recommendation
Recommending random walks (ZMS, VF, PTD, CB), pp. 15–24.
CHICHI-2006-BonhardHMS #recommendation #similarity #using
Accounting for taste: using profile similarity to improve recommender systems (PB, CH, JDM, MAS), pp. 1057–1066.
CSCWCSCW-2006-McNeeKK #recommendation #research
Don’t look stupid: avoiding pitfalls when recommending research papers (SMM, NK, JAK), pp. 171–180.
ICEISICEIS-SAIC-2006-LazanasKP #framework #recommendation #transaction
Providing Recommendations in an Agent-Based Transportation Transactions Management Platform (AL, NIK, YP), pp. 87–92.
KDDKDD-2006-BurkeMWB #classification #collaboration #detection #recommendation
Classification features for attack detection in collaborative recommender systems (RDB, BM, CW, RB), pp. 542–547.
KDDKDD-2006-IwataSY #recommendation
Recommendation method for extending subscription periods (TI, KS, TY), pp. 574–579.
KDDKDD-2006-ParkPMGD #recommendation #robust
Naïve filterbots for robust cold-start recommendations (STP, DP, OM, NG, DD), pp. 699–705.
KDDKDD-2006-ZhangCFM #detection #recommendation
Attack detection in time series for recommender systems (SZ, AC, JF, FM), pp. 809–814.
SEKESEKE-2006-CazellaA #architecture #data mining #mining #multi #recommendation #research
An architecture based on multi-agent system and data mining for recommending research papers and researchers (SCC, LOCA), pp. 67–72.
SIGIRSIGIR-2006-SongTLS #data flow #personalisation #recommendation
Personalized recommendation driven by information flow (XS, BLT, CYL, MTS), pp. 509–516.
SIGIRSIGIR-2006-ZhangOFM #analysis #linear #recommendation
Analysis of a low-dimensional linear model under recommendation attacks (SZ, YO, JF, FM), pp. 517–524.
SACSAC-2006-BaragliaLOSS #privacy #recommendation #web
A privacy preserving web recommender system (RB, CL, SO, MS, FS), pp. 559–563.
SACSAC-2006-HessSS #documentation #personalisation #recommendation
Trust-enhanced visibility for personalized document recommendations (CH, KS, CS), pp. 1865–1869.
SACSAC-2006-NikovskiK #induction #personalisation #recommendation
Induction of compact decision trees for personalized recommendation (DN, VK), pp. 575–581.
SACSAC-2006-RothF #information management #recommendation
Trust-decisions on the base of maximal information of recommended direct-trust (UR, VF), pp. 1898–1901.
SACSAC-2006-ZankerG #recommendation
Recommendation-based browsing assistance for corporate knowledge portals (MZ, SG), pp. 1116–1117.
SIGMODSIGMOD-2005-ConsensBTM #benchmark #metric #recommendation
Goals and Benchmarks for Autonomic Configuration Recommenders (MPC, DB, AMT, LM), pp. 239–250.
ICEISICEIS-v4-2005-FoussFKPS #recommendation #web
Web Recommendation System Based on a Markov-Chainmodel (FF, SF, MK, AP, MS), pp. 56–63.
CIKMCIKM-2005-HanK #recommendation
Feature-based recommendation system (EHH, GK), pp. 446–452.
KDDKDD-2005-JinZM #collaboration #recommendation #web
A maximum entropy web recommendation system: combining collaborative and content features (XJ, YZ, BM), pp. 612–617.
SEKESEKE-2005-FelfernigG #development #effectiveness #knowledge-based #process #recommendation
AI Technologies Supporting Effective Development Processes for Knowledge-based Recommender Applications (AF, SG), pp. 372–379.
SEKESEKE-2005-LienT #network #recommendation #web
A Web Pages Recommender with Bayesian Networks (CCL, HLT), pp. 82–87.
SEKESEKE-2005-TsunodaKOMM #collaboration #java #named #recommendation
Javawock: A Java Class Recommender System Based on Collaborative Filtering (MT, TK, NO, AM, KiM), pp. 491–497.
SIGIRSIGIR-2005-CanoKW #music #recommendation
An industrial-strength content-based music recommendation system (PC, MK, NW), p. 673.
SACSAC-2005-AvesaniMT #recommendation
A trust-enhanced recommender system application: Moleskiing (PA, PM, RT), pp. 1589–1593.
SACSAC-2005-Moloney #distributed #network #pervasive #recommendation #simulation
Simulation of a distributed recommendation system for pervasive networks (SM), pp. 1577–1581.
ESEC-FSEESEC-FSE-2005-HolmesWM #recommendation
Strathcona example recommendation tool (RH, RJW, GCM), pp. 237–240.
ICSEICSE-2005-HolmesM #recommendation #source code #using
Using structural context to recommend source code examples (RH, GCM), pp. 117–125.
VLDBVLDB-2004-ThorR #adaptation #named #recommendation
AWESOME — A Data Warehouse-based System for Adaptive Website Recommendations (AT, ER), pp. 384–395.
ICEISICEIS-v2-2004-BaykalAP #automation #reasoning #recommendation
Automated Product Recommendation by Employing Case-Based Reasoning Agents (MÖB, RA, FP), pp. 515–518.
ICEISICEIS-v3-2004-MontanerLR #evaluation #recommendation
Evaluation of Recommender Systems Through Simulated Users (MM, BL, JLdlR), pp. 303–308.
ICEISICEIS-v4-2004-DegemmisLSCLG #collaboration #hybrid #recommendation
A Hybrid Collaborative Recommender System Based on User Profiles (MD, PL, GS, MFC, OL, SG), pp. 162–169.
ICEISICEIS-v4-2004-KaracapilidisL #framework #online #recommendation
A Recommendation Based Framework for Online Product Configuration (NIK, TL), pp. 303–308.
ICEISICEIS-v4-2004-LohGLBRSAP #chat #library #recommendation #web
Analyzing Web Chat Messages for Recommending Items from a Digital Library (SL, RSG, DL, TB, RR, GS, LA, TP), pp. 41–48.
ICEISICEIS-v5-2004-GonzalezLR #modelling #recommendation
Managing Emotions in Smart User Models for Recommender Systems (GG, BL, JLdlR), pp. 187–194.
ICEISICEIS-v5-2004-PluAVM #recommendation #social #social media
A Contact Recommender System for a Mediated Social Media (MP, LA, LV, JCM), pp. 107–114.
CIKMCIKM-2004-JungHWH #named #recommendation
SERF: integrating human recommendations with search (SJ, KH, JW, JLH), pp. 571–580.
CIKMCIKM-2004-ZieglerLS #recommendation
Taxonomy-driven computation of product recommendations (CNZ, GL, LST), pp. 406–415.
KDDKDD-2004-AliS #architecture #collaboration #distributed #named #recommendation #using
TiVo: making show recommendations using a distributed collaborative filtering architecture (KA, WvS), pp. 394–401.
SIGIRSIGIR-2004-LiKGO #music #recommendation
A music recommender based on audio features (QL, BMK, DG, DwO), pp. 532–533.
SIGIRSIGIR-2004-UpstillR #recommendation #web
Exploiting hyperlink recommendation evidence in navigational web search (TU, SER), pp. 576–577.
SACSAC-2004-StraubH #mobile #recommendation
An anonymous bonus point system for mobile commerce based on word-of-mouth recommendation (TS, AH), pp. 766–773.
HTHT-2003-MacedoTCP #automation #case study #experience #recommendation #web
Automatically sharing web experiences through a hyperdocument recommender system (AAM, KNT, JACG, MdGCP), pp. 48–56.
CHICHI-2003-CosleyLAKR #how #interface #recommendation
Is seeing believing?: how recommender system interfaces affect users’ opinions (DC, SKL, IA, JAK, JR), pp. 585–592.
CHICHI-2003-McDonald #collaboration #comparative #evaluation #network #recommendation #social
Recommending collaboration with social networks: a comparative evaluation (DWM), pp. 593–600.
ICEISICEIS-v2-2003-WangRLC #mining #online #realtime #recommendation #web
Mining Web Usage Data for Real-Time Online Recommendation (MW, SJR, SYL, JKYC), pp. 575–578.
ICEISICEIS-v4-2003-LiKM #architecture #automation #e-commerce #multi #recommendation
Multi-Agent Architecture for Automatic Recommendation System in E-Commerce (QL, RK, YM), pp. 265–270.
ECIRECIR-2003-TianC #collaboration #learning #rating #recommendation #similarity
Learning User Similarity and Rating Style for Collaborative Recommendation (LFT, KWC), pp. 135–145.
KDDKDD-2003-Kamishima #collaboration #order #recommendation
Nantonac collaborative filtering: recommendation based on order responses (TK), pp. 583–588.
ICSEICSE-2003-CubranicM #development #named #recommendation
Hipikat: Recommending Pertinent Software Development Artifacts (DC, GCM), pp. 408–418.
VLDBVLDB-2002-CosleyLP #framework #named #recommendation #testing #using
REFEREE: An Open Framework for Practical Testing of Recommender Systems using ResearchIndex (DC, SL, DMP), pp. 35–46.
STOCSTOC-2002-DrineasKR #recommendation
Competitive recommendation systems (PD, IK, PR), pp. 82–90.
CSCWCSCW-2002-McNeeACGLRKR #on the #recommendation #research
On the recommending of citations for research papers (SMM, IA, DC, PG, SKL, AMR, JAK, JR), pp. 116–125.
CAiSECAiSE-2002-PapadopoulosP #community #recommendation #semantics
The Role of Semantic Relevance in Dynamic User Community Management and the Formulation of Recommendations (NP, DP), pp. 200–215.
CIKMCIKM-2002-SchaferKR #integration #recommendation
Meta-recommendation systems: user-controlled integration of diverse recommendations (JBS, JAK, JR), pp. 43–51.
SIGIRSIGIR-2002-ScheinPUP #metric #recommendation
Methods and metrics for cold-start recommendations (AIS, AP, LHU, DMP), pp. 253–260.
CIKMCIKM-2001-ChenC #music #recommendation
A Music Recommendation System Based on Music Data Grouping and User Interests (HCC, ALPC), pp. 231–238.
CIKMCIKM-2001-Karypis #algorithm #evaluation #recommendation
Evaluation of Item-Based Top-N Recommendation Algorithms (GK), pp. 247–254.
ICMLICML-2001-Lee #collaboration #learning #recommendation
Collaborative Learning and Recommender Systems (WSL), pp. 314–321.
KDDKDD-2001-Riedl #community #recommendation
Recommender systems in commerce and community (JR), p. 15.
CHICHI-2000-WoodruffGPCC #recommendation
Enhancing a digital book with a reading recommender (AW, RG, JEP, EHhC, SKC), pp. 153–160.
CSCWCSCW-2000-HerlockerKR #collaboration #recommendation
Explaining collaborative filtering recommendations (JLH, JAK, JR), pp. 241–250.
CSCWCSCW-2000-McDonaldA #architecture #flexibility #recommendation
Expertise recommender: a flexible recommendation system and architecture (DWM, MSA), pp. 231–240.
CIKMCIKM-2000-LavrenkoSLOJA #modelling #recommendation
Language Models for Financial News Recommendation (VL, MDS, DL, PO, DJ, JA), pp. 389–396.
KDDKDD-2000-KittsFV #independence #named #performance #recommendation
Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities (BK, DF, MV), pp. 437–446.
CSEETCSEET-1999-Tockey #recommendation
Recommended Skills and Knowledge for Software Engineers (SRT), pp. 168–176.
HCIHCI-CCAD-1999-TrousseJK #approach #behaviour #recommendation #similarity #using
Using user behaviour similarity for recommendation computation: the broadway approach (BT, MJ, RK), pp. 85–89.
ITiCSEITiCSE-WGR-1997-JoyceKGKKLLSW #design #guidelines #recommendation #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.
CHICHI-1995-HillSRF #community #recommendation
Recommending and Evaluating Choices in a Virtual Community of Use (WCH, LS, MR, GWF), pp. 194–201.
AdaEuropeAdaEurope-1994-Gale #ada #development #recommendation
Recommendations and Proposals for an Ada Strategy in the Space Software Development Environment (LPG), pp. 175–203.
CSEETSEI-1992-MedairosCCK #re-engineering #recommendation
Software Engineering Course Projects: Failures and Recommendations (SM, KWC, JSC, MK), pp. 324–338.
CHICHI-1992-WhartonBJF #case study #experience #recommendation #user interface
Applying cognitive walkthroughs to more complex user interfaces: experiences, issues, and recommendations (CW, JB, RJ, MF), pp. 381–388.
SIGIRSIGIR-1983-Somers #analysis #file system #recommendation #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.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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