BibSLEIGH — Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, Jeffrey Xu Yu
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
CIKM, 2019.

KER

@proceedings{CIKM-2019,
	acmid         = "3357384",
	editor        = "Wenwu Zhu and Dacheng Tao and Xueqi Cheng and Peng Cui and Elke A. Rundensteiner and David Carmel and Qi He and Jeffrey Xu Yu",
	isbn          = "978-1-4503-6976-3",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 28th ACM International Conference on Information and Knowledge Management}",
	year          = 2019,
}

Contents (394 items)

CIKM-2019-Shi #towards: Autonomous Driving Towards Mass Production (JS), p. 1.
CIKM-2019-Maybank #metric: The Fisher-Rao Metric in Computer Vision (SJM), p. 3.
CIKM-2019-Han #automation #multi #text-to-text: From Unstructured Text to TextCube: Automated Construction and Multidimensional Exploration (JH), pp. 5–6.
CIKM-2019-Pei: Practicing the Art of Data Science (JP), p. 7.
CIKM-2019-KoLLLY #on the #query: On VR Spatial Query for Dual Entangled Worlds (SHK, YCL, HCL, WCL, DNY), pp. 9–18.
CIKM-2019-DuongRN #multi #sketching #streaming #using: Sketching Streaming Histogram Elements using Multiple Weighted Factors (QHD, HR, KN), pp. 19–28.
CIKM-2019-BrisaboaCBN #string: Improved Compressed String Dictionaries (NRB, ACP, GdB, GN), pp. 29–38.
CIKM-2019-0003RMMD #on the: On Transforming Relevance Scales (LH0, KR, EM, SM, GD), pp. 39–48.
CIKM-2019-YangSC #clustering: Streamline Density Peak Clustering for Practical Adoptions (SY, XS, MC), pp. 49–58.
CIKM-2019-ZhangSTZLAZW0Y #on-demand #platform #recommendation: Recommendation-based Team Formation for On-demand Taxi-calling Platforms (LZ, TS, YT, ZZ, DL, WA, LZ, GW, YL0, JY), pp. 59–68.
CIKM-2019-FuL #estimation #named #network: DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation (TYF, WCL), pp. 69–78.
CIKM-2019-SuCCZ0 #personalisation: Personalized Route Description Based On Historical Trajectories (HS, GC, WC, BZ, KZ0), pp. 79–88.
CIKM-2019-IzbickiPT #twitter: Geolocating Tweets in any Language at any Location (MI, VP, VJT), pp. 89–98.
CIKM-2019-SiddiqueeAM #detection #named: SeiSMo: Semi-supervised Time Series Motif Discovery for Seismic Signal Detection (MAS, ZA, AM), pp. 99–108.
CIKM-2019-TanMYYDWTYWCCY #named #network #predict #risk management: UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction (QT, AJM, MY, BY, HD, VWSW, YKT, TCFY, GLHW, JYLC, FKLC, PCY), pp. 109–118.
CIKM-2019-HanMNKURNS #detection #image #learning #using: Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (CH, KM, TN, YK, FU, LR, HN, SS), pp. 119–127.
CIKM-2019-LiQWZCZn #classification #visual notation: Domain Knowledge Guided Deep Atrial Fibrillation Classification and Its Visual Interpretation (XL, BQ, JW, XZ, SC, QZ), pp. 129–138.
CIKM-2019-QiuW0 #multi #predict #problem: Question Difficulty Prediction for Multiple Choice Problems in Medical Exams (ZQ, XW, WF0), pp. 139–148.
CIKM-2019-ZhaoSSW #graph #learning #named #precise #retrieval: GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment (SZ, CS, AS, FW), pp. 149–158.
CIKM-2019-WangZWYZL #multi #recognition: Video-level Multi-model Fusion for Action Recognition (XW, JZ, LW, PSY, JZ, HL), pp. 159–168.
CIKM-2019-BoiarovT #learning #metric #recognition #scalability: Large Scale Landmark Recognition via Deep Metric Learning (AB, ET), pp. 169–178.
CIKM-2019-GuoAWPC00 #classification #multi #recognition: Multi-stage Deep Classifier Cascades for Open World Recognition (XG, AAF, LW, HP, XC0, KZ0, LZ0), pp. 179–188.
CIKM-2019-ShahVLFLTJS #classification #image #multimodal: Inferring Context from Pixels for Multimodal Image Classification (MS, KV, CTL, AF, ZL, AT, CJ, CS), pp. 189–198.
CIKM-2019-WangSGYZF #multi #semantics: Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features (MW, DS, NG, WY, TZ, ZF), pp. 199–208.
CIKM-2019-0002LG #data type #performance #semistructured data: Efficient Join Processing Over Incomplete Data Streams (WR0, XL, KG), pp. 209–218.
CIKM-2019-DurschSWFFSBHJP #algorithm #dependence #evaluation: Inclusion Dependency Discovery: An Experimental Evaluation of Thirteen Algorithms (FD, AS, FW, MF, TF, NS, TB, HH, LJ, TP, FN), pp. 219–228.
CIKM-2019-WangKGS #database #web: Constructing a Comprehensive Events Database from the Web (QW, BK, VG, DS), pp. 229–238.
CIKM-2019-ChengHLWLC #memory management: Deploying Hash Tables on Die-Stacked High Bandwidth Memory (XC, BH, EL, WW, SL, XC), pp. 239–248.
CIKM-2019-LiWWLYLW #learning #multi #platform: Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage (CL, SW, HW, YL, PSY, ZL, WW), pp. 249–258.
CIKM-2019-CaoZXPY #adaptation #classification #consistency #image #semantics: Adversarial Domain Adaptation with Semantic Consistency for Cross-Domain Image Classification (MC, XZ, YX, YP, BY), pp. 259–268.
CIKM-2019-PratamaCXL0 #atl #information management #named #process #streaming: ATL: Autonomous Knowledge Transfer from Many Streaming Processes (MP, MdC, RX, EL, JL0), pp. 269–278.
CIKM-2019-WeiK #information management #multi: Knowledge Transfer based on Multiple Manifolds Assumption (PW, YK), pp. 279–287.
CIKM-2019-JiangWZSLL #detection #graph #learning #representation: Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning (ZJ, JW, LZ, CS, YL, XL), pp. 289–298.
CIKM-2019-QiLDCQD #e-commerce #framework: A Deep Neural Framework for Sales Forecasting in E-Commerce (YQ, CL, HD, MC, YQ, YD), pp. 299–308.
CIKM-2019-YangTDZLL #framework #query #semantics: An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine (YY, JT, HD, ZZ, YL, XL), pp. 309–318.
CIKM-2019-ZhaoZXQJ0 #named #predict: AIBox: CTR Prediction Model Training on a Single Node (WZ, JZ, DX, YQ, RJ, PL0), pp. 319–328.
CIKM-2019-YuanHYZCDL #predict: Improving Ad Click Prediction by Considering Non-displayed Events (BWY, JYH, MYY, HZ, CYC, ZD, CJL), pp. 329–338.
CIKM-2019-Lakhotia0 #algorithm #approximate #coordination #network #social: Approximation Algorithms for Coordinating Ad Campaigns on Social Networks (KL, DK0), pp. 339–348.
CIKM-2019-WangZDSZHYB #predict: Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction (YW, LZ, QD, FS, BZ, YH, WY, YB), pp. 349–358.
CIKM-2019-BiAZC #feedback: Conversational Product Search Based on Negative Feedback (KB, QA, YZ, WBC), pp. 359–368.
CIKM-2019-ZouK #learning: Learning to Ask: Question-based Sequential Bayesian Product Search (JZ, EK), pp. 369–378.
CIKM-2019-AiHVC #personalisation: A Zero Attention Model for Personalized Product Search (QA, DNH, SVNV, WBC), pp. 379–388.
CIKM-2019-EladGNKR #learning #personalisation: Learning to Generate Personalized Product Descriptions (GE, IG, SN, BK, KR), pp. 389–398.
CIKM-2019-ChenSTCS #network #performance #random: Fast and Accurate Network Embeddings via Very Sparse Random Projection (HC, SFS, YT, MC, SS), pp. 399–408.
CIKM-2019-LongWDSJL #approach #community #network: Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach (QL, YW, LD, GS, YJ, WL), pp. 409–418.
CIKM-2019-JiaoXZZ #graph #network #predict: Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention (YJ, YX, JZ, YZ), pp. 419–428.
CIKM-2019-Wang0T00 #network: Discerning Edge Influence for Network Embedding (YW, YY0, HT, FX0, JL0), pp. 429–438.
CIKM-2019-LuoLM #community #profiling: Constrained Co-embedding Model for User Profiling in Question Answering Communities (YL, SL, ZM), pp. 439–448.
CIKM-2019-00090S #learning #representation: Hyper-Path-Based Representation Learning for Hyper-Networks (JH0, XL0, YS), pp. 449–458.
CIKM-2019-LiZWHYL #multi #network: Multi-Hot Compact Network Embedding (CL, LZ, SW, FH, PSY, ZL), pp. 459–468.
CIKM-2019-LuWSYY #network: Temporal Network Embedding with Micro- and Macro-dynamics (YL, XW0, CS, PSY, YY), pp. 469–478.
CIKM-2019-DuT #multi #named: MrMine: Multi-resolution Multi-network Embedding (BD, HT), pp. 479–488.
CIKM-2019-ParkKZ0Y #network: Task-Guided Pair Embedding in Heterogeneous Network (CP, DK, QZ, JH0, HY), pp. 489–498.
CIKM-2019-LeeRKKKR #graph #network: Graph Convolutional Networks with Motif-based Attention (JBL, RAR, XK, SK, EK, AR), pp. 499–508.
CIKM-2019-LiGLCYN #graph #hashtag #network #recommendation: Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network (ML, TG, ML, ZC, JY, LN), pp. 509–518.
CIKM-2019-ZhaoCXLZ0 #classification #graph: Hashing Graph Convolution for Node Classification (WZ, ZC, CX, CL, TZ0, JY0), pp. 519–528.
CIKM-2019-XuLHLX0 #e-commerce #graph #network #recommendation #social: Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation (FX, JL, ZH, YL0, YX, XX0), pp. 529–538.
CIKM-2019-LiCWZW #feature model #graph #interactive #modelling #named #network #predict: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction (ZL, ZC, SW, XZ, LW0), pp. 539–548.
CIKM-2019-ZhangFYZS #framework #identification #network: Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework (YZ, YF, YY, LZ, CS), pp. 549–558.
CIKM-2019-FanZDCSL #approach #graph #identification #learning #network #novel: Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach (CF, LZ, YD, MC, YS, ZL), pp. 559–568.
CIKM-2019-DongZHSL #detection #graph #multi #network: Multiple Rumor Source Detection with Graph Convolutional Networks (MD, BZ, NQVH, HS, GL), pp. 569–578.
CIKM-2019-QiuLHY #graph #network #order #recommendation: Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (RQ, JL, ZH, HY), pp. 579–588.
CIKM-2019-SalhaLHTV #graph #predict: Gravity-Inspired Graph Autoencoders for Directed Link Prediction (GS, SL, RH, VAT, MV), pp. 589–598.
CIKM-2019-ShiSLZHLZW00 #network: Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity (YS, JS, YL, NZ, XH, ZL, QZ, MW, MK0, JH0), pp. 599–608.
CIKM-2019-HouFZYLWWXS #android #detection #graph #named #robust: αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model (SH, YF, YZ, YY, JL, WW, JW, QX, FS), pp. 609–618.
CIKM-2019-LeePY #named #network: BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network (SL, CP, HY), pp. 619–628.
CIKM-2019-NieHHSCZWK: Deep Sequence-to-Sequence Entity Matching for Heterogeneous Entity Resolution (HN, XH, BH, LS, BC, WZ, SW, HK), pp. 629–638.
CIKM-2019-HeSLJPP #named #network #random: HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding (YH, YS, JL, CJ, JP, HP), pp. 639–648.
CIKM-2019-XieXYZ #graph #multi: EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation (XX, YX, PSY, YZ), pp. 649–658.
CIKM-2019-QuHOZL #fine-grained: A Fine-grained and Noise-aware Method for Neural Relation Extraction (JQ, WH, DO, XZ0, XL), pp. 659–668.
CIKM-2019-JinOLLLC #graph #learning #semantics #similarity: Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations (XJ, BO, SL, DL, KHL, LC), pp. 669–678.
CIKM-2019-YeWYJZXY #behaviour #graph #network #representation: Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks (YY, XW, JY, KJ, JZ, YX, HY), pp. 679–688.
CIKM-2019-HuynhP #algorithm #benchmark #knowledge base #metric: A Benchmark for Fact Checking Algorithms Built on Knowledge Bases (VPH, PP), pp. 689–698.
CIKM-2019-BhutaniJ #knowledge base #online #query: Online Schemaless Querying of Heterogeneous Open Knowledge Bases (NB, HVJ), pp. 699–708.
CIKM-2019-ZhengZ #modelling: Enhancing Conversational Dialogue Models with Grounded Knowledge (WZ, KZ), pp. 709–718.
CIKM-2019-DengLSDFYL #approach #multi #named: MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data (YD, YL, YS, ND, WF, MY0, KL), pp. 719–728.
CIKM-2019-ChristmannRASW #graph #using: Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion (PC, RSR, AA, JS, GW), pp. 729–738.
CIKM-2019-BhutaniZJ #composition #knowledge base #learning #query: Learning to Answer Complex Questions over Knowledge Bases with Query Composition (NB, XZ, HVJ), pp. 739–748.
CIKM-2019-0006B #relational: Auto-completion for Data Cells in Relational Tables (SZ0, KB), pp. 761–770.
CIKM-2019-ZhengSKY #clique #identification #set: Author Set Identification via Quasi-Clique Discovery (YZ, CS, XK, YY), pp. 771–780.
CIKM-2019-IosifidisN #adaptation #cumulative #named: AdaFair: Cumulative Fairness Adaptive Boosting (VI, EN), pp. 781–790.
CIKM-2019-XuZL #incremental #kernel #online #predict: New Online Kernel Ridge Regression via Incremental Predictive Sampling (SX, XZ, SL), pp. 791–800.
CIKM-2019-LiaoZ #kernel #online #sketching: Online Kernel Selection via Tensor Sketching (SL, XZ), pp. 801–810.
CIKM-2019-TrittenbachB #detection #learning #multi: One-Class Active Learning for Outlier Detection with Multiple Subspaces (HT, KB), pp. 811–820.
CIKM-2019-Cohen-ShapiraRS #dataset #named #recommendation #representation #visual notation: AutoGRD: Model Recommendation Through Graphical Dataset Representation (NCS, LR, BS, GK, RV), pp. 821–830.
CIKM-2019-TanYHD #learning #multi #segmentation #semantics: Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection (YT, LY, QH, ZD), pp. 831–840.
CIKM-2019-RekatsinasDP #adaptation #crowdsourcing #named #performance #query: CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction (TR, AD, AGP), pp. 841–850.
CIKM-2019-ZhangKXL #fine-grained: Deep Forest with LRRS Feature for Fine-grained Website Fingerprinting with Encrypted SSL/TLS (ZZ, CK, GX, ZL0), pp. 851–860.
CIKM-2019-KangT #mining #named #network: N2N: Network Derivative Mining (JK, HT), pp. 861–870.
CIKM-2019-LiuNX0Y #framework #named #self: MoBoost: A Self-improvement Framework for Linear-based Hashing (XL, XN, XX, LZ0, YY), pp. 871–880.
CIKM-2019-LiuWSL #learning: Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning (XL, BW, FS, HL), pp. 881–890.
CIKM-2019-AliARR #identification #named: EPA: Exoneration and Prominence based Age for Infection Source Identification (SSA, TA, AR, SAMR), pp. 891–900.
CIKM-2019-LiuD0SGWZRXCM #generative #persuasion #visual notation: Generating Persuasive Visual Storylines for Promotional Videos (CL0, YD, HY0, ZS, ZG, PW, CZ, PR, XX, LC, CM), pp. 901–910.
CIKM-2019-MarinR #clustering #programming #semantics: Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments (VJM, CRR), pp. 911–920.
CIKM-2019-0004RG #microblog #summary: Going Beyond Content Richness: Verified Information Aware Summarization of Crisis-Related Microblogs (AS0, KR, NG), pp. 921–930.
CIKM-2019-AmsterdamerMSY #constraints #declarative: Declarative User Selection with Soft Constraints (YA, TM, AS, BY), pp. 931–940.
CIKM-2019-SinhaMSMS0 #approach #identification #multi #twitter: #suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter (PPS, RM, RS, DM, RRS, HL0), pp. 941–950.
CIKM-2019-JinCCHV #interactive #music #named #recommendation: MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction (YJ, WC, LC, NNH, KV), pp. 951–960.
CIKM-2019-BonchiGGOR #community #network: Discovering Polarized Communities in Signed Networks (FB, EG, AG, BO, GR), pp. 961–970.
CIKM-2019-XiaoGJLCZY #modelling: Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing (SX, LG, ZJ, LL, YC, JZ, SY), pp. 971–980.
CIKM-2019-KaghazgaranAC #empirical #overview: Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures (PK, MA, JC), pp. 981–990.
CIKM-2019-RizosHS #classification #learning: Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification (GR, KH, BWS), pp. 991–1000.
CIKM-2019-CaoCL0 #network: Nested Relation Extraction with Iterative Neural Network (YC, DC0, HL0, PL0), pp. 1001–1010.
CIKM-2019-ZhangLZZLWCZ #learning #word: Learning Chinese Word Embeddings from Stroke, Structure and Pinyin of Characters (YZ, YL, JZ, ZZ, XL, WW, ZC, SZ), pp. 1011–1020.
CIKM-2019-ChenLX0 #classification #network #sentiment: Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification (SC, XL, YX, LH0), pp. 1021–1030.
CIKM-2019-YinLW #analysis #interactive #multi #sentiment: Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis (DY, XL, XW0), pp. 1031–1040.
CIKM-2019-WangA0 #documentation #word: Beyond word2vec: Distance-graph Tensor Factorization for Word and Document Embeddings (SW, CCA, HL0), pp. 1041–1050.
CIKM-2019-HuangCLCHLZZW #approach #classification #multi #network: Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach (WH, EC, QL0, YC, ZH, YL, ZZ, DZ, SW), pp. 1051–1060.
CIKM-2019-IslamLL00 #classification #random #semantics: A Semantics Aware Random Forest for Text Classification (MZI, JL, JL, LL0, WK0), pp. 1061–1070.
CIKM-2019-JiangSTWZXY #modelling #topic: Federated Topic Modeling (DJ, YS, YT, XW0, WZ, QX, QY), pp. 1071–1080.
CIKM-2019-WangWC #multi #network: Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network (HW, ZW, JC), pp. 1081–1090.
CIKM-2019-WuWLH0 #classification #sentiment: Sentiment Lexicon Enhanced Neural Sentiment Classification (CW, FW, JL, YH, XX0), pp. 1091–1100.
CIKM-2019-LuoZWZ #framework #learning #named #representation: ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning (YL, HZ, YW, XZ), pp. 1101–1110.
CIKM-2019-YaoHGH #low level #network: Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method (SY, YH, LG, ZH), pp. 1111–1120.
CIKM-2019-ChenSHG #detection #network: Attention-Residual Network with CNN for Rumor Detection (YC, JS, LH, WG), pp. 1121–1130.
CIKM-2019-YanLWLWZG #classification #image #multi #random #using: Imbalance Rectification in Deep Logistic Regression for Multi-Label Image Classification Using Random Noise Samples (WY, RL, JW, YL, JW, PZ, XG), pp. 1131–1140.
CIKM-2019-WangWLL #named #network: CamDrop: A New Explanation of Dropout and A Guided Regularization Method for Deep Neural Networks (HW, GW, GL, LL), pp. 1141–1149.
CIKM-2019-XiaoLM #collaboration #learning: Dynamic Collaborative Recurrent Learning (TX, SL, ZM), pp. 1151–1160.
CIKM-2019-SongS0DX0T #automation #feature model #interactive #learning #named #network #self: AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (WS, CS, ZX0, ZD, YX, MZ0, JT), pp. 1161–1170.
CIKM-2019-PratamaZAO0 #automation #multi #network #streaming: Automatic Construction of Multi-layer Perceptron Network from Streaming Examples (MP, CZ, AA, YSO, WD0), pp. 1171–1180.
CIKM-2019-ZhangTXZ #clustering #embedded #robust: Robust Embedded Deep K-means Clustering (RZ0, HT, YX, YZ), pp. 1181–1190.
CIKM-2019-AdriaensABGL #graph: Discovering Interesting Cycles in Directed Graphs (FA, ÇA, TDB, AG, JL), pp. 1191–1200.
CIKM-2019-Sanei-MehriZST #estimation #graph #named: FLEET: Butterfly Estimation from a Bipartite Graph Stream (SVSM, YZ, AES, ST), pp. 1201–1210.
CIKM-2019-Zhang00QZL #clique: Selecting the Optimal Groups: Efficiently Computing Skyline k-Cliques (CZ0, WZ0, YZ0, LQ, FZ0, XL0), pp. 1211–1220.
CIKM-2019-DerrJCT #network: Balance in Signed Bipartite Networks (TD, CJ, YC, JT), pp. 1221–1230.
CIKM-2019-ChehreghaniBA #adaptation #algorithm: Adaptive Algorithms for Estimating Betweenness and k-path Centralities (MHC, AB, TA), pp. 1231–1240.
CIKM-2019-YangJGZL #detection #interactive #online: Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments (WY, WJ, WG, XZ, YL), pp. 1241–1250.
CIKM-2019-GongZ00XWH #community #detection #developer #learning #online #using: Detecting Malicious Accounts in Online Developer Communities Using Deep Learning (QG, JZ, YC0, QL0, YX, XW, PH), pp. 1251–1260.
CIKM-2019-HuangLZYCGH #education #multi #online #recommendation: Exploring Multi-Objective Exercise Recommendations in Online Education Systems (ZH, QL0, CZ, YY, EC, WG, GH), pp. 1261–1270.
CIKM-2019-Dutta0KMM0 #modelling #online: Into the Battlefield: Quantifying and Modeling Intra-community Conflicts in Online Discussion (SD, DD0, GK, SM, AM, TC0), pp. 1271–1280.
CIKM-2019-ChoiAA #online #predict: Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems (JIC, AA, EA), pp. 1281–1290.
CIKM-2019-MaZLH0J #collaboration #data analysis #health #privacy: Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (JM, QZ, JL, JCH, LX0, XJ), pp. 1291–1300.
CIKM-2019-TangFXLH #privacy: Achieve Privacy-Preserving Truth Discovery in Crowdsensing Systems (JT, SF, MX, YL, KH), pp. 1301–1310.
CIKM-2019-WangZYLYRS #privacy: Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas (TW, JZ, HY, JL, XY, XR, SS), pp. 1311–1320.
CIKM-2019-BiswasGRB #approximate #clustering #privacy: Privacy Preserving Approximate K-means Clustering (CB, DG, DR, UB), pp. 1321–1330.
CIKM-2019-Zhang0LFW #privacy: Practical Access Pattern Privacy by Combining PIR and Oblivious Shuffle (ZZ, KW0, WL, AWCF, RCWW), pp. 1331–1340.
CIKM-2019-0005HQQGCLSL #hybrid: A Hybrid Retrieval-Generation Neural Conversation Model (LY0, JH, MQ, CQ, JG, WBC, XL, YS, JL), pp. 1341–1350.
CIKM-2019-ZhengWWW: A Latent-Constrained Variational Neural Dialogue Model for Information-Rich Responses (YZ, YW, LW, JW), pp. 1351–1360.
CIKM-2019-DuanZYZLWWZS0 #learning #mining #multi #summary: Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning (XD, YZ, LY, XZ, XL, TW, RW, QZ, CS, FW0), pp. 1361–1370.
CIKM-2019-AhmadvandSCA #classification #named #topic: ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents (AA, HS, JIC, EA), pp. 1371–1380.
CIKM-2019-Zhang0H #feedback #interactive: An Interactive Mechanism to Improve Question Answering Systems via Feedback (XZ, LZ0, SH), pp. 1381–1390.
CIKM-2019-QuYQZCCI: Attentive History Selection for Conversational Question Answering (CQ, LY0, MQ, YZ, CC, WBC, MI), pp. 1391–1400.
CIKM-2019-0002LMGZZH #automation #chat #generative #interactive: Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction (WW0, JL, XM, GG, FZ0, PZ, YH), pp. 1401–1410.
CIKM-2019-RomeroRPPSW #query: Commonsense Properties from Query Logs and Question Answering Forums (JR, SR, KP, JZP, AS, GW), pp. 1411–1420.
CIKM-2019-SrivastavaLF #adaptation #community #modelling #multimodal #platform #visual notation: Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms (AS, HWL, SF), pp. 1421–1430.
CIKM-2019-VakulenkoGPRC #graph #message passing: Message Passing for Complex Question Answering over Knowledge Graphs (SV, JDFG, AP, MdR, MC), pp. 1431–1440.
CIKM-2019-SunLWPLOJ #bidirectional #named #recommendation: BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (FS, JL, JW, CP, XL, WO, PJ), pp. 1441–1450.
CIKM-2019-ShiZYZHLM #adaptation #recommendation: Adaptive Feature Sampling for Recommendation with Missing Content Feature Values (SS, MZ0, XY, YZ, BH, YL, SM), pp. 1451–1460.
CIKM-2019-ChenCCR #network #recommendation: A Dynamic Co-attention Network for Session-based Recommendation (WC, FC, HC, MdR), pp. 1461–1470.
CIKM-2019-YouVLL #multi #network #recommendation: Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation (DY, NV, KL, QL), pp. 1471–1480.
CIKM-2019-HeWNC #recommendation #self: A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists (YH, JW, WN, JC), pp. 1481–1490.
CIKM-2019-LiJC0W #approach #hybrid #named #recommendation: HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity (XL, WJ, WC, JW0, GW0), pp. 1503–1512.
CIKM-2019-MaWZLLCYT0 #named #recommendation: DBRec: Dual-Bridging Recommendation via Discovering Latent Groups (JM, JW, MZ, LL, CL, WC, YY, HT, XL0), pp. 1513–1522.
CIKM-2019-KangM #generative #recommendation #scalability: Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation (WCK, JJM), pp. 1523–1532.
CIKM-2019-ZhuC0LZ #framework #named #recommendation: DTCDR: A Framework for Dual-Target Cross-Domain Recommendation (FZ, CC, YW0, GL, XZ), pp. 1533–1542.
CIKM-2019-KangPKCC #modelling #recommendation #topic #using: Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling (KK, JP, WK, HC, JC), pp. 1543–1552.
CIKM-2019-XueJLWZT #on-demand #recommendation: A Spatio-temporal Recommender System for On-demand Cinemas (TX, BJ, BL, WW0, QZ, ST), pp. 1553–1562.
CIKM-2019-KangHLY #learning #recommendation: Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users (SK, JH, DL, HY), pp. 1563–1572.
CIKM-2019-XiaWDZCC #recommendation: Leveraging Ratings and Reviews with Gating Mechanism for Recommendation (HX, ZW, BD, LZ, SC, GC), pp. 1573–1582.
CIKM-2019-ZhangC #recommendation #visual notation: Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence (YZ, JC), pp. 1583–1592.
CIKM-2019-SunQYCCC #question #what: What Can History Tell Us? (KS, TQ, HY, TC, YC, LC0), pp. 1593–1602.
CIKM-2019-ZhangMLZ0MXT #learning #ranking: Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning (JZ, JM, YL, RZ, MZ0, SM, JX0, QT), pp. 1603–1612.
CIKM-2019-ShiLLP #multi #network: A Multi-Scale Temporal Feature Aggregation Convolutional Neural Network for Portfolio Management (SS, JL, GL, PP), pp. 1613–1622.
CIKM-2019-WangRCR0R #graph #learning #predict: Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning (SW, PR, ZC, ZR, JM0, MdR), pp. 1623–1632.
CIKM-2019-LuYGWLC #clustering #learning #realtime: Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding (JL, CY, XG, LW, CL, GC), pp. 1633–1641.
CIKM-2019-LiuZYCY #generative #learning #refinement: Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System (YL, CZ, XY, YC, PSY), pp. 1643–1652.
CIKM-2019-Pothirattanachaikul #behaviour #documentation: Analyzing the Effects of Document's Opinion and Credibility on Search Behaviors and Belief Dynamics (SP, TY, YY, MY), pp. 1653–1662.
CIKM-2019-SrinivasanRSG #identification: Identifying Facet Mismatches In Search Via Micrographs (SS, NSR, KS, LG), pp. 1663–1672.
CIKM-2019-ZhangH #multi #named #nearest neighbour: GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine (MZ, YH), pp. 1673–1682.
CIKM-2019-XieMLRAHZM #image #information management #web: Improving Web Image Search with Contextual Information (XX, JM, YL, MdR, QA, YH, MZ0, SM), pp. 1683–1692.
CIKM-2019-XiaoRMSL #learning #metric #personalisation: Dynamic Bayesian Metric Learning for Personalized Product Search (TX, JR, ZM, HS, SL), pp. 1693–1702.
CIKM-2019-WangLLW #predict #towards: Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor (JW, QL0, ZL, SW), pp. 1703–1712.
CIKM-2019-YuanLZW #classification: Locally Slope-based Dynamic Time Warping for Time Series Classification (JY, QL, WZ, ZW), pp. 1713–1722.
CIKM-2019-CaoZSX #modelling #named #network #sequence: HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling (YC, WZ, BS, CX), pp. 1723–1732.
CIKM-2019-KawabataMS #automation #data type #mining: Automatic Sequential Pattern Mining in Data Streams (KK, YM, YS), pp. 1733–1742.
CIKM-2019-Wang0CRR #algorithm #higher-order #parallel #performance: Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra (ZW, AAM0, XC, NR, SR), pp. 1743–1752.
CIKM-2019-KrishnanCTS #approach #composition #recommendation #social: A Modular Adversarial Approach to Social Recommendation (AK, HC, TC0, HS), pp. 1753–1762.
CIKM-2019-WangJLHMD #community #mining #network #sentiment #social: Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities (XW, DJ, ML, DH, KM, JD), pp. 1763–1772.
CIKM-2019-LaiSYHLY #multi #recommendation: Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization (HCL, HHS, DNY, JLH, WCL, PSY), pp. 1773–1782.
CIKM-2019-0001WJPC #fault: Tracking Top-k Influential Users with Relative Errors (YY0, ZW, TJ, JP, EC), pp. 1783–1792.
CIKM-2019-IslamMR #graph #named #network #predict #social #using: NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network (MRI, SM, NR), pp. 1793–1802.
CIKM-2019-LiuWJYZZ #named #recommendation: In2Rec: Influence-based Interpretable Recommendation (HL, JW, LJ, JY, XZ0, MZ), pp. 1803–1812.
CIKM-2019-ChuCW #documentation: Accounting for Temporal Dynamics in Document Streams (ZC, RC, HW), pp. 1813–1822.
CIKM-2019-AkenWLG #analysis #how: How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations (BvA, BW, AL, FAG), pp. 1823–1832.
CIKM-2019-AbualsaudS #query: Patterns of Search Result Examination: Query to First Action (MA, MDS), pp. 1833–1842.
CIKM-2019-ZhaoCY #comprehension #e-commerce #learning #query: A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding (JZ, HC, DY), pp. 1843–1852.
CIKM-2019-XuHY #graph #learning #network #scalability: Scalable Causal Graph Learning through a Deep Neural Network (CX, HH, SY), pp. 1853–1862.
CIKM-2019-HosseiniH #feature model #kernel #learning #multi #prototype #representation: Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection (BH, BH), pp. 1863–1872.
CIKM-2019-QianW0 #behaviour #comprehension #modelling #named #process: BePT: A Behavior-based Process Translator for Interpreting and Understanding Process Models (CQ, LW, AK0), pp. 1873–1882.
CIKM-2019-LeHSZ00 #effectiveness #towards: Towards Effective and Interpretable Person-Job Fitting (RL, WH, YS, TZ, DZ0, RY0), pp. 1883–1892.
CIKM-2019-ChekolS #graph #performance: Leveraging Graph Neighborhoods for Efficient Inference (MWC, HS), pp. 1893–1902.
CIKM-2019-GaoHWWWPC #named #recommendation: STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints (JG, YH, YW, XW, JW, GP, XC), pp. 1903–1912.
CIKM-2019-WeiXZZZC0ZXL #learning #named: CoLight: Learning Network-level Cooperation for Traffic Signal Control (HW, NX, HZ, GZ, XZ, CC, WZ0, YZ, KX, ZL), pp. 1913–1922.
CIKM-2019-WuWZJ #effectiveness #learning #performance #recommendation: Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation (NW, JW, WXZ, YJ), pp. 1923–1932.
CIKM-2019-ZhangRZYZ #named #network: PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network (YZ, WR, KZ, MY, JZ), pp. 1933–1942.
CIKM-2019-Jia0WW #collaboration #energy: Active Collaborative Sensing for Energy Breakdown (YJ, NB0, HW, KW), pp. 1943–1952.
CIKM-2019-DongSLLQZD #performance: Forecasting Pavement Performance with a Feature Fusion LSTM-BPNN Model (YD, YS, XL, SL, LQ, WZ0, JD), pp. 1953–1962.
CIKM-2019-ZhengXZFWZLXL #contest #learning: Learning Phase Competition for Traffic Signal Control (GZ, YX, XZ, JF, HW, HZ, YL0, KX, ZL), pp. 1963–1972.
CIKM-2019-LinWXLB #estimation #hybrid #network #using: Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network (XL, YW, XX, ZL, SSB), pp. 1973–1982.
CIKM-2019-JinZ0LGQJTWWWY #multi #named #order #platform: CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms (JJ, MZ, WZ0, ML, ZG, ZQ, YJ, XT, CW, JW0, GW, JY), pp. 1983–1992.
CIKM-2019-JenkinsFWL #learning #multimodal #representation: Unsupervised Representation Learning of Spatial Data via Multimodal Embedding (PJ, AF, SW, ZL), pp. 1993–2002.
CIKM-2019-QiuSR #crowdsourcing #platform #rating: Rating Mechanisms for Sustainability of Crowdsourcing Platforms (CQ, ACS, SMR), pp. 2003–2012.
CIKM-2019-YangLSB #behaviour #interactive #predict: Exploring The Interaction Effects for Temporal Spatial Behavior Prediction (HY, TL, YS, EB), pp. 2013–2022.
CIKM-2019-JonesWN #comprehension #social #web: Social Cards Probably Provide For Better Understanding Of Web Archive Collections (SMJ, MCW, MLN), pp. 2023–2032.
CIKM-2019-ShresthaMAV #behaviour #graph #interactive #learning #social: Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior (PS, SM, DA, SV), pp. 2033–2042.
CIKM-2019-0009XWSWZG #behaviour #comprehension #online: Understanding Default Behavior in Online Lending (YY0, YX, CW, YS, FW, YZ, MG), pp. 2043–2052.
CIKM-2019-ZhangL #using: Interpretable MTL from Heterogeneous Domains using Boosted Tree (YLZ, LL), pp. 2053–2056.
CIKM-2019-LiuQLZX #comprehension #order: Machine Reading Comprehension: Matching and Orders (AL, LQ, JL, CZ, ZX), pp. 2057–2060.
CIKM-2019-TianY0 #overview #summary: Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder (YT, JY, JJ0), pp. 2061–2064.
CIKM-2019-HuUMH #datalog #knowledge base #rdf #reasoning: Datalog Reasoning over Compressed RDF Knowledge Bases (PH, JU, BM, IH), pp. 2065–2068.
CIKM-2019-LinPLO #network #recognition #using: An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals (JL, SP, CSL, SLO), pp. 2069–2072.
CIKM-2019-ZouLAWZ #learning #multi #named #rank: MarlRank: Multi-agent Reinforced Learning to Rank (SZ, ZL, MA, JW0, PZ), pp. 2073–2076.
CIKM-2019-GuHDM #analysis #learning #named: LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning (DG, ZH, SD, YM0), pp. 2077–2080.
CIKM-2019-PangWZG0 #design #generative #named #network: NAD: Neural Network Aided Design for Textile Pattern Generation (ZP, SW, DZ, YG, GC0), pp. 2081–2084.
CIKM-2019-NiYWLNQC #algorithm #facebook #feature model #ranking: Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm (XN, YY, PW, YL, SN, QQ, CC), pp. 2085–2088.
CIKM-2019-GaoLY #fine-grained #probability: Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model (CG, YL, JY), pp. 2089–2092.
CIKM-2019-0002DKBJ #clustering: A Compare-Aggregate Model with Latent Clustering for Answer Selection (SY0, FD, DSK, TB, KJ), pp. 2093–2096.
CIKM-2019-WangL #behaviour #learning #network: Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding (PCW, CTL), pp. 2097–2100.
CIKM-2019-WuH #network #scalability: Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy (JW, JH), pp. 2101–2104.
CIKM-2019-HamdiBA: Tensor Decomposition-based Node Embedding (SMH, SFB, RAA), pp. 2105–2108.
CIKM-2019-ArabzadehZJB #estimation #geometry: Geometric Estimation of Specificity within Embedding Spaces (NA, FZ, JJ, EB), pp. 2109–2112.
CIKM-2019-HuangSZWC #learning #network #self: Similarity-Aware Network Embedding with Self-Paced Learning (CH0, BS, XZ, XW, NVC), pp. 2113–2116.
CIKM-2019-XuePLS #multi: Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding (HX, JP, JL, XS), pp. 2117–2120.
CIKM-2019-YangGWSX0 #network #summary: Query-Specific Knowledge Summarization with Entity Evolutionary Networks (CY, LG, ZW, JS, JX, JH0), pp. 2121–2124.
CIKM-2019-LiYH #clustering #graph #realtime: Real-time Edge Repartitioning for Dynamic Graph (HL, HY, JH), pp. 2125–2128.
CIKM-2019-HuangWWT #multi #named #network #self: DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting (SH, DW, XW, AT), pp. 2129–2132.
CIKM-2019-TianLWT #predict #re-engineering: Time Series Prediction with Interpretable Data Reconstruction (QT, JL, DW, AT), pp. 2133–2136.
CIKM-2019-LiuZH #graph #network #representation #towards: Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks (ZL, DZ, JH), pp. 2137–2140.
CIKM-2019-HuangWZLC #classification #network #prototype: Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity (CH0, XW, XZ, SL, NVC), pp. 2141–2144.
CIKM-2019-ChenLYZS #graph #network: Knowledge-aware Textual Entailment with Graph Attention Network (DC, YL, MY0, HTZ, YS), pp. 2145–2148.
CIKM-2019-MauryaLM #approximate #graph #network #performance: Fast Approximations of Betweenness Centrality with Graph Neural Networks (SKM, XL0, TM), pp. 2149–2152.
CIKM-2019-WangLL #interactive #network #predict: Neighborhood Interaction Attention Network for Link Prediction (ZW, YL, WL), pp. 2153–2156.
CIKM-2019-WuPDTZD #distance #graph #learning #network: Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning (MW, SP, LD, IWT, XZ, BD), pp. 2157–2160.
CIKM-2019-YangWCW #graph #network #predict #using: Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks (YY, ZW, QC, LW), pp. 2161–2164.
CIKM-2019-ZhaoLF #recommendation: Cross-Domain Recommendation via Preference Propagation GraphNet (CZ, CL, CF), pp. 2165–2168.
CIKM-2019-WuWLH019a #named #overview #predict #rating: ARP: Aspect-aware Neural Review Rating Prediction (CW, FW, JL, YH, XX0), pp. 2169–2172.
CIKM-2019-YanCKWM #2d #named #network #recommendation: CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (AY, SC, WCK, MW, JJM), pp. 2173–2176.
CIKM-2019-ChenL #recommendation: Data Poisoning Attacks on Cross-domain Recommendation (HC, JL), pp. 2177–2180.
CIKM-2019-SongCZX #memory management #network #recommendation: Session-based Recommendation with Hierarchical Memory Networks (BS, YC, WZ, CX), pp. 2181–2184.
CIKM-2019-ChenAJC #bias #recommendation: Correcting for Recency Bias in Job Recommendation (RCC, QA, GJ, WBC), pp. 2185–2188.
CIKM-2019-ZhaoZSL #network #recommendation: Motif Enhanced Recommendation over Heterogeneous Information Network (HZ, YZ, YS, DLL), pp. 2189–2192.
CIKM-2019-MalliaSSZ #integer: GPU-Accelerated Decoding of Integer Lists (AM, MS, TS, MZ), pp. 2193–2196.
CIKM-2019-ChenZLYY #approximate #matrix #modelling: Synergizing Local and Global Models for Matrix Approximation (CC0, HZ, DL, JY, XY), pp. 2197–2200.
CIKM-2019-Tang: Deep Colorization by Variation (ZT), pp. 2201–2204.
CIKM-2019-FujiwaraIKKAU #algorithm #bound #incremental #performance #random: Fast Random Forest Algorithm via Incremental Upper Bound (YF, YI, SK, AK, JA, NU), pp. 2205–2208.
CIKM-2019-LiuHDO #matrix: Convolution-Consistent Collective Matrix Completion (XL, JH, SD, LO), pp. 2209–2212.
CIKM-2019-DongZ #algorithm #performance #set: Faster Algorithms for k-Regret Minimizing Sets via Monotonicity and Sampling (QD, JZ), pp. 2213–2216.
CIKM-2019-RoiteroBUM #probability #simulation #towards: Towards Stochastic Simulations of Relevance Profiles (KR, AB, JU, SM), pp. 2217–2220.
CIKM-2019-LiHLDZ #detection #named #network: SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks (YL, XH, JL, MD, NZ), pp. 2233–2236.
CIKM-2019-ZhangG0G #evolution #graph #on the: On Continuously Matching of Evolving Graph Patterns (QZ, DG, XZ0, AG), pp. 2237–2240.
CIKM-2019-HwangYKK #ambiguity #detection #precise: Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling (WSH, JHY, JK, HK), pp. 2241–2244.
CIKM-2019-GiurgiuS #detection #multi: Additive Explanations for Anomalies Detected from Multivariate Temporal Data (IG, AS), pp. 2245–2248.
CIKM-2019-NeutatzMA #detection #fault #learning #named: ED2: A Case for Active Learning in Error Detection (FN, MM, ZA), pp. 2249–2252.
CIKM-2019-LiECL #clustering #identification #mobile #multi #network: Multi-scale Trajectory Clustering to Identify Corridors in Mobile Networks (LL, SME, CAC, CL), pp. 2253–2256.
CIKM-2019-XiaoZZXBZY #3d #multi #network #recognition: Multi-view Moments Embedding Network for 3D Shape Recognition (JX, YZ, PZ, KX, KB, CZ, WY), pp. 2257–2260.
CIKM-2019-GaoKPC #recognition: Active Entity Recognition in Low Resource Settings (NG, NK, RP, SC), pp. 2261–2264.
CIKM-2019-LiMB0G #adaptation #framework #novel #on the #recognition: On Novel Object Recognition: A Unified Framework for Discriminability and Adaptability (KL0, MRM, BB, YF0, HPG), pp. 2265–2268.
CIKM-2019-XuWHL #multi #recognition: Exploiting Multiple Embeddings for Chinese Named Entity Recognition (CX, FW, JH, CL), pp. 2269–2272.
CIKM-2019-Gao0WL #bidirectional #interactive #network #recognition: Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition (YG, YC0, JW, HL), pp. 2273–2276.
CIKM-2019-YuPY #concurrent #modelling #recognition: Modeling Long-Range Context for Concurrent Dialogue Acts Recognition (YY, SP, GHY), pp. 2277–2280.
CIKM-2019-ShaposhnikovBGD #detection: Labelling for Venue Visit Detection by Matching Wi-Fi Hotspots with Businesses (DS, AAB, EG, AD), pp. 2281–2284.
CIKM-2019-LiY0X #component #network: Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations (KL, FY, CF0, TX), pp. 2285–2288.
CIKM-2019-XiongZXL #learning: Learning Traffic Signal Control from Demonstrations (YX, GZ, KX, ZL), pp. 2289–2292.
CIKM-2019-BaiYK0LY #graph #network #predict: Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction (LB, LY, SSK, XW0, WL0, ZY), pp. 2293–2296.
CIKM-2019-WuWS #analysis #collaboration: Collaborative Analysis for Computational Risk in Urban Water Supply Systems (DW, HW, RS), pp. 2297–2300.
CIKM-2019-WuLZQ #learning #recommendation: Long- and Short-term Preference Learning for Next POI Recommendation (YW, KL, GZ, XQ), pp. 2301–2304.
CIKM-2019-SheetritK #clustering #retrieval: Cluster-Based Focused Retrieval (ES, OK), pp. 2305–2308.
CIKM-2019-LuoSAZ0 #learning #multi #retrieval: Cross-modal Image-Text Retrieval with Multitask Learning (JL, YS, XA, ZZ, MY0), pp. 2309–2312.
CIKM-2019-XuZYACT #framework #image: A Unified Generation-Retrieval Framework for Image Captioning (CX, WZ, MY, XA, WC, JT), pp. 2313–2316.
CIKM-2019-GaoLL0 #effectiveness #performance #retrieval: A Lossy Compression Method on Positional Index for Efficient and Effective Retrieval (SG, JL, XL, GW0), pp. 2317–2320.
CIKM-2019-GuLL #interactive #multi #network: Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots (JCG, ZHL, QL), pp. 2321–2324.
CIKM-2019-KuziLSJZ #adaptation #analysis #information retrieval #learning #rank: Analysis of Adaptive Training for Learning to Rank in Information Retrieval (SK, SL, SKKS, PPJ, CZ), pp. 2325–2328.
CIKM-2019-WangLZHG #e-commerce #interactive #named #network #recommendation: QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation (PW, ZL, YZ, YH, LG), pp. 2329–2332.
CIKM-2019-BiTDMC #case study #dependence #multi: A Study of Context Dependencies in Multi-page Product Search (KB, CHT, YD, VM, WBC), pp. 2333–2336.
CIKM-2019-FuJHZ0CY #e-commerce: Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce (ZF, FJ, WH, WZ, DZ0, HC, RY0), pp. 2337–2340.
CIKM-2019-YuanWLWHX #memory management #overview #predict #rating: Neural Review Rating Prediction with User and Product Memory (ZY, FW, JL, CW, YH, XX0), pp. 2341–2344.
CIKM-2019-ManchandaSK #e-commerce #query: Intent Term Weighting in E-commerce Queries (SM, MS, GK), pp. 2345–2348.
CIKM-2019-ChenZY #categorisation #e-commerce #fine-grained: Fine-Grained Product Categorization in E-commerce (HC, JZ, DY), pp. 2349–2352.
CIKM-2019-FanBSL #classification #fine-grained #network #prototype #scalability: Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features (MF, YB, MS, PL0), pp. 2353–2356.
CIKM-2019-0002CZTZG #classification #graph #named: Meta-GNN: On Few-shot Node Classification in Graph Meta-learning (FZ0, CC, KZ, GT, TZ, JG), pp. 2357–2360.
CIKM-2019-WuH19a #classification: Enriching Pre-trained Language Model with Entity Information for Relation Classification (SW, YH), pp. 2361–2364.
CIKM-2019-GozuacikBBC #classification #concept #detection: Unsupervised Concept Drift Detection with a Discriminative Classifier (ÖG, AB, HRB, FC), pp. 2365–2368.
CIKM-2019-KimRG #ambiguity #classification #hybrid: Hybrid Deep Pairwise Classification for Author Name Disambiguation (KK, SR, CLG), pp. 2369–2372.
CIKM-2019-Pasca #approximate #detection #lightweight #wiki: Approximate Definitional Constructs as Lightweight Evidence for Detecting Classes Among Wikipedia Articles (MP), pp. 2373–2376.
CIKM-2019-ZhangLY #generative #towards: Towards the Gradient Vanishing, Divergence Mismatching and Mode Collapse of Generative Adversarial Nets (ZZ, CL, JY), pp. 2377–2380.
CIKM-2019-LiuLLD0 #generative #information management #topic: Generating Paraphrase with Topic as Prior Knowledge (YL, ZL, FL, QD, WW0), pp. 2381–2384.
CIKM-2019-LiuLLZS #classification #identification: Sexual Harassment Story Classification and Key Information Identification (YL, QL, XL, QZ, LS), pp. 2385–2388.
CIKM-2019-Liu0 #overview #summary: Neural Review Summarization Leveraging User and Product Information (HL, XW0), pp. 2389–2392.
CIKM-2019-XiaWY #comprehension #learning #multi: Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning (JX, CW, MY), pp. 2393–2396.
CIKM-2019-ChengLCHHCMH #learning #named: DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis (SC, QL0, EC, ZH, ZH, YC, HM, GH), pp. 2397–2400.
CIKM-2019-WuWQLH0 #gender #microblog #predict #representation: Neural Gender Prediction in Microblogging with Emotion-aware User Representation (CW, FW, TQ, JL, YH, XX0), pp. 2401–2404.
CIKM-2019-JimmyZKD #empirical #health #retrieval: Health Card Retrieval for Consumer Health Search: An Empirical Investigation of Methods (J, GZ, BK, GD), pp. 2405–2408.
CIKM-2019-WuWHX #estimation #named: NICE: Neural In-Hospital Cost Estimation from Medical Records (CW, FW, YH, XX0), pp. 2409–2412.
CIKM-2019-WangLL19a #evolution #modelling #sentiment #social: Modeling Sentiment Evolution for Social Incidents (YW, HL, CL), pp. 2413–2416.
CIKM-2019-ZhangTH #adaptation: Adaptive Feature Redundancy Minimization (RZ0, HT, YH), pp. 2417–2420.
CIKM-2019-DuttaL #clique #graph #statistics: Finding a Maximum Clique in Dense Graphs via χ2 Statistics (SD0, JL), pp. 2421–2424.
CIKM-2019-WangGLML #bias #on the #testing: On Heavy-user Bias in A/B Testing (YW, SG, JL, AM, SL), pp. 2425–2428.
CIKM-2019-CalzavaraLT: Adversarial Training of Gradient-Boosted Decision Trees (SC, CL, GT), pp. 2429–2432.
CIKM-2019-CaiYZR #network: Adversarial Structured Neural Network Pruning (XC, JY, FZ, SR), pp. 2433–2436.
CIKM-2019-BremenDJ #logic programming #probability #query: Ontology-Mediated Queries over Probabilistic Data via Probabilistic Logic Programming (TvB, AD, JCJ), pp. 2437–2440.
CIKM-2019-ChenTL #learning #query #social: Query Embedding Learning for Context-based Social Search (YCC, YCT, CTL), pp. 2441–2444.
CIKM-2019-ChenWCKQ #dataset #generative #query #towards: Towards More Usable Dataset Search: From Query Characterization to Snippet Generation (JC, XW, GC0, EK, YQ), pp. 2445–2448.
CIKM-2019-GhoshS #behaviour: Session-based Search Behavior in Naturalistic Settings for Learning-related Tasks (SG, CS), pp. 2449–2452.
CIKM-2019-LuoCXQ #community #network: Best Co-Located Community Search in Attributed Networks (JL, XC, XX, QQ), pp. 2453–2456.
CIKM-2019-YafayA #performance #query: Caching Scores for Faster Query Processing with Dynamic Pruning in Search Engines (EY, ISA), pp. 2457–2460.
CIKM-2019-MaoSSSS #learning #process: Investigating the Learning Process in Job Search: A Longitudinal Study (JM, DS, SS, FS, MS), pp. 2461–2464.
CIKM-2019-LuoGRML #set: Set Reconciliation with Cuckoo Filters (LL, DG, OR, RTBM, XL), pp. 2465–2468.
CIKM-2019-ConteFPT #distributed: Shared-Nothing Distributed Enumeration of 2-Plexes (AC, DF, MP, RT), pp. 2469–2472.
CIKM-2019-Apfelbaum #database: Estimating the Number of Distinct Items in a Database by Sampling (RA), pp. 2473–2476.
CIKM-2019-ChenLCHT #effectiveness #resource management: Cost-effective Resource Provisioning for Spark Workloads (YC, JL, CC, MH, ST), pp. 2477–2480.
CIKM-2019-SalloumWH #approximate #big data #clustering #data analysis: A Sampling-Based System for Approximate Big Data Analysis on Computing Clusters (SS, YW, JZH), pp. 2481–2484.
CIKM-2019-ChenMLZM #dataset #named #scalability #web: TianGong-ST: A New Dataset with Large-scale Refined Real-world Web Search Sessions (JC, JM, YL, MZ0, SM), pp. 2485–2488.
CIKM-2019-ZhangPZZWXJ #behaviour #e-commerce #visual notation: Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance (YZ, PP, YZ, KZ, JW, YX, RJ), pp. 2489–2497.
CIKM-2019-ZhangYWH #automation #e-commerce #learning #named #ranking #realtime: Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising (YZ, ZY, LW, LH), pp. 2499–2507.
CIKM-2019-QiuWCZHCZB #e-commerce #network: Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search (MQ, BW, CC, XZ, JH0, DC, JZ, FSB), pp. 2509–2515.
CIKM-2019-LuoYZGY #concept #e-commerce: Conceptualize and Infer User Needs in E-commerce (XL, YY, KQZ, YG, KY), pp. 2517–2525.
CIKM-2019-ChenJZPNYWLXG #e-commerce #learning: Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds (DC, JJ, WZ0, FP, LN, CY, JW0, HL, JX, KG), pp. 2527–2535.
CIKM-2019-HuangYX #detection #graph #learning: System Deterioration Detection and Root Cause Learning on Time Series Graphs (HH, SY, YX), pp. 2537–2545.
CIKM-2019-ChengZYTN0 #framework #predict: A Dynamic Default Prediction Framework for Networked-guarantee Loans (DC, YZ, FY, YT, ZN, LZ0), pp. 2547–2555.
CIKM-2019-ArianAAKSS #feature model #network #predict: Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native (MA, EA, MA, YK, OS, RS), pp. 2557–2565.
CIKM-2019-ZhaoPZZWZXJ #distributed #graph #scalability #visual notation: Large-Scale Visual Search with Binary Distributed Graph at Alibaba (KZ, PP, YZ, YZ, CW, YZ, YX, RJ), pp. 2567–2575.
CIKM-2019-LiuWYZSMZGZYQ #graph #learning #mobile #optimisation #representation: Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing (ZL, DW, QY, ZZ, YS, JM, WZ, JG, JZ, SY, YQ), pp. 2577–2584.
CIKM-2019-ZhuGLMOZWGC #adaptation #interactive #recommendation: Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU (YZ, YG, QL, YM, WO, JZ, BW, ZG, DC), pp. 2585–2593.
CIKM-2019-WangJH0YZWHWLXG #adaptation #learning #realtime: Learning Adaptive Display Exposure for Real-Time Advertising (WW, JJ, JH, CC0, CY, WZ0, JW0, XH, YW, HL, JX, KG), pp. 2595–2603.
CIKM-2019-ZhaoLZWJXWM #evaluation #matter #problem #what: What You Look Matters?: Offline Evaluation of Advertising Creatives for Cold-start Problem (ZZ, LL, BZ0, MW, YJ, LX, FW, WYM), pp. 2605–2613.
CIKM-2019-LiLWXZHKCLL #multi #network #recommendation: Multi-Interest Network with Dynamic Routing for Recommendation at Tmall (CL, ZL, MW, YX, HZ, PH, GK, QC, WL, DLL), pp. 2615–2623.
CIKM-2019-RaoSPJCTGK #evolution #learning #recommendation: Learning to be Relevant: Evolution of a Course Recommendation System (SR, KS, GP, MJ, SC, VT, JG, DK), pp. 2625–2633.
CIKM-2019-LvJYSLYN #named #online #recommendation #scalability: SDM: Sequential Deep Matching Model for Online Large-scale Recommender System (FL, TJ, CY, FS, QL, KY, WN), pp. 2635–2643.
CIKM-2019-ZhouJZQJWWYY #learning #multi: Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching (MZ, JJ, WZ0, ZQ, YJ, CW, GW, YY0, JY), pp. 2645–2653.
CIKM-2019-FanHZLLW #learning #named #scalability: MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data (MF, JH, AZ, YL, PL0, HW), pp. 2655–2663.
CIKM-2019-YiDLLZZ #approach #modelling #named: CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach (XY, ZD, TL, TL, JZ, YZ), pp. 2665–2671.
CIKM-2019-HuangZDB #network: Deep Dynamic Fusion Network for Traffic Accident Forecasting (CH, CZ, PD, LB), pp. 2673–2681.
CIKM-2019-PanWWYZZ #matrix #network #predict: Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction (ZP, ZW, WW, YY, JZ, YZ), pp. 2683–2691.
CIKM-2019-ArkoudasY #semantics: Semantically Driven Auto-completion (KA, MY), pp. 2693–2701.
CIKM-2019-LiQLYL #detection #graph #network #overview: Spam Review Detection with Graph Convolutional Networks (AL, ZQ, RL, YY, DL), pp. 2703–2711.
CIKM-2019-KhabiriGVPM #classification #industrial #word: Industry Specific Word Embedding and its Application in Log Classification (EK, WMG, BV, DP, PM), pp. 2713–2721.
CIKM-2019-ShiRWR #classification #multi #online #sentiment: Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts (TS, VR, SW, CKR), pp. 2723–2731.
CIKM-2019-KimSRLW #learning #predict: Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces (KK, BS, SHR, SL, SSW), pp. 2733–2741.
CIKM-2019-MaAWSCTY #data analysis #graph #learning #similarity: Deep Graph Similarity Learning for Brain Data Analysis (GM, NKA, TLW, DS, MWC, NBTB, PSY), pp. 2743–2751.
CIKM-2019-LiLXLC #how #source code: How to Find It Better?: Cross-Learning for WeChat Mini Programs (HL, ZL, SX, ZL, XC), pp. 2753–2761.
CIKM-2019-ZhangLZLWWX #benchmark #learning #metric #multi #named #representation: Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning (DZ, JL, HZ, YL, LW, PW, HX), pp. 2763–2771.
CIKM-2019-JiangCBWYN #learning #predict #smarttech: Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices (JYJ, ZC, ALB, WW0, SDY, DN), pp. 2773–2781.
CIKM-2019-FangSCG #fine-grained #predict: Fine-Grained Fuel Consumption Prediction (CF, SS, ZC, AG), pp. 2783–2791.
CIKM-2019-AharonKLSBESSZ #predict: Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native (MA, YK, RL, OS, AB, NE, AS, AS, AZ), pp. 2793–2801.
CIKM-2019-DoanYR #modelling: Adversarial Factorization Autoencoder for Look-alike Modeling (KDD, PY, CKR), pp. 2803–2812.
CIKM-2019-TianKA0C #adaptation #concept #detection #health #monitoring #online: Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring (HT, NLDK, AA, YW0, FC0), pp. 2813–2821.
CIKM-2019-XinEBYLZY0 #multi #online #predict: Multi-task based Sales Predictions for Online Promotions (SX, ME, JB, CY, ZL, XZ, YY, CW0), pp. 2823–2831.
CIKM-2019-YinRYZZZLZ #case study #modelling #multi: Experimental Study of Multivariate Time Series Forecasting Models (JY, WR, MY, JZ, KZ, CZ, JL, QZ), pp. 2833–2839.
CIKM-2019-TaoGFCYZ #game studies #learning #multi #named #online #predict: GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games (JT, LG, CF, LC, DY, SZ), pp. 2841–2849.
CIKM-2019-YangDTTZQD #composition #learning #predict #relational #visual notation: Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search (XY, TD, WT, XT, JZ, SQ, ZD), pp. 2851–2859.
CIKM-2019-BoutetG #privacy #what: Inspect What Your Location History Reveals About You: Raising user awareness on privacy threats associated with disclosing his location data (AB, SG), pp. 2861–2864.
CIKM-2019-MiloMY #analysis #datalog #named #probability: PODIUM: Probabilistic Datalog Analysis via Contribution Maximization (TM, YM, BY), pp. 2865–2868.
CIKM-2019-Jatowt0BD #analysis #documentation: Document in Context of its Time (DICT): Providing Temporal Context to Support Analysis of Past Documents (AJ, RC0, SSB, AD), pp. 2869–2872.
CIKM-2019-ElMS #data analysis #learning #named: ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning (OBE, TM, AS), pp. 2873–2876.
CIKM-2019-GuillyPSI #database #interactive #named #sql: ExplIQuE: Interactive Databases Exploration with SQL (MLG, JMP, VMS, IFI), pp. 2877–2880.
CIKM-2019-WangCCCHHLC #bound #interactive #mining #mobile #named #process #visualisation: TraVis: An Interactive Visualization System for Mining Inbound Traveler Activities by Leveraging Mobile Ad Request Data (PXW, HC, WQC, CCC, YHH, THH, YL, CHC), pp. 2881–2884.
CIKM-2019-PaganelliSMI0 #comprehension: Understanding Data in the Blink of an Eye (MP, PS, AM, MI, FG0), pp. 2885–2888.
CIKM-2019-BernhauerSHPS #modelling #named #similarity: SIMILANT: An Analytic Tool for Similarity Modeling (DB, TS, IH, LP, MS), pp. 2889–2892.
CIKM-2019-SunAJHS #dataset #flexibility #named: MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science (CS, AA, HVJ, BH, JS), pp. 2893–2896.
CIKM-2019-JungLPP #framework #named #using: PRIVATA: Differentially Private Data Market Framework using Negotiation-based Pricing Mechanism (KJ, JL, KP, SP), pp. 2897–2900.
CIKM-2019-HuangLCKQ #named: MiCRon: Making Sense of News via Relationship Subgraphs (ZH, SL, GC0, EK, YQ), pp. 2901–2904.
CIKM-2019-OppoldH #data-driven #named #personalisation: LuPe: A System for Personalized and Transparent Data-driven Decisions (SO, MH), pp. 2905–2908.
CIKM-2019-MohantyR #effectiveness #graph #named #towards: Insta-Search: Towards Effective Exploration of Knowledge Graphs (MM, MR), pp. 2909–2912.
CIKM-2019-Liu0PLZZ #named: SkyRec: Finding Pareto Optimal Groups (JL, LX0, JP, JL, HZ, SZ), pp. 2913–2916.
CIKM-2019-ZhengQJCM #interactive #named: CurrentClean: Interactive Change Exploration and Cleaning of Stale Data (ZZ, TMQ, ZJ, FC, MM), pp. 2917–2920.
CIKM-2019-HaoYLLJL #distributed #named #pattern matching: PatMat: A Distributed Pattern Matching Engine with Cypher (KH, ZY, LL, ZL, XJ, XL0), pp. 2921–2924.
CIKM-2019-GalitskyI #on the: On a Chatbot Conducting Virtual Dialogues (BG, DII), pp. 2925–2928.
CIKM-2019-0001SKJ #named #recommendation: Rehab-Path: Recommending Alcohol and Drug-free Routes (YZ0, PS, YK, AJ), pp. 2929–2932.
CIKM-2019-BespinyowongT #graph #named: kBrowse: kNN Graph Browser (RB, AKHT), pp. 2933–2936.
CIKM-2019-VergoulisCKDTD #exclamation #ranking: BIP! Finder: Facilitating Scientific Literature Search by Exploiting Impact-Based Ranking (TV, SC, IK, PD, CT, TD), pp. 2937–2940.
CIKM-2019-CaoDGMT #named #network #query: BeLink: Querying Networks of Facts, Statements and Beliefs (TDC, LD, FG, IM, XT), pp. 2941–2944.
CIKM-2019-PaganelliS0V #named #rule-based: TuneR: Fine Tuning of Rule-based Entity Matchers (MP, PS, FG0, YV), pp. 2945–2948.
CIKM-2019-RoySMSG #information retrieval #named #plugin: I-REX: A Lucene Plugin for EXplainable IR (DR, SS, MM, BS, DG), pp. 2949–2952.
CIKM-2019-BozarthDHJMPPQS #deployment #learning #ubiquitous: Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment (AB, BD, FH, DJ, KM, NP, SP, GdQ, SS, PT, XW, HX0, FRR, VB), pp. 2953–2956.
CIKM-2019-GershteinMN #e-commerce #effectiveness #named #reduction: ReducE-Comm: Effective Inventory Reduction System for E-Commerce (SG, TM, SN), pp. 2957–2960.
CIKM-2019-CuiSW0L #detection #named: dEFEND: A System for Explainable Fake News Detection (LC, KS, SW, DL0, HL0), pp. 2961–2964.
CIKM-2019-DuanX #enterprise #graph #information management: Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management (RD, YX), pp. 2965–2966.
CIKM-2019-MueenCM #detection #metric #social: Taming Social Bots: Detection, Exploration and Measurement (AM, NC, AJM), pp. 2967–2968.
CIKM-2019-Gurajada0QS: Learning-Based Methods with Human-in-the-Loop for Entity Resolution (SG, LP0, KQ0, PS), pp. 2969–2970.
CIKM-2019-Wang0C #graph #learning #reasoning #recommendation: Learning and Reasoning on Graph for Recommendation (XW, XH0, TSC), pp. 2971–2972.
CIKM-2019-ShiY #analysis #network: Recent Developments of Deep Heterogeneous Information Network Analysis (CS, PSY), pp. 2973–2974.
CIKM-2019-LuLW0 #database #machine learning #modelling #similarity #string: Synergy of Database Techniques and Machine Learning Models for String Similarity Search and Join (JL, CL, JW, CL0), pp. 2975–2976.
CIKM-2019-ShanahanD #detection #pipes and filters #realtime: Realtime Object Detection via Deep Learning-based Pipelines (JGS, LD), pp. 2977–2978.
CIKM-2019-ChelliahZS #mining #multi #overview #recommendation: Recommendation for Multi-stakeholders and through Neural Review Mining (MC, YZ, SS), pp. 2979–2981.
CIKM-2019-VazirgiannisNS #graph #kernel #machine learning: Machine Learning on Graphs with Kernels (MV, GN, GS), pp. 2983–2984.
CIKM-2019-PaikXL #mining: DTMBIO 2019: The Thirteenth International Workshop on Data and Text Mining in Biomedical Informatics (HP, RX, DL), pp. 2985–2987.
CIKM-2019-UkilMJF #quality: Knowledge-Driven Analytics and Systems Impacting Human Quality of Life (AU, LM, AJ, JF), pp. 2989–2990.
CIKM-2019-ShiYZ #analysis #network: HENA 2019: The 3rd Workshop of Heterogeneous Information Network Analysis and Applications (CS, YY, JZ), pp. 2991–2992.
CIKM-2019-ChengGW #retrieval: EYRE 2019: 2nd International Workshop on EntitY REtrieval (GC0, KG, JW), pp. 2993–2994.
CIKM-2019-0001JZZLY: CIKM 2019 Workshop on Artificial Intelligence in Transportation (AI in transportation) (WZ0, HJ, LZ, HZ, ZJL, JY), pp. 2995–2996.
CIKM-2019-ShenTB #graph #learning #representation: GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications (HS, JT, PB), pp. 2997–2998.
CIKM-2019-SivrikayaAL #parametricity #recommendation: International Workshop on Model Selection and Parameter Tuning in Recommender Systems (FS, SA, DL), pp. 2999–3000.
CIKM-2019-AnelliN #recommendation: 2nd Workshop on Knowledge-aware and Conversational Recommender Systems - KaRS (VWA, TDN), pp. 3001–3002.
CIKM-2019-0001LKW: BigScholar 2019: The 6th Workshop on Big Scholarly Data (FX0, HL, IK, KW), pp. 3003–3004.

Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, Jeffrey Xu YuProceedings of the 28th ACM International Conference on Information and Knowledge ManagementCIKM, 2019.

Contents (394 items)

Wenwu Zhu, Dacheng Tao, Xueqi Cheng, Peng Cui, Elke A. Rundensteiner, David Carmel, Qi He, Jeffrey Xu Yu
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
CIKM, 2019.