Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining
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Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, George Karypis
Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining
KDD, 2019.

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@proceedings{KDD-2019,
	doi           = "10.1145/3292500",
	editor        = "Ankur Teredesai and Vipin Kumar and Ying Li and Rómer Rosales and Evimaria Terzi and George Karypis",
	isbn          = "978-1-4503-6201-6",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 25th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2019,
}

Contents (374 items)

KDD-2019-Rudin #how #modelling #question
Do Simpler Models Exist and How Can We Find Them? (CR), pp. 1–2.
KDD-2019-Lee #effectiveness
The Unreasonable Effectiveness, and Difficulty, of Data in Healthcare (PL), pp. 3–4.
KDD-2019-InabaFKZ #approach #distance #energy #learning #metric
A Free Energy Based Approach for Distance Metric Learning (SI, CTF, RVK, KZ), pp. 5–13.
KDD-2019-MengZXZX #network #predict
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction (QM, HZ, KX, LZ, HX), pp. 14–24.
KDD-2019-WangQZZWLG #set #sketching #streaming
A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets (PW, YQ, YZ, QZ, CW, JCSL, XG), pp. 25–33.
KDD-2019-WangQWLGZHZCZ #game studies #learning
A Minimax Game for Instance based Selective Transfer Learning (BW, MQ, XW, YL, YG, XZ, JH0, BZ, DC, JZ), pp. 34–43.
KDD-2019-LiuA #adaptation #locality #multi #statistics
A Multiscale Scan Statistic for Adaptive Submatrix Localization (YL, EAC), pp. 44–53.
KDD-2019-NetoPPTBMO #approach #health #machine learning #permutation
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health (ECN, AP, TMP, MT, BMB, LM, LO), pp. 54–64.
KDD-2019-HouCLCY #framework #graph #learning #representation
A Representation Learning Framework for Property Graphs (YH, HC, CL, JC, MCY), pp. 65–73.
KDD-2019-YangZZX0 #adaptation #capacity #incremental #learning #modelling #scalability
Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (YY, DWZ, DCZ, HX, YJ0), pp. 74–82.
KDD-2019-WangLZ #adaptation #ambiguity #graph #learning
Adaptive Graph Guided Disambiguation for Partial Label Learning (DBW, LL0, MLZ), pp. 83–91.
KDD-2019-LiGL0 #adaptation #feature model #network
Adaptive Unsupervised Feature Selection on Attributed Networks (JL, RG, CL, HL0), pp. 92–100.
KDD-2019-HartvigsenSKR #adaptation #classification #network #policy
Adaptive-Halting Policy Network for Early Classification (TH, CS, XK, EAR), pp. 101–110.
KDD-2019-WangYCZ #convergence #learning #performance
ADMM for Efficient Deep Learning with Global Convergence (JW, FY, XC0, LZ0), pp. 111–119.
KDD-2019-HuFS #learning #network
Adversarial Learning on Heterogeneous Information Networks (BH, YF0, CS), pp. 120–129.
KDD-2019-WangFXL #learning #mobile #profiling #representation
Adversarial Substructured Representation Learning for Mobile User Profiling (PW, YF, HX, XL), pp. 130–138.
KDD-2019-ZhangYY #learning #robust
Adversarial Variational Embedding for Robust Semi-supervised Learning (XZ0, LY, FY), pp. 139–147.
KDD-2019-AvdiukhinMYZ #constraints #robust
Adversarially Robust Submodular Maximization under Knapsack Constraints (DA, SM, GY, SZ), pp. 148–156.
KDD-2019-YuSJ #interactive #recommendation #visual notation
A Visual Dialog Augmented Interactive Recommender System (TY, YS, HJ), pp. 157–165.
KDD-2019-GoodrichRLS
Assessing The Factual Accuracy of Generated Text (BG, VR, PJL, MS), pp. 166–175.
KDD-2019-FuZCR #gpu #named #optimisation #performance #robust #visualisation
AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization (CF, YZ, DC, XR), pp. 176–186.
KDD-2019-GuanMW
Attribute-Driven Backbone Discovery (SG, HM, YW), pp. 187–195.
KDD-2019-SongS #modelling
Auditing Data Provenance in Text-Generation Models (CS, VS), pp. 196–206.
KDD-2019-LiuFWWBL #automation #learning #multi
Automating Feature Subspace Exploration via Multi-Agent Reinforcement Learning (KL, YF, PW, LW, RB, XL), pp. 207–215.
KDD-2019-TuM0P0 #named #network #optimisation
AutoNE: Hyperparameter Optimization for Massive Network Embedding (KT, JM, PC0, JP, WZ0), pp. 216–225.
KDD-2019-ZhangTKLCC #axiom #modelling #multi
Axiomatic Interpretability for Multiclass Additive Models (XZ, ST, PK, YL, UC, RC), pp. 226–234.
KDD-2019-XiaoZPSZ0 #personalisation #recommendation #similarity #social
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction (WX, HZ, HP, YS, VWZ, QY0), pp. 235–245.
KDD-2019-ZugnerG #graph #network #robust
Certifiable Robustness and Robust Training for Graph Convolutional Networks (DZ, SG), pp. 246–256.
KDD-2019-ChiangLSLBH #algorithm #clustering #graph #named #network #performance #scalability
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (WLC, XL, SS, YL0, SB, CJH), pp. 257–266.
KDD-2019-AhmadianE0M #clustering
Clustering without Over-Representation (SA, AE, RK0, MM), pp. 267–275.
KDD-2019-GaoPH #graph #network #random
Conditional Random Field Enhanced Graph Convolutional Neural Networks (HG, JP, HH), pp. 276–284.
KDD-2019-HuangLK00M0 #ranking #synthesis
Contextual Fact Ranking and Its Applications in Table Synthesis and Compression (SH, JL, FK, XW0, YW0, DM, CY0), pp. 285–293.
KDD-2019-BendimeradLPRB #anti
Contrastive Antichains in Hierarchies (AB, JL, MP, CR, TDB), pp. 294–304.
KDD-2019-YeSDFTX #multi #network #predict
Co-Prediction of Multiple Transportation Demands Based on Deep Spatio-Temporal Neural Network (JY, LS, BD, YF, XT, HX), pp. 305–313.
KDD-2019-WangLT
Coresets for Minimum Enclosing Balls over Sliding Windows (YW, YL, KLT), pp. 314–323.
KDD-2019-LiuLTL #named
CoSTCo: A Neural Tensor Completion Model for Sparse Tensors (HL, YL, MT, YL0), pp. 324–334.
KDD-2019-SongCH #collaboration
Coupled Variational Recurrent Collaborative Filtering (QS, SC, XH), pp. 335–343.
KDD-2019-LiuLDCG #learning #named #recommendation
DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
KDD-2019-PangSH #detection #network
Deep Anomaly Detection with Deviation Networks (GP, CS, AvdH), pp. 353–362.
KDD-2019-RenQZY00 #realtime
Deep Landscape Forecasting for Real-time Bidding Advertising (KR, JQ, LZ, ZY, WZ0, YY0), pp. 363–372.
KDD-2019-OkawaIK0TU #information management #predict #process
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information (MO, TI, TK, YT0, HT, NU), pp. 373–383.
KDD-2019-KeXZBL #framework #learning #named #online #predict
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
KDD-2019-ShuCW0L #detection #named
dEFEND: Explainable Fake News Detection (KS, LC, SW, DL0, HL0), pp. 395–405.
KDD-2019-WuHX #classification #graph #named #network
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification (JW, JH, JX), pp. 406–415.
KDD-2019-WuZ #ambiguity #analysis #linear #reduction
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction (JHW, MLZ), pp. 416–424.
KDD-2019-DoronSS #black box #interactive #modelling
Discovering Unexpected Local Nonlinear Interactions in Scientific Black-box Models (MD, IS, DS), pp. 425–435.
KDD-2019-Zhou0Y #online
Dual Averaging Method for Online Graph-structured Sparsity (BZ, FC0, YY), pp. 436–446.
KDD-2019-WuGGWC #modelling #predict #recommendation
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination (QW, YG, XG, PW, GC), pp. 447–457.
KDD-2019-MatsubaraS #data type #modelling
Dynamic Modeling and Forecasting of Time-evolving Data Streams (YM, YS), pp. 458–468.
KDD-2019-ZangC0W
Dynamical Origins of Distribution Functions (CZ, PC, WZ0, FW), pp. 469–478.
KDD-2019-LiHWL #approach #community #detection #named
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection (PZL, LH, CDW, JHL), pp. 479–487.
KDD-2019-ChenSJ0Z0 #behaviour #effectiveness #performance #recommendation #reuse #using
Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation (LC, SS, CSJ, BY0, ZZ, LS0), pp. 488–498.
KDD-2019-WangLCJ #effectiveness #game studies #learning #performance #representation #retrieval
Effective and Efficient Sports Play Retrieval with Deep Representation Learning (ZW, CL, GC, CJ), pp. 499–509.
KDD-2019-LiZY #effectiveness #learning #performance
Efficient and Effective Express via Contextual Cooperative Reinforcement Learning (YL, YZ, QY), pp. 510–519.
KDD-2019-WuYHZX0JA #kernel #performance #random #string
Efficient Global String Kernel with Random Features: Beyond Counting Substructures (LW, IEHY, SH, LZ0, KX, LM0, SJ, CCA), pp. 520–528.
KDD-2019-Chang #clique #graph #performance #scalability
Efficient Maximum Clique Computation over Large Sparse Graphs (LC), pp. 529–538.
KDD-2019-WangWZPL #algorithm #network #personalisation #recommendation
Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation (JW, NW, WXZ, FP, XL), pp. 539–547.
KDD-2019-WangYWNHC #collaboration #generative
Enhancing Collaborative Filtering with Generative Augmentation (QW, HY, HW, QVHN, ZH, LC), pp. 548–556.
KDD-2019-YaoYX #semantics #word
Enhancing Domain Word Embedding via Latent Semantic Imputation (SY, DY, KX), pp. 557–565.
KDD-2019-ShangYLQMY #learning #re-engineering #recommendation
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation (WS, YY, QL, ZQ, YM, JY), pp. 566–576.
KDD-2019-AdhikariXRP #named
EpiDeep: Exploiting Embeddings for Epidemic Forecasting (BA, XX, NR, BAP), pp. 577–586.
KDD-2019-ParamonovSS #statistics
Estimating Graphlet Statistics via Lifting (KP, DS, JS), pp. 587–595.
KDD-2019-ParkKDZF #graph #network #using
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks (NP, AK, XLD, TZ, CF), pp. 596–606.
KDD-2019-YangWZK #named #performance
ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data (SY, JW, XZ, DK), pp. 607–616.
KDD-2019-GongZDLGSOZ #clique #optimisation #recommendation
Exact-K Recommendation via Maximal Clique Optimization (YG, YZ, LD, QL, ZG, FS, WO, KQZ), pp. 617–626.
KDD-2019-LiuTLZCMW #adaptation #learning
Exploiting Cognitive Structure for Adaptive Learning (QL0, ST, CL, HZ, EC, HM, SW), pp. 627–635.
KDD-2019-WuLWCW #online
Factorization Bandits for Online Influence Maximization (QW, ZL, HW, WC, HW), pp. 636–646.
KDD-2019-YoonHSF #approach #detection #graph #performance
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged Approach (MY, BH, KS, CF), pp. 647–657.
KDD-2019-WangD #approximate #empirical #performance
Fast Approximation of Empirical Entropy via Subsampling (CW, BD), pp. 658–667.
KDD-2019-LiCZZ0L #ecosystem #question #social
Fates of Microscopic Social Ecosystems: Keep Alive or Dead? (HL, PC, CZ, TZ, WZ0, YL), pp. 668–676.
KDD-2019-AmelkinS #network #recommendation #social
Fighting Opinion Control in Social Networks via Link Recommendation (VA, AKS), pp. 677–685.
KDD-2019-KlyuchnikovMKMK #multi
Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging (NK, DM, GK, EM, PK), pp. 686–695.
KDD-2019-ZouKCC0 #evaluation #policy #robust
Focused Context Balancing for Robust Offline Policy Evaluation (HZ, KK, BC, PC, PC0), pp. 696–704.
KDD-2019-HanYZSLZ0K #graph #identification #matrix #named #network
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization (PH, PY, PZ, SS, YL0, JZ, XG0, PK), pp. 705–713.
KDD-2019-MonathZSMA #clustering #using
Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space (NM, MZ, DS, AM, AA), pp. 714–722.
KDD-2019-0001WAT #graph #network
Graph Convolutional Networks with EigenPooling (YM0, SW, CCA, JT), pp. 723–731.
KDD-2019-HuangSLH #graph #network #random
Graph Recurrent Networks With Attributed Random Walks (XH, QS, YL, XH), pp. 732–740.
KDD-2019-GaoJ #graph #learning #network #representation
Graph Representation Learning via Hard and Channel-Wise Attention Networks (HG, SJ), pp. 741–749.
KDD-2019-Do0V #graph transformation #network #policy #predict
Graph Transformation Policy Network for Chemical Reaction Prediction (KD, TT0, SV), pp. 750–760.
KDD-2019-JiaSSB #graph #learning
Graph-based Semi-Supervised & Active Learning for Edge Flows (JJ, MTS, SS, ARB), pp. 761–771.
KDD-2019-YanZDSSK #classification #named #network
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (YY, JZ, MD, ES, CSS, DK), pp. 772–782.
KDD-2019-MengY0N #framework #induction #named
HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings (CM, JY, BR0, JN), pp. 783–792.
KDD-2019-ZhangSHSC #graph #network
Heterogeneous Graph Neural Network (CZ, DS, CH0, AS, NVC), pp. 793–803.
KDD-2019-0001S #markov
Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications (ZJ0, AMS), pp. 804–813.
KDD-2019-CuiDZYZ0 #crowdsourcing #ranking
Hidden POI Ranking with Spatial Crowdsourcing (YC, LD, YZ0, BY0, VWZ, KZ0), pp. 814–824.
KDD-2019-MaKL #network #recommendation
Hierarchical Gating Networks for Sequential Recommendation (CM, PK, XL), pp. 825–833.
KDD-2019-FeiTL #learning #multi #predict #word
Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction (HF, ST, PL0), pp. 834–842.
KDD-2019-JhaXWZ #co-evolution #concept #generative
Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts (KJ, GX, YW, AZ), pp. 843–851.
KDD-2019-MarxV #using
Identifiability of Cause and Effect using Regularized Regression (AM, JV), pp. 852–861.
KDD-2019-Jia0RLH #quality
Improving the Quality of Explanations with Local Embedding Perturbations (YJ, JB0, KR, CL, MEH), pp. 875–884.
KDD-2019-ChengSHZ #analysis #modelling #performance
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (WC, YS, LH, YZ), pp. 885–893.
KDD-2019-LiYZBQL #optimisation
Individualized Indicator for All: Stock-wise Technical Indicator Optimization with Stock Embedding (ZL, DY, LZ, JB0, TQ, TYL), pp. 894–902.
KDD-2019-MingXQR #learning #prototype #sequence
Interpretable and Steerable Sequence Learning via Prototypes (YM, PX, HQ, LR), pp. 903–913.
KDD-2019-YanLSZZ0 #profiling
Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories (RY0, RL, YS, TZ, XZ0, DZ0), pp. 914–922.
KDD-2019-LiuMLZM #user satisfaction
Investigating Cognitive Effects in Session-level Search User Satisfaction (ML, JM, YL, MZ0, SM), pp. 923–931.
KDD-2019-LiuTLYZH #network
Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding (NL, QT, YL, HY, JZ, XH), pp. 932–940.
KDD-2019-XuTZ #kernel #learning #multi
Isolation Set-Kernel and Its Application to Multi-Instance Learning (BCX, KMT, ZHZ), pp. 941–949.
KDD-2019-Wang00LC #graph #named #network #recommendation
KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
KDD-2019-NieWL #clustering #multi #named
K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters (FN, CLW, XL), pp. 959–967.
KDD-2019-WangZZLZLW #graph #network #recommendation
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (HW, FZ, MZ, JL, MZ, WL, ZW), pp. 968–977.
KDD-2019-ChenC0GLLW #modelling #named #recommendation
λOpt: Learn to Regularize Recommender Models in Finer Levels (YC, BC, XH0, CG, YL0, JGL, YW), pp. 978–986.
KDD-2019-JinRKKRK #network #summary
Latent Network Summarization: Bridging Network Embedding and Summarization (DJ, RAR, EK, SK, AR, DK), pp. 987–997.
KDD-2019-XieH #learning
Learning Class-Conditional GANs with Active Sampling (MKX, SJH), pp. 998–1006.
KDD-2019-DengRN #graph #learning #predict #social
Learning Dynamic Context Graphs for Predicting Social Events (SD, HR, YN), pp. 1007–1016.
KDD-2019-ZhangZJZ #learning
Learning from Incomplete and Inaccurate Supervision (ZyZ, PZ, YJ0, ZHZ), pp. 1017–1025.
KDD-2019-YoshidaTK #graph #learning #metric #mining
Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDD-2019-HeLLH #learning #network
Learning Network-to-Network Model for Content-rich Network Embedding (ZH, JL0, NL, YH), pp. 1037–1045.
KDD-2019-XuH0D #network #predict #social
Link Prediction with Signed Latent Factors in Signed Social Networks (PX, WH, JW0, BD), pp. 1046–1054.
KDD-2019-Tao0WFYZ0 #memory management #modelling #named #semantics #towards
Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit (ZT, SL0, ZW, CF, LY, HZ, YF0), pp. 1055–1063.
KDD-2019-WangXLLCDWS #framework #learning #multi #named #network #social
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network (HW, TX, QL0, DL, EC, DD, HW, WS), pp. 1064–1072.
KDD-2019-LeeIJCC #named #recommendation
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (HL, JI, SJ, HC, SC), pp. 1073–1082.
KDD-2019-ZhaCLY #algorithm #mining #roadmap
Mining Algorithm Roadmap in Scientific Publications (HZ, WC, KL, XY), pp. 1083–1092.
KDD-2019-Zhang0 #named #performance #similarity
MinJoin: Efficient Edit Similarity Joins via Local Hash Minima (HZ, QZ0), pp. 1093–1103.
KDD-2019-LambaS #modelling #multi #visual notation
Modeling Dwell Time Engagement on Visual Multimedia (HL, NS), pp. 1104–1113.
KDD-2019-DingZP0H #modelling #predict
Modeling Extreme Events in Time Series Prediction (DD, MZ, XP, MY0, XH0), pp. 1114–1122.
KDD-2019-ZhaoDSZLX #learning #multi #network #relational
Multiple Relational Attention Network for Multi-task Learning (JZ, BD, LS, FZ, WL, HX), pp. 1123–1131.
KDD-2019-RashedGS #classification #multi
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings (AR, JG, LST), pp. 1132–1140.
KDD-2019-LiST #classification #higher-order #markov #multi #network #predict #random
Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification (CL, DS, DT), pp. 1141–1151.
KDD-2019-DongBB #network
Network Density of States (KD, ARB, DB), pp. 1152–1161.
KDD-2019-YangRLC #graph #named #recursion #sketching
NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching (DY, PR, BL, PCM), pp. 1162–1172.
KDD-2019-YangAKU #collaboration #named
OBOE: Collaborative Filtering for AutoML Model Selection (CY, YA, DWK, MU), pp. 1173–1183.
KDD-2019-HeXZMZY #learning #multi
Off-policy Learning for Multiple Loggers (LH, LX, WZ, ZMM, YZ, DY), pp. 1184–1193.
KDD-2019-ChenBF #modelling #network #on the
On Dynamic Network Models and Application to Causal Impact (YCC, ASB, JLF), pp. 1194–1204.
KDD-2019-ZhangLBMZ #optimisation
Optimizing Impression Counts for Outdoor Advertising (YZ, YL, ZB, SM, PZ), pp. 1205–1215.
KDD-2019-EsfandiariWAR #learning #online #optimisation
Optimizing Peer Learning in Online Groups with Affinities (ME, DW, SAY, SBR), pp. 1216–1226.
KDD-2019-WangYCW00 #graph #matrix #modelling #predict
Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling (YW, HY, HC, TW, JX0, KZ0), pp. 1227–1235.
KDD-2019-MaystreKG #flexibility
Pairwise Comparisons with Flexible Time-Dynamics (LM, VK, MG), pp. 1236–1246.
KDD-2019-KobrenSM #constraints
Paper Matching with Local Fairness Constraints (AK, BS, AM), pp. 1247–1257.
KDD-2019-DeyZSN #effectiveness #named #personalisation #predict
PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data (SD, PZ, DS, KN), pp. 1258–1268.
KDD-2019-KumarZL #interactive #network #predict
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (SK, XZ, JL), pp. 1269–1278.
KDD-2019-LiHCSWZP #graph #predict
Predicting Path Failure In Time-Evolving Graphs (JL, ZH, HC, JS, PW, JZ, LP), pp. 1279–1289.
KDD-2019-WeiCZWGXL #coordination #learning #named #network
PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
KDD-2019-LiX #data mining #mining #named #privacy #scalability
PrivPy: General and Scalable Privacy-Preserving Data Mining (YL, WX), pp. 1299–1307.
KDD-2019-GaoPH19a #generative #named #network #proximity
ProGAN: Network Embedding via Proximity Generative Adversarial Network (HG, JP, HH), pp. 1308–1316.
KDD-2019-BellettiCC #behaviour #dependence
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs (FB, MC, EHC), pp. 1317–1327.
KDD-2019-YinLHCTW0 #named #representation
QuesNet: A Unified Representation for Heterogeneous Test Questions (YY, QL0, ZH, EC, WT, SW, YS0), pp. 1328–1336.
KDD-2019-TaiebK
Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions (SBT, BK), pp. 1337–1347.
KDD-2019-DiSC #learning
Relation Extraction via Domain-aware Transfer Learning (SD, YS, LC), pp. 1348–1357.
KDD-2019-CenZZYZ0 #learning #multi #network #representation
Representation Learning for Attributed Multiplex Heterogeneous Network (YC, XZ, JZ, HY, JZ, JT0), pp. 1358–1368.
KDD-2019-TangXWZL #learning #multi
Retaining Privileged Information for Multi-Task Learning (FT, CX, FW, JZ, LWHL), pp. 1369–1377.
KDD-2019-RamS #nearest neighbour
Revisiting kd-tree for Nearest Neighbor Search (PR, KS), pp. 1378–1388.
KDD-2019-ZhaoGS #keyword #mining #named
Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (JZ, ZG, HS), pp. 1389–1398.
KDD-2019-ZhuZ00 #graph #network #robust
Robust Graph Convolutional Networks Against Adversarial Attacks (DZ, ZZ, PC0, WZ0), pp. 1399–1407.
KDD-2019-YaoCC #clustering #learning #multi #robust
Robust Task Grouping with Representative Tasks for Clustered Multi-Task Learning (YY, JC0, HC), pp. 1408–1417.
KDD-2019-WuYZXZPXA #graph #kernel #random #scalability #using
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding (LW, IEHY, ZZ0, KX, LZ0, XP0, YX, CCA), pp. 1418–1428.
KDD-2019-YinW #graph #scalability
Scalable Graph Embeddings via Sparse Transpose Proximities (YY, ZW), pp. 1429–1437.
KDD-2019-MonathKKGM #clustering #scalability
Scalable Hierarchical Clustering with Tree Grafting (NM, AK, AK, MRG, AM), pp. 1438–1448.
KDD-2019-FoucheKB #algorithm #multi #scalability
Scaling Multi-Armed Bandit Algorithms (EF, JK, KB), pp. 1449–1459.
KDD-2019-RamanSMZYV #hybrid #parallel #scalability
Scaling Multinomial Logistic Regression via Hybrid Parallelism (PR, SS, SM, XZ, HY, SVNV), pp. 1460–1470.
KDD-2019-ZouZCW0 #optimisation #policy #trust
Separated Trust Regions Policy Optimization Method (LZ, ZZ, YC, XW, WZ0), pp. 1471–1479.
KDD-2019-OhI #detection #learning #using
Sequential Anomaly Detection using Inverse Reinforcement Learning (MhO, GI), pp. 1480–1490.
KDD-2019-HuH #learning #named #network #set
Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
KDD-2019-HulsebosHBZSKDH #approach #data type #detection #learning #named #semantics
Sherlock: A Deep Learning Approach to Semantic Data Type Detection (MH, KZH, MAB, EZ, AS, TK, ÇD, CAH), pp. 1500–1508.
KDD-2019-SavvidesHOP #visualisation
Significance of Patterns in Data Visualisations (RS, AH, EO, KP), pp. 1509–1517.
KDD-2019-Wang0L #recommendation #social
Social Recommendation with Optimal Limited Attention (XW0, WZ0, CL), pp. 1518–1527.
KDD-2019-PellegrinaRV #mining #named #testing
SPuManTE: Significant Pattern Mining with Unconditional Testing (LP, MR, FV), pp. 1528–1538.
KDD-2019-VermaZ #graph #network
Stability and Generalization of Graph Convolutional Neural Networks (SV, ZLZ), pp. 1539–1548.
KDD-2019-ShiZYLSJ #markov #modelling #personalisation #sequence
State-Sharing Sparse Hidden Markov Models for Personalized Sequences (HS, CZ0, QY, YL0, FS, DJ), pp. 1549–1559.
KDD-2019-DeshpandeS #adaptation #modelling #streaming #using
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units (PD, SS), pp. 1560–1568.
KDD-2019-GuoYWCZH #recommendation #streaming
Streaming Session-based Recommendation (LG0, HY, QW, TC, AZ, NQVH), pp. 1569–1577.
KDD-2019-WangYMHLS #named #privacy
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data (ZW, XY, SM, YH, SML, HS), pp. 1578–1586.
KDD-2019-YuGNCPH #constraints #incremental #learning
Tackle Balancing Constraint for Incremental Semi-Supervised Support Vector Learning (SY, BG, KN, HC, JP, HH), pp. 1587–1595.
KDD-2019-YangTTH
Task-Adversarial Co-Generative Nets (PY, QT, HT, JH), pp. 1596–1604.
KDD-2019-WarlopMG #process #recommendation
Tensorized Determinantal Point Processes for Recommendation (RW, JM, MG), pp. 1605–1615.
KDD-2019-DengL #testing
Testing Dynamic Incentive Compatibility in Display Ad Auctions (YD, SL), pp. 1616–1624.
KDD-2019-SunZZSHX #approach #network
The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach (YS, FZ, HZ, XS, QH, HX), pp. 1625–1633.
KDD-2019-JiangZ00C0 #graph #novel #representation
The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model (TJ, TZ, BQ0, TL0, NVC, MJ0), pp. 1634–1642.
KDD-2019-LiC0W0 #3d #crowdsourcing #platform #problem
Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms (BL, YC, YY0, GW, LC0), pp. 1643–1653.
KDD-2019-RizzoVC #policy
Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios (SGR, GV, SC), pp. 1654–1664.
KDD-2019-JiaLZKK #how #learning #question #robust #towards
Towards Robust and Discriminative Sequential Data Learning: When and How to Perform Adversarial Training? (XJ, SL0, HZ, SK, VK), pp. 1665–1673.
KDD-2019-FawazKPSM #network #quantum
Training and Meta-Training Binary Neural Networks with Quantum Computing (AF, PK, SP, SS, PM), pp. 1674–1681.
KDD-2019-WangJCJ #behaviour #named #predict
TUBE: Embedding Behavior Outcomes for Predicting Success (DW, TJ, NVC, MJ0), pp. 1682–1690.
KDD-2019-Zang0S0W #data flow
Uncovering Pattern Formation of Information Flow (CZ, PC0, CS, WZ0, FW0), pp. 1691–1699.
KDD-2019-ZhangFWL0 #learning
Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning (YZ, YF, PW, XL, YZ0), pp. 1700–1708.
KDD-2019-HaoCYSW #concept #knowledge base #learning #ontology #representation
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts (JH, MC, WY, YS, WW), pp. 1709–1719.
KDD-2019-PanLW00Z #learning #predict #using
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (ZP, YL, WW, YY0, YZ0, JZ), pp. 1720–1730.
KDD-2019-BernardiME #lessons learnt #machine learning #modelling
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (LB, TM, PE), pp. 1743–1751.
KDD-2019-ZhouGHZXJLX #collaboration #framework #learning #refinement
A Collaborative Learning Framework to Tag Refinement for Points of Interest (JZ, SG, RH, DZ, JX, AJ, YL, HX), pp. 1752–1761.
KDD-2019-ChenTYZC #approach #data-driven #industrial #multi #problem
A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry (LC0, XT, MY, JZ, LC0), pp. 1762–1770.
KDD-2019-HanNCLHX #approach #generative #recommendation
A Deep Generative Approach to Search Extrapolation and Recommendation (FXH, DN, HC, KL, YH, YX), pp. 1771–1779.
KDD-2019-TangQZWXMZY #approach #multi #order
A Deep Value-network Based Approach for Multi-Driver Order Dispatching (XT, Z(Q, FZ, ZW, ZX, YM, HZ, JY), pp. 1780–1790.
KDD-2019-LiSPDJGBHG #framework
A Generalized Framework for Population Based Training (AL, OS, SP, VD, MJ, CG, DB, TH, PG), pp. 1791–1799.
KDD-2019-ChakrabortyF #framework #identification #robust
A Robust Framework for Accelerated Outcome-driven Risk Factor Identification from EHR (PC, FF), pp. 1800–1808.
KDD-2019-TianGKOCCDEI
A Severity Score for Retinopathy of Prematurity (PT, YG, JKC, SO, JPC, MFC, JGD, DE, SI), pp. 1809–1819.
KDD-2019-ZhaoHYZXY #framework
A Unified Framework for Marketing Budget Allocation (KZ, JH, LY, QZ, HX, CY), pp. 1820–1830.
KDD-2019-LiuGNWXLLX #comprehension #concept #documentation #mining #query
A User-Centered Concept Mining System for Query and Document Understanding at Tencent (BL, WG, DN, CW, SX, JL, KL, YX), pp. 1831–1841.
KDD-2019-LuoHHLZ #named #predict #quality
AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 (ZL, JH, KH, XL, PZ), pp. 1842–1850.
KDD-2019-DecroosBHD
Actions Speak Louder than Goals: Valuing Player Actions in Soccer (TD, LB, JVH, JD), pp. 1851–1861.
KDD-2019-HossainR #learning #process #recognition
Active Deep Learning for Activity Recognition with Context Aware Annotator Selection (HMSH, NR), pp. 1862–1870.
KDD-2019-TaiSC
Adversarial Matching of Dark Net Market Vendor Accounts (XHT, KS, NC), pp. 1871–1880.
KDD-2019-YangSZDGDQZ #automation #named
AiAds: Automated and Intelligent Advertising System for Sponsored Search (XY, DS, RZ, TD, ZG, ZD, SQ, YZ0), pp. 1881–1890.
KDD-2019-TangWYS #named #recommendation
AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation (XT, TW, HY, HS), pp. 1891–1899.
KDD-2019-WangZTWX #named #network #using
AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (JW, YZ0, KT, JW, ZX), pp. 1900–1908.
KDD-2019-ShenVAAHN #learning #monitoring #smarttech #using
Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning (YS, MV, AA, AA, AYH, AYN), pp. 1909–1916.
KDD-2019-RamakrishnanSLS #detection #e-commerce
Anomaly Detection for an E-commerce Pricing System (JR, ES, CL, MAS), pp. 1917–1926.
KDD-2019-HaldarARXYDZBTC #learning
Applying Deep Learning to Airbnb Search (MH, MA, PR, TX, SY, HD, QZ, NBW, BCT, BMC, TL), pp. 1927–1935.
KDD-2019-LuoWZYTCDY #automation #named
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications (YL, MW, HZ, QY, WWT, YC, WD, QY0), pp. 1936–1945.
KDD-2019-JinSH #architecture #named #performance
Auto-Keras: An Efficient Neural Architecture Search System (HJ, QS, XH), pp. 1946–1956.
KDD-2019-LiuWXLY #automation #generative #summary
Automatic Dialogue Summary Generation for Customer Service (CL, PW, JX, ZL, JY), pp. 1957–1965.
KDD-2019-YangLWWTXG #multi #optimisation
Bid Optimization by Multivariable Control in Display Advertising (XY, YL, HW, DW, QT, JX, KG), pp. 1966–1974.
KDD-2019-SinghWWWKMDLK #migration #predict #social #social media
Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration (LS, LW, YW, YW, CK, SM, KD, YL, KK), pp. 1975–1983.
KDD-2019-GuoHJZW0 #behaviour #multi #network #predict #realtime #using
Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior (LG, LH, RJ, BZ, XW, BC0), pp. 1984–1992.
KDD-2019-AharonSSSTVD #optimisation
Carousel Ads Optimization in Yahoo Gemini Native (MA, OS, AS, AS, BT, HV, DD), pp. 1993–2001.
KDD-2019-TokuiOANOSSUVV #framework #learning #named #research
Chainer: A Deep Learning Framework for Accelerating the Research Cycle (ST, RO, TA, YN, TO, SS, SS, KU, BV, HYV), pp. 2002–2011.
KDD-2019-XiaLGLCS #case study #detection #mobile #network #privacy #social
Characterizing and Detecting Malicious Accounts in Privacy-Centric Mobile Social Networks: A Case Study (ZX, CL, NZG, QL0, YC0, DS), pp. 2012–2022.
KDD-2019-LiuSPR #case study #graph
Characterizing and Forecasting User Engagement with In-App Action Graph: A Case Study of Snapchat (YL, XS, LP, XR), pp. 2023–2031.
KDD-2019-LiQWM #network #ranking
Combining Decision Trees and Neural Networks for Learning-to-Rank in Personal Search (PL0, ZQ, XW, DM), pp. 2032–2040.
KDD-2019-ZheSX #community #detection #network #scalability
Community Detection on Large Complex Attribute Network (CZ, AS, XX), pp. 2041–2049.
KDD-2019-KobrenBYHL #knowledge base #precise
Constructing High Precision Knowledge Bases with Subjective and Factual Attributes (AK, PB0, OY, JH, IL), pp. 2050–2058.
KDD-2019-SchulzeMRLB #identification #using
Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy (JPS, AM, ER, HAL, KB), pp. 2059–2068.
KDD-2019-KitadaIS #effectiveness #multi #network #predict #using
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives (SK, HI, YS), pp. 2069–2077.
KDD-2019-OuyangZLZXLD #network #predict
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction (WO, XZ, LL, HZ, XX, ZL, YD), pp. 2078–2086.
KDD-2019-WangLYLLZ0 #approach #machine learning #nondeterminism #quantifier
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting (BW, JL0, ZY0, HL, TL, YZ0, GZ0), pp. 2087–2095.
KDD-2019-SiciliaPG #named #using
DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data (AS, KP, KG), pp. 2096–2104.
KDD-2019-LeeIFSM #approach #data-driven #estimation #named #using
DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery (SL, SI, MF, PJS, SM), pp. 2105–2113.
KDD-2019-JiangSHSXCWKS #named #predict
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events (RJ, XS, DH, XS, TX, ZC, ZW, KSK, RS), pp. 2114–2122.
KDD-2019-TariqLSLJCW #detection #multi #probability #using
Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA (ST, SL, YS, MSL, OJ, DC, SSW), pp. 2123–2133.
KDD-2019-YelundurCM #composition #detection #multi #overview
Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition (ARY, VC, BM), pp. 2134–2144.
KDD-2019-ChenJMFKSPSYMSS #metric #multimodal
Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams (RC, FJ, NM, LF, LK, AS, MP, JS, RY, VM, MS, HHS, HJJ, BT, AT), pp. 2145–2155.
KDD-2019-FabijanGGOQVD #online #taxonomy
Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners (AF, JG, SG, JO, WQ, LV, PAD), pp. 2156–2164.
KDD-2019-QinZZXZMZX #named #personalisation #perspective #recommendation
DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview (CQ, HZ, CZ, TX, FZ, CM, JZ, HX), pp. 2165–2173.
KDD-2019-ShuklaKOMY
Dynamic Pricing for Airline Ancillaries with Customer Context (NS, AK, KO, LM, KY), pp. 2174–2182.
KDD-2019-BabaevSTU #learning
E.T.-RNN: Applying Deep Learning to Credit Loan Applications (DB, MS, AT, DU), pp. 2183–2190.
KDD-2019-WagstaffDDACCDP #detection
Enabling Onboard Detection of Events of Scientific Interest for the Europa Clipper Spacecraft (KLW, GD, AD, SA, SC, MC, ID, CAP), pp. 2191–2201.
KDD-2019-YangSLPB #biology
Estimating Cellular Goals from High-Dimensional Biological Data (LY, MAS, JCL, BOP, JB), pp. 2202–2211.
KDD-2019-BeutelCDQWWHZHC #ranking #recommendation
Fairness in Recommendation Ranking through Pairwise Comparisons (AB, JC, TD, HQ, LW, YW, LH, ZZ, LH, EHC, CG), pp. 2212–2220.
KDD-2019-GeyikAK #ranking #recommendation
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search (SCG, SA, KK), pp. 2221–2231.
KDD-2019-HuNYZ #collaboration #distributed #framework #machine learning #named
FDML: A Collaborative Machine Learning Framework for Distributed Features (YH, DN, JY, SZ), pp. 2232–2240.
KDD-2019-TuLYC #approach #feedback #modelling
Feedback Shaping: A Modeling Approach to Nurture Content Creation (YT, CL, YY, SC), pp. 2241–2250.
KDD-2019-deWetO #learning
Finding Users Who Act Alike: Transfer Learning for Expanding Advertiser Audiences (Sd, JO), pp. 2251–2259.
KDD-2019-SahooHKWLALH #image #learning #named #recognition
FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging (DS, WH, SK, XW, HL, PA, EPL, SCHH), pp. 2260–2268.
KDD-2019-HughesCZ #generative #learning
Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning (JWH, KhC, RZ), pp. 2269–2277.
KDD-2019-ZhengXKWMAY #linear #predict #using
Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression (YZ, LX, TK, JW0, HM, RA, KY), pp. 2278–2286.
KDD-2019-ChenLBCZLTWDCSW #realtime
Gmail Smart Compose: Real-Time Assisted Writing (MXC, BNL, GB, YC, SZ, JL, JT, YW, AMD, ZC, TS, YW), pp. 2287–2295.
KDD-2019-AroraC0KLLMTTW #scalability
Hard to Park?: Estimating Parking Difficulty at Scale (NA, JC, RK0, IK, YL, HJL, AM, AT, IT, YW), pp. 2296–2304.
KDD-2019-ZhangHDV #how #lessons learnt
How to Invest my Time: Lessons from Human-in-the-Loop Entity Extraction (SZ, LH, ECD, SV), pp. 2305–2313.
KDD-2019-LiuTZLDX #multi #named #personalisation #recommendation
Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System (HL, YT, PZ, XL, JD, HX), pp. 2314–2324.
KDD-2019-HwangOCPM #machine learning
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning (JH, PO, JC, KP, LM), pp. 2325–2335.
KDD-2019-Ding0LXZSJS #realtime #recommendation
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation (XD, JT0, TXL, CX, YZ, FS, QJ, DS), pp. 2336–2346.
KDD-2019-0009ZGZNQH #framework #graph #named #recommendation #scalability
IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation (JZ0, ZZ, ZG, WZ0, WN, GQ, XH), pp. 2347–2357.
KDD-2019-GuptaCY #optimisation
Internal Promotion Optimization (RG, GC, SY), pp. 2358–2366.
KDD-2019-LuSPHM #mining
Investigate Transitions into Drug Addiction through Text Mining of Reddit Data (JL, SS, RP, MAH, GM), pp. 2367–2375.
KDD-2019-ChenZBXL #behaviour #predict #what
Investment Behaviors Can Tell What Inside: Exploring Stock Intrinsic Properties for Stock Trend Prediction (CC, LZ, JB0, CX, TYL), pp. 2376–2384.
KDD-2019-JhaWYWFLCA #framework #named
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery (DJ, LTW, ZY, CW, ITF, WkL, ANC, AA), pp. 2385–2393.
KDD-2019-HwangLVGXN #framework #scalability #video
Large-Scale Training Framework for Video Annotation (SJH, JL, BV, AG, ZX, AN), pp. 2394–2402.
KDD-2019-MaZXLCXWW #comprehension #framework #scalability
Large-scale User Visits Understanding and Forecasting with Deep Spatial-Temporal Tensor Factorization Framework (XM, LZ, LX, ZL, GC, ZX, YW, ZW), pp. 2403–2411.
KDD-2019-ZhaiWTPR #learning #visual notation
Learning a Unified Embedding for Visual Search at Pinterest (AZ, HYW, ET, DHP, CR), pp. 2412–2420.
KDD-2019-ParkLHHLC #learning #quality
Learning Sleep Quality from Daily Logs (SP, CTL, SH, CH, SWL, MC), pp. 2421–2429.
KDD-2019-KillianWSCDT #learning #using
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data (JAK, BW, AS, VC, BD, MT), pp. 2430–2438.
KDD-2019-GengLLJXZYLZ #named #network #predict
LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction (YaG, QL, TL, LJ, LX, DZ, WY, WL, YZ), pp. 2439–2447.
KDD-2019-AhmedABCCDDEFFG #machine learning #ml
Machine Learning at Microsoft with ML.NET (ZA, SA, MB, RC, WSC, YD, XD, VE, SF, TF, AG, MH, SI, MI, NK, GK, PL, IM, SM, SM, GN, JO, GO, AP, JP, PR, MZS, MW, SZ, YZ), pp. 2448–2458.
KDD-2019-SrivastavaHK #approach #machine learning
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning (MS, HH, AK), pp. 2459–2468.
KDD-2019-YeS #named #online #ranking
MediaRank: Computational Ranking of Online News Sources (JY, SS), pp. 2469–2477.
KDD-2019-FanZHSHML #graph #network #recommendation
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation (SF, JZ, XH, CS, LH, BM, YL), pp. 2478–2486.
KDD-2019-ZhangTDZW #health #named #predict #risk management
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (XSZ, FT, HHD, JZ, FW), pp. 2487–2495.
KDD-2019-FanGZMSL #generative #named #towards
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search (MF, JG, SZ, SM, MS, PL0), pp. 2509–2517.
KDD-2019-TangBMLLK #classification #e-commerce #image #named #scalability
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data (YT, FB, SM, YL, YL, SK), pp. 2518–2526.
KDD-2019-FanZPLZYWWPH #learning #multi
Multi-Horizon Time Series Forecasting with Temporal Attention Learning (CF, YZ, YP, XL, CZ0, RY, DW, WW, JP, HH), pp. 2527–2535.
KDD-2019-TaoLZZWFC #detection #game studies #multi #named #network #online
MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games (JT, JL, SZ, SZ, RW, CF, PC), pp. 2536–2546.
KDD-2019-RawatLY #learning #multi #using
Naranjo Question Answering using End-to-End Multi-task Learning Model (BPSR, FL, HY0), pp. 2547–2555.
KDD-2019-LiuCGD #parametricity #predict
Nonparametric Mixture of Sparse Regressions on Spatio-Temporal Data - An Application to Climate Prediction (YL, JC, ARG, JGD), pp. 2556–2564.
KDD-2019-KapoorLK #3d #image #modelling #named
Nostalgin: Extracting 3D City Models from Historical Image Data (AK, HL, RK), pp. 2565–2575.
KDD-2019-WuWAHHX #named #personalisation #recommendation
NPA: Neural News Recommendation with Personalized Attention (CW, FW, MA, JH, YH, XX0), pp. 2576–2584.
KDD-2019-ZhangLTDYZGWSLW #graph #named #scalability #towards
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (FZ, XL, JT, YD, PY, JZ, XG, YW, BS, RL, KW), pp. 2585–2595.
KDD-2019-WengZBT #named #performance
OCC: A Smart Reply System for Efficient In-App Communications (YW, HZ, FB, GT), pp. 2596–2603.
KDD-2019-Yeh0DDNK #monitoring #online #realtime
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring (CCMY, YZ0, HAD, AD, MN, EJK), pp. 2604–2612.
KDD-2019-HuangWZZZYC #behaviour #modelling #multi #online #predict
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics (CH0, XW, XZ, CZ, JZ, DY, NVC), pp. 2613–2622.
KDD-2019-AkibaSYOK #framework #named #optimisation
Optuna: A Next-generation Hyperparameter Optimization Framework (TA, SS, TY, TO, MK), pp. 2623–2631.
KDD-2019-ZhaoZWGGQNCL #learning #multi #personalisation
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning (WZ0, BZ, BW, ZG, WG, GQ, WN, JC, HL), pp. 2632–2642.
KDD-2019-KrausF #personalisation #predict #sequence
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching (MK, SF), pp. 2643–2652.
KDD-2019-ZhuangL #multi #named
PinText: A Multitask Text Embedding System in Pinterest (JZ, YL), pp. 2653–2661.
KDD-2019-ChenHXGGSLPZZ #generative #named #personalisation #recommendation
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion (WC, PH, JX, XG, CG, FS, CL, AP, HZ, BZ), pp. 2662–2670.
KDD-2019-PiBZZG #behaviour #modelling #predict
Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (QP, WB, GZ, XZ, KG), pp. 2671–2679.
KDD-2019-LebedevIRGMOGBS
Precipitation Nowcasting with Satellite Imagery (VL, VI, IR, AG, AM, SO, RG, IB, DS), pp. 2680–2688.
KDD-2019-PanMRSF #learning #multi #online #predict
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising (JP, YM, ALR, YS, AF), pp. 2689–2697.
KDD-2019-SheehanMTUJBLE #development #predict #using #wiki
Predicting Economic Development using Geolocated Wikipedia Articles (ES, CM, MT, BU, NJ, MB, DBL, SE), pp. 2698–2706.
KDD-2019-YabeTSSU #behaviour #predict #using #web
Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior (TY, KT, TS, YS, SVU), pp. 2707–2717.
KDD-2019-NosakhareP #behaviour #modelling #probability
Probabilistic Latent Variable Modeling for Assessing Behavioral Influences on Well-Being (EN, RWP), pp. 2718–2726.
KDD-2019-SvyatkovskiyZFS #code completion #named
Pythia: AI-assisted Code Completion System (AS, YZ, SF, NS), pp. 2727–2735.
KDD-2019-ZhaoWSNHJSNBRPL #detection #power management #smarttech
Raise to Speak: An Accurate, Low-power Detector for Activating Voice Assistants on Smartwatches (SZ, BW, SS, HN, RH, MJ, KS, BN, MB, SR, TP, KL, CG), pp. 2736–2744.
KDD-2019-RolnickAPKMN #clustering #design #random
Randomized Experimental Design via Geographic Clustering (DR, KA, JPA, SK, VSM, AN), pp. 2745–2753.
KDD-2019-JiangYBLWM #ranking
Ranking in Genealogy: Search Results Fusion at Ancestry (PJ, YY, GB, FAL, RW, AM), pp. 2754–2764.
KDD-2019-LiuGZL #realtime #recommendation
Real-time Attention Based Look-alike Model for Recommender System (YL, KG, XZ, LL), pp. 2765–2773.
KDD-2019-FedoryszakFRZ #data type #detection #realtime #social
Real-time Event Detection on Social Data Streams (MF, BF, VR, CZ), pp. 2774–2782.
KDD-2019-OchiaiSYTF #realtime #recommendation #smarttech
Real-time On-Device Troubleshooting Recommendation for Smartphones (KO, KS, NY, YT, YF), pp. 2783–2791.
KDD-2019-OkoshiTT #adaptation #deployment #scheduling #smarttech
Real-World Product Deployment of Adaptive Push Notification Scheduling on Smartphones (TO, KT, HT), pp. 2792–2800.
KDD-2019-GrislainPT #network #probability #realtime
Recurrent Neural Networks for Stochastic Control in Real-Time Bidding (NG, NP0, AT), pp. 2801–2809.
KDD-2019-ZouXDS0Y #learning #recommendation
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems (LZ, LX, ZD, JS, WL0, DY), pp. 2810–2818.
KDD-2019-KalraWB0 #predict
Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising (AK, CW, CB, YC0), pp. 2819–2827.
KDD-2019-SuZNLSP #detection #multi #network #probability #robust
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (YS, YZ, CN, RL, WS, DP), pp. 2828–2837.
KDD-2019-LinSQL0ZJ #precise #process #realtime #robust
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement (ML, XS, QQ, HL, LS0, SZ, RJ), pp. 2838–2847.
KDD-2019-Li0WGYK #adaptation #kernel #learning #multi #predict
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points (ZL, JZ0, QW0, YG, JY, CK), pp. 2848–2856.
KDD-2019-TranS #feature model #predict #scalability
Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs (TQT, JS), pp. 2857–2866.
KDD-2019-BiswasPVSN #interactive #named #realtime
Seeker: Real-Time Interactive Search (AB, TTP, MV, BS, HN), pp. 2867–2875.
KDD-2019-NigamSMLDSTGY #semantics
Semantic Product Search (PN, YS, VM, VL, WAD, AS, CHT, HG, BY), pp. 2876–2885.
KDD-2019-SpathisRFMR #learning #multi #self #sequence
Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data (DS, SSR, KF, CM, JR), pp. 2886–2894.
KDD-2019-DuWYZT #online #recommendation
Sequential Scenario-Specific Meta Learner for Online Recommendation (ZD, XW, HY, JZ, JT0), pp. 2895–2904.
KDD-2019-MoosaviS00R #scalability
Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data (SM, MHS, AN0, SP0, RR), pp. 2905–2913.
KDD-2019-DimmeryBS #online
Shrinkage Estimators in Online Experiments (DD, EB, JS), pp. 2914–2922.
KDD-2019-JinHSWLSK #email #network
Smart Roles: Inferring Professional Roles in Email Networks (DJ, MH, TS, MW, WL, LS, DK), pp. 2923–2933.
KDD-2019-ChenreddyPNCA #learning #named #optimisation
SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
KDD-2019-YanYOZBWH #social #validation
Social Skill Validation at LinkedIn (XY, JY, MO, LZ, JB, SW, QH), pp. 2943–2951.
KDD-2019-ChowdhurySM #detection
Structured Noise Detection: Application on Well Test Pressure Derivative Data (FAC, SS, AM), pp. 2952–2960.
KDD-2019-SheetritNKS #predict #probability
Temporal Probabilistic Profiles for Sepsis Prediction in the ICU (ES, NN, DK, YS), pp. 2961–2969.
KDD-2019-PasumarthiBWLBN #library #named #ranking #scalability
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (RKP, SB, XW, CL0, MB, MN, JP, NG, RA, SW), pp. 2970–2978.
KDD-2019-SchonD0 #fault #how #predict #using
The Error is the Feature: How to Forecast Lightning using a Model Prediction Error (CS, JD, RM0), pp. 2979–2988.
KDD-2019-YinH #analysis #estimation #identification #testing
The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis (XY, LH), pp. 2989–2999.
KDD-2019-YeS19a #social #social media
The Secret Lives of Names?: Name Embeddings from Social Media (JY, SS), pp. 3000–3008.
KDD-2019-RenXWYHKXYTZ #detection
Time-Series Anomaly Detection Service at Microsoft (HR, BX, YW, CY, CH, XK, TX, MY, JT, QZ), pp. 3009–3017.
KDD-2019-ZhouMZ #memory management #network #personalisation #recommendation #topic
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation (XZ, CM, ZZ), pp. 3018–3028.
KDD-2019-Venkataraman0 #identification #towards
Towards Identifying Impacted Users in Cellular Services (SV, JW0), pp. 3029–3039.
KDD-2019-ChenLZYZ0 #e-commerce #generative #knowledge-based #personalisation #towards
Towards Knowledge-Based Personalized Product Description Generation in E-commerce (QC, JL, YZ, HY, JZ, JT0), pp. 3040–3050.
KDD-2019-FauvelMFFT #detection #machine learning #towards
Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection (KF, VM, ÉF, PF, AT), pp. 3051–3059.
KDD-2019-Pan0Z00 #named
TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme (ZP, JB0, WZ0, YY0, YZ0), pp. 3060–3070.
KDD-2019-SuzukiWN #learning #scheduling
TV Advertisement Scheduling by Learning Expert Intentions (YS, WMW, IN), pp. 3071–3081.
KDD-2019-SuhrBZGC #case study #framework #platform
Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform (TS, AJB, MZ, KPG, AC), pp. 3082–3092.
KDD-2019-LuY0ZXLL0 #social
Uncovering the Co-driven Mechanism of Social and Content Links in User Churn Phenomena (YL, LY, PC0, CZ, RX, YL, LL, WZ0), pp. 3093–3101.
KDD-2019-ZhouMGBB #comprehension #network #using
Understanding Consumer Journey using Attention based Recurrent Neural Networks (YZ, SM, JG, TB, NB), pp. 3102–3111.
KDD-2019-JiangSHHH #comprehension #e-commerce
Understanding the Role of Style in E-commerce Shopping (HJ, AS, AH, DH, LH), pp. 3112–3120.
KDD-2019-WengCS
Unsupervised Clinical Language Translation (WHW, YAC, PS), pp. 3121–3131.
KDD-2019-LiangOJRLZRZ #fine-grained #named
UrbanFM: Inferring Fine-Grained Urban Flows (YL, KO, LJ, SR, YL0, JZ, DSR, YZ0), pp. 3132–3142.
KDD-2019-ChenLPS #predict #twitter #using
Using Twitter to Predict When Vulnerabilities will be Exploited (HC0, RL, NP, VSS), pp. 3143–3152.
KDD-2019-DingGV #constraints #optimisation
Whole Page Optimization with Global Constraints (WD, DG, SVNV), pp. 3153–3161.
KDD-2019-Guestrin #machine learning
4 Perspectives in Human-Centered Machine Learning (CG), p. 3162.
KDD-2019-Bradley #challenge #complexity #set
Addressing Challenges in Data Science: Scale, Skill Sets and Complexity (JB), p. 3163.
KDD-2019-Srivastava
AI for Small Businesses and Consumers: Applications and Innovations (AS), p. 3164.
KDD-2019-Yang #framework #graph #named #network #platform
AliGraph: A Comprehensive Graph Neural Network Platform (HY), pp. 3165–3166.
KDD-2019-Thondikulam #approach #ml #scalability
Analytics Journey Map: An Approach Enable to ML at Scale (GT), p. 3167.
KDD-2019-Sawaf #ml
Applications of AI/ML in Established and New Industries (HS), p. 3168.
KDD-2019-Chellapilla #hardware #self
Building a Better Self-Driving Car: Hardware, Software, and Knowledge (KC), p. 3169.
KDD-2019-Xu #challenge
Data Science Challenges @ LinkedIn (YX), p. 3170.
KDD-2019-Nemani #generative
Earth Observations from a New Generation of Geostationary Satellites (RRN), p. 3171.
KDD-2019-Heckerman #big data
Exploiting High Dimensionality in Big Data (DH), p. 3172.
KDD-2019-Maas #facebook
Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery (PM), p. 3173.
KDD-2019-Caruana #black box #machine learning #modelling
Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning (RC), p. 3174.
KDD-2019-Sundaresan #developer #scalability
From Code to Data: AI at Scale for Developer Productivity (NS), p. 3175.
KDD-2019-Salakhutdinov #learning
Integrating Domain-Knowledge into Deep Learning (RS), p. 3176.
KDD-2019-LiakhovichD #machine learning
Preventing Rhino Poaching through Machine Learning (OL, GDC), p. 3177.
KDD-2019-Rosales #ecosystem #optimisation
Product Ecosystem Optimization at LinkedIn (RR), p. 3178.
KDD-2019-Xia #analysis #graph #platform
Roll of Unified Graph Analysis Platforms (YX), p. 3179.
KDD-2019-Grewal
Seven Years of Data Science at Airbnb (EG), p. 3180.
KDD-2019-Ranganathan
Spinning the AI Pinwheel (JR), p. 3181.
KDD-2019-KatsiapisH #ml #towards
Towards ML Engineering with TensorFlow Extended (TFX) (KK, KH), p. 3182.
KDD-2019-Ye #approach #data-driven #named
Transportation: A Data Driven Approach (JY), p. 3183.
KDD-2019-Gollapudi #online
Welfare Maximization in Online Two-sided Marketplaces (SG), p. 3184.
KDD-2019-CautisMT #adaptation
Adaptive Influence Maximization (BC, SM, NT), pp. 3185–3186.
KDD-2019-Lin #classification #multi #roadmap
Advances in Cost-sensitive Multiclass and Multilabel Classification (HTL), pp. 3187–3188.
KDD-2019-ShiDGF #challenge #online
Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments (XS, PAD, SG, XF), pp. 3189–3190.
KDD-2019-ShangSL0 #mining #network
Constructing and Mining Heterogeneous Information Networks from Massive Text (JS, JS, LL, JH0), pp. 3191–3192.
KDD-2019-DongR #integration #machine learning
Data Integration and Machine Learning: A Natural Synergy (XLD, TR), pp. 3193–3194.
KDD-2019-XiaoS #data mining #development #mining #named #tutorial
Tutorial: Data Mining Methods for Drug Discovery and Development (CX, JS), pp. 3195–3196.
KDD-2019-Chien #comprehension #learning #mining
Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDD-2019-GuoGSLZCA #natural language #recommendation
Deep Natural Language Processing for Search and Recommender Systems (WG, HG, JS, BL, LZ, BCC, DA), pp. 3199–3200.
KDD-2019-Qin0Y #learning
Deep Reinforcement Learning with Applications in Transportation (Z(Q, JT0, JY), pp. 3201–3202.
KDD-2019-GadeGKMT #industrial
Explainable AI in Industry (KG, SCG, KK, VM, AT), pp. 3203–3204.
KDD-2019-BirdHKKM #challenge #lessons learnt #machine learning
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (SB, BH, KK, EK, MM), pp. 3205–3206.
KDD-2019-ZafaraniZSL #detection #problem #research
Fake News Research: Theories, Detection Strategies, and Open Problems (RZ, XZ, KS, HL0), pp. 3207–3208.
KDD-2019-FaloutsosFGJW #theory and practice
Forecasting Big Time Series: Theory and Practice (CF, VF, JG, TJ, YW), pp. 3209–3210.
KDD-2019-Ramdas #scalability
Foundations of Large-Scale Sequential Experimentation (AR), pp. 3211–3212.
KDD-2019-ZhouH #representation
Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation (DZ, JH), pp. 3213–3214.
KDD-2019-PellegrinaRV19a #mining #statistics #testing
Hypothesis Testing and Statistically-sound Pattern Mining (LP, MR, FV), pp. 3215–3216.
KDD-2019-Eliassi-RadCL #bias #network
Incompleteness in Networks: Biases, Skewed Results, and Some Solutions (TER, RSC, TL), pp. 3217–3218.
KDD-2019-Kovalerchuk #information management #visual notation
Interpretable Knowledge Discovery Reinforced by Visual Methods (BK), pp. 3219–3220.
KDD-2019-Huang0DLLPST0Y0 #algorithm #learning #network #theory and practice
Learning From Networks: Algorithms, Theory, and Applications (XH, PC0, YD, JL, HL0, JP, LS, JT0, FW0, HY, WZ0), pp. 3221–3222.
KDD-2019-SpiliopoulouP #comprehension #mining
Mining and Model Understanding on Medical Data (MS, PP), pp. 3223–3224.
KDD-2019-RozenshteinG #mining #network
Mining Temporal Networks (PR, AG), pp. 3225–3226.
KDD-2019-YanXL #modelling #process
Modeling and Applications for Temporal Point Processes (JY, HX, LL), pp. 3227–3228.
KDD-2019-VreekenY #data mining #mining #theory and practice
Modern MDL meets Data Mining Insights, Theory, and Practice (JV, KY), pp. 3229–3230.
KDD-2019-ZhouM0H #education #learning #optimisation
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching (YZ, FM, JG0, JH), pp. 3231–3232.
KDD-2019-Chen0 #data mining #machine learning #mining #optimisation #order #robust
Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning (PYC, SL0), pp. 3233–3234.
KDD-2019-ZarrinkalamFB #mining #social
Social User Interest Mining: Methods and Applications (FZ, HF, EB), pp. 3235–3236.
KDD-2019-Ning00LR #identification
Spatio-temporal Event Forecasting and Precursor Identification (YN, LZ0, FC0, CTL, HR), pp. 3237–3238.
KDD-2019-MartinM #network #quality #statistics
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks (CHM, MWM), pp. 3239–3240.
KDD-2019-AnastasiuRT #named #order #problem #tutorial
Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems (DCA, HR, AT), pp. 3241–3242.

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