-
Notifications
You must be signed in to change notification settings - Fork 200
/
data.json
1 lines (1 loc) · 142 KB
/
data.json
1
[{"tag": ["ABTest"], "name": "Overlapping Experiment Infrastructure - More, Better, Faster Experimentation", "category": "ABTest", "authors": ["Diane Tang", "Ashish Agarwal", "Deirdre O'Brein", "Mike Meyer"], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ABTest/Overlapping Experiment Infrastructure - More, Better, Faster Experimentation.pdf", "year": 1900, "id": 0}, {"tag": ["Calibration"], "name": "Attended Temperature Scaling - A Practical Approach for Calibrating Deep Neural Networks", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Attended Temperature Scaling - A Practical Approach for Calibrating Deep Neural Networks.pdf", "year": 1900, "id": 1}, {"tag": ["Calibration"], "name": "Beta calibration - a well-founded and easily implemented improvement on logistic calibration for binary classifiers", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Beta calibration - a well-founded and easily implemented improvement on logistic calibration for binary classifiers.pdf", "year": 1900, "id": 2}, {"tag": ["Calibration"], "name": "Beyond temperature scaling - Obtaining well-calibrated multiclass probabilities with Dirichlet calibration", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Beyond temperature scaling - Obtaining well-calibrated multiclass probabilities with Dirichlet calibration.pdf", "year": 1900, "id": 3}, {"tag": ["Calibration"], "name": "Calibrated Recommendations", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Calibrated Recommendations.pdf", "year": 1900, "id": 4}, {"tag": ["Calibration"], "name": "Calibrating User Response Predictions in Online Advertising", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Calibrating User Response Predictions in Online Advertising.pdf", "year": 1900, "id": 5}, {"tag": ["Calibration"], "name": "CALIBRATION OF NEURAL NETWORKS USING SPLINES", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/CALIBRATION OF NEURAL NETWORKS USING SPLINES.pdf", "year": 1900, "id": 6}, {"tag": ["Calibration"], "name": "Crank up the volume - preference bias amplificationin collaborative recommendation", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Crank up the volume - preference bias amplificationin collaborative recommendation.pdf", "year": 1900, "id": 7}, {"tag": ["Calibration"], "name": "Distribution-free calibration guarantees for histogram binning without sample splitting", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Distribution-free calibration guarantees for histogram binning without sample splitting.pdf", "year": 1900, "id": 8}, {"tag": ["Calibration"], "name": "Field-aware Calibration - A Simple and Empirically Strong Method for Reliable Probabilistic Predictions", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Field-aware Calibration - A Simple and Empirically Strong Method for Reliable Probabilistic Predictions.pdf", "year": 1900, "id": 9}, {"tag": ["Calibration"], "name": "MBCT - Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/MBCT - Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration.pdf", "year": 1900, "id": 10}, {"tag": ["Calibration"], "name": "Measuring Calibration in Deep Learning", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Measuring Calibration in Deep Learning.pdf", "year": 1900, "id": 11}, {"tag": ["Calibration"], "name": "Mitigating Bias in Calibration Error Estimation", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Mitigating Bias in Calibration Error Estimation.pdf", "year": 1900, "id": 12}, {"tag": ["Calibration"], "name": "Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers.pdf", "year": 1900, "id": 13}, {"tag": ["Calibration"], "name": "Obtaining Well Calibrated Probabilities Using Bayesian Binning", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Obtaining Well Calibrated Probabilities Using Bayesian Binning.pdf", "year": 1900, "id": 14}, {"tag": ["Calibration"], "name": "On Calibration of Modern Neural Networks", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/On Calibration of Modern Neural Networks.pdf", "year": 1900, "id": 15}, {"tag": ["Calibration"], "name": "Posterior Probability Matters - Doubly-Adaptive Calibration for Neural Predictions in Online Advertising", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Posterior Probability Matters - Doubly-Adaptive Calibration for Neural Predictions in Online Advertising.pdf", "year": 1900, "id": 16}, {"tag": ["Calibration"], "name": "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.pdf", "year": 1900, "id": 17}, {"tag": ["Calibration"], "name": "Transforming Classifier Scores into Accurate Multiclass Probability Estimates", "category": "Calibration", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Calibration/Transforming Classifier Scores into Accurate Multiclass Probability Estimates.pdf", "year": 1900, "id": 18}, {"tag": ["CausalInference"], "name": "Causal Inference in Recommender Systems - A Survey and Future Directions", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Causal Inference in Recommender Systems - A Survey and Future Directions.pdf", "year": 1900, "id": 19}, {"tag": ["CausalInference"], "name": "CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/CauseRec - Counterfactual User Sequence Synthesis for Sequential Recommendation.pdf", "year": 1900, "id": 20}, {"tag": ["CausalInference"], "name": "Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Clicks can be Cheating - Counterfactual Recommendation for Mitigating Clickbait Issue.pdf", "year": 1900, "id": 21}, {"tag": ["CausalInference"], "name": "Counterfactual Data-Augmented Sequential Recommendation", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Counterfactual Data-Augmented Sequential Recommendation.pdf", "year": 1900, "id": 22}, {"tag": ["CausalInference"], "name": "Deconfounded Recommendation for Alleviating Bias Amplification", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Deconfounded Recommendation for Alleviating Bias Amplification.pdf", "year": 1900, "id": 23}, {"tag": ["CausalInference"], "name": "Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random.pdf", "year": 1900, "id": 24}, {"tag": ["CausalInference"], "name": "Improving Ad Click Prediction by Considering Non-displayed Events", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Improving Ad Click Prediction by Considering Non-displayed Events.pdf", "year": 1900, "id": 25}, {"tag": ["CausalInference"], "name": "Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System.pdf", "year": 1900, "id": 26}, {"tag": ["CausalInference"], "name": "Practical Counterfactual Policy Learning for Top-K Recommendations", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Practical Counterfactual Policy Learning for Top-K Recommendations.pdf", "year": 1900, "id": 27}, {"tag": ["CausalInference"], "name": "Recommendations as Treatments - Debiasing Learning and Evaluation", "category": "CausalInference", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/CausalInference/Recommendations as Treatments - Debiasing Learning and Evaluation.pdf", "year": 1900, "id": 28}, {"tag": ["Cold-Start"], "name": "A Practical Exploration System for Search Advertising", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/A Practical Exploration System for Search Advertising.pdf", "year": 1900, "id": 29}, {"tag": ["Cold-Start"], "name": "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps.pdf", "year": 1900, "id": 30}, {"tag": ["Cold-Start"], "name": "Addressing the Item Cold-start Problem by Attribute-driven Active Learning", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Addressing the Item Cold-start Problem by Attribute-driven Active Learning.pdf", "year": 1900, "id": 31}, {"tag": ["Cold-Start"], "name": "Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework.pdf", "year": 1900, "id": 32}, {"tag": ["Cold-Start"], "name": "Cold-start Sequential Recommendation via Meta Learner", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Cold-start Sequential Recommendation via Meta Learner.pdf", "year": 1900, "id": 33}, {"tag": ["Cold-Start"], "name": "GIFT - Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/GIFT - Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction.pdf", "year": 1900, "id": 34}, {"tag": ["Cold-Start"], "name": "Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Handling User Cold Start Problem in Recommender Systems Using Fuzzy Clustering.pdf", "year": 1900, "id": 35}, {"tag": ["Cold-Start"], "name": "Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks.pdf", "year": 1900, "id": 36}, {"tag": ["Cold-Start"], "name": "MAMO - Memory-Augmented Meta-Optimization for Cold-start Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MAMO - Memory-Augmented Meta-Optimization for Cold-start Recommendation.pdf", "year": 1900, "id": 37}, {"tag": ["Cold-Start"], "name": "Telepath - Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Telepath - Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems.pdf", "year": 1900, "id": 38}, {"tag": ["Cold-Start"], "name": "Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Transform Cold-Start Users into Warm via Fused Behaviors in Large-Scale Recommendation.pdf", "year": 1900, "id": 39}, {"tag": ["Cold-Start"], "name": "Warm Up Cold-start Advertisements - Improving CTR Predictions via Learning to Learn ID Embeddings", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Warm Up Cold-start Advertisements - Improving CTR Predictions via Learning to Learn ID Embeddings.pdf", "year": 1900, "id": 40}, {"tag": ["Cold-Start"], "name": "[2017][DropoutNet] DropoutNet - Addressing Cold Start in Recommender Systems", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/[2017][DropoutNet] DropoutNet - Addressing Cold Start in Recommender Systems.pdf", "year": 2017, "id": 41}, {"tag": ["Cold-Start"], "name": "[2017][HIN] Heterogeneous Information Network Embedding for Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/[2017][HIN] Heterogeneous Information Network Embedding for Recommendation.pdf", "year": 2017, "id": 42}, {"tag": ["Cold-Start", "MAML"], "name": "[2017][MAML]Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/[2017][MAML]Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.pdf", "year": 2017, "id": 43}, {"tag": ["Cold-Start", "ICAN"], "name": "[2020][Wechat][ICAN] Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation", "category": "Cold-Start", "authors": [], "company": "Wechat", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/[2020][Wechat][ICAN] Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation.pdf", "year": 2020, "id": 44}, {"tag": ["Cold-Start", "POSO"], "name": "[2021][Kuaishou][POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems", "category": "Cold-Start", "authors": [], "company": "Kuaishou", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/[2021][Kuaishou][POSO] POSO - Personalized Cold Start Modules for Large-scale Recommender Systems.pdf", "year": 2021, "id": 45}, {"tag": ["ContrastiveLearning"], "name": "A Simple Framework for Contrastive Learning of Visual Representations", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/A Simple Framework for Contrastive Learning of Visual Representations.pdf", "year": 1900, "id": 46}, {"tag": ["ContrastiveLearning"], "name": "An Empirical Study of Training Self-Supervised Vision Transformers", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/An Empirical Study of Training Self-Supervised Vision Transformers.pdf", "year": 1900, "id": 47}, {"tag": ["ContrastiveLearning"], "name": "Bootstrap Your Own Latent A New Approach to Self-Supervised Learning", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Bootstrap Your Own Latent A New Approach to Self-Supervised Learning.pdf", "year": 1900, "id": 48}, {"tag": ["ContrastiveLearning"], "name": "CCL4Rec - Contrast over Contrastive Learning for Micro-video Recommendation", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/CCL4Rec - Contrast over Contrastive Learning for Micro-video Recommendation.pdf", "year": 1900, "id": 49}, {"tag": ["ContrastiveLearning"], "name": "Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems.pdf", "year": 1900, "id": 50}, {"tag": ["ContrastiveLearning"], "name": "Contrastive Learning for Interactive Recommendation in Fashion", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Contrastive Learning for Interactive Recommendation in Fashion.pdf", "year": 1900, "id": 51}, {"tag": ["ContrastiveLearning"], "name": "Disentangled Contrastive Learning for Social Recommendation", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Disentangled Contrastive Learning for Social Recommendation.pdf", "year": 1900, "id": 52}, {"tag": ["ContrastiveLearning"], "name": "Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning.pdf", "year": 1900, "id": 53}, {"tag": ["ContrastiveLearning"], "name": "Improved Baselines with Momentum Contrastive Learning", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Improved Baselines with Momentum Contrastive Learning.pdf", "year": 1900, "id": 54}, {"tag": ["ContrastiveLearning"], "name": "Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning.pdf", "year": 1900, "id": 55}, {"tag": ["ContrastiveLearning"], "name": "Momentum Contrast for Unsupervised Visual Representation Learning", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Momentum Contrast for Unsupervised Visual Representation Learning.pdf", "year": 1900, "id": 56}, {"tag": ["ContrastiveLearning"], "name": "Multi-level Contrastive Learning Framework for Sequential Recommendation", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Multi-level Contrastive Learning Framework for Sequential Recommendation.pdf", "year": 1900, "id": 57}, {"tag": ["ContrastiveLearning"], "name": "Multi-view Multi-behavior Contrastive Learning in Recommendation", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Multi-view Multi-behavior Contrastive Learning in Recommendation.pdf", "year": 1900, "id": 58}, {"tag": ["ContrastiveLearning"], "name": "Predictive and Contrastive- Dual-Auxiliary Learning for Recommendation", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Predictive and Contrastive- Dual-Auxiliary Learning for Recommendation.pdf", "year": 1900, "id": 59}, {"tag": ["ContrastiveLearning"], "name": "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere.pdf", "year": 1900, "id": 60}, {"tag": ["ContrastiveLearning"], "name": "Understanding the Behaviour of Contrastive Loss", "category": "ContrastiveLearning", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/ContrastiveLearning/Understanding the Behaviour of Contrastive Loss.pdf", "year": 1900, "id": 61}, {"tag": ["Debias"], "name": "AutoDebias - Learning to Debias for Recommendation", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/AutoDebias - Learning to Debias for Recommendation.pdf", "year": 1900, "id": 62}, {"tag": ["Debias"], "name": "Bias and Debias in Recommender System A Survey and Future Directions", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Bias and Debias in Recommender System A Survey and Future Directions.pdf", "year": 1900, "id": 63}, {"tag": ["Debias"], "name": "Deep Position-wise Interaction Network for CTR Prediction", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Deep Position-wise Interaction Network for CTR Prediction.pdf", "year": 1900, "id": 64}, {"tag": ["Debias"], "name": "Denoising Implicit Feedback for Recommendation", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Denoising Implicit Feedback for Recommendation.pdf", "year": 1900, "id": 65}, {"tag": ["Debias"], "name": "DVR - Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/DVR - Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias.pdf", "year": 1900, "id": 66}, {"tag": ["Debias"], "name": "Improving Micro-video Recommendation by Controlling Position Bias", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Improving Micro-video Recommendation by Controlling Position Bias.pdf", "year": 1900, "id": 67}, {"tag": ["Debias"], "name": "Learning to rank with selection bias in personal search", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Learning to rank with selection bias in personal search.pdf", "year": 1900, "id": 68}, {"tag": ["Debias"], "name": "Unbiased Learning-to-Rank with Biased Feedback", "category": "Debias", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/Unbiased Learning-to-Rank with Biased Feedback.pdf", "year": 1900, "id": 69}, {"tag": ["Debias", "PAL"], "name": "[2019][Huawei][PAL] a position-bias aware learning framework for CTR prediction in live recommender systems", "category": "Debias", "authors": ["Huifeng Guo", "Ruiming Tang"], "company": "Huawei", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Debias/[2019][Huawei][PAL] a position-bias aware learning framework for CTR prediction in live recommender systems.pdf", "year": 2019, "id": 70}, {"tag": ["Distillation"], "name": "Ensembled CTR Prediction via Knowledge Distillation", "category": "Distillation", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Distillation/Ensembled CTR Prediction via Knowledge Distillation.pdf", "year": 1900, "id": 71}, {"tag": ["Distillation"], "name": "Privileged Features Distillation at Taobao Recommendations", "category": "Distillation", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Distillation/Privileged Features Distillation at Taobao Recommendations.pdf", "year": 1900, "id": 72}, {"tag": ["Distillation"], "name": "Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System", "category": "Distillation", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Distillation/Ranking Distillation - Learning Compact Ranking Models With High Performance for Recommender System.pdf", "year": 1900, "id": 73}, {"tag": ["Distillation"], "name": "Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net", "category": "Distillation", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Distillation/Rocket Launching - A Universal and Efficient Framework for Training Well-performing Light Net.pdf", "year": 1900, "id": 74}, {"tag": ["Distillation", "DMTL"], "name": "[2021][Tencent][DMTL] Distillation based Multi-task Learning - A Candidate Generation Model for Improving Reading Duration", "category": "Distillation", "authors": [], "company": "Tencent", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Distillation/[2021][Tencent][DMTL] Distillation based Multi-task Learning - A Candidate Generation Model for Improving Reading Duration.pdf", "year": 2021, "id": 75}, {"tag": ["Diversity"], "name": "A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks.pdf", "year": 1900, "id": 76}, {"tag": ["Diversity"], "name": "Adaptive, Personalized Diversity for Visual Discovery", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Adaptive, Personalized Diversity for Visual Discovery.pdf", "year": 1900, "id": 77}, {"tag": ["Diversity"], "name": "DGCN - Diversified Recommendation with Graph Convolutional Networks", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/DGCN - Diversified Recommendation with Graph Convolutional Networks.pdf", "year": 1900, "id": 78}, {"tag": ["Diversity"], "name": "Diversifying Search Results", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Diversifying Search Results.pdf", "year": 1900, "id": 79}, {"tag": ["Diversity"], "name": "Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Diversity on the Go! Streaming Determinantal Point Processes under a Maximum Induced Cardinality Objective.pdf", "year": 1900, "id": 80}, {"tag": ["Diversity"], "name": "Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation.pdf", "year": 1900, "id": 81}, {"tag": ["Diversity"], "name": "Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs.pdf", "year": 1900, "id": 82}, {"tag": ["Diversity"], "name": "Exploiting Query Reformulations for Web Search Result Diversification", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Exploiting Query Reformulations for Web Search Result Diversification.pdf", "year": 1900, "id": 83}, {"tag": ["Diversity"], "name": "Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation.pdf", "year": 1900, "id": 84}, {"tag": ["Diversity"], "name": "Future-Aware Diverse Trends Framework for Recommendation", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Future-Aware Diverse Trends Framework for Recommendation.pdf", "year": 1900, "id": 85}, {"tag": ["Diversity"], "name": "Improving Recommendation Lists Through Topic Diversification", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Improving Recommendation Lists Through Topic Diversification.pdf", "year": 1900, "id": 86}, {"tag": ["Diversity"], "name": "Managing Diversity in Airbnb Search", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Managing Diversity in Airbnb Search.pdf", "year": 1900, "id": 87}, {"tag": ["Diversity"], "name": "Novelty and Diversity in Information Retrieval Evaluation", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/Novelty and Diversity in Information Retrieval Evaluation.pdf", "year": 1900, "id": 88}, {"tag": ["Diversity"], "name": "P-Companion - A Principled Framework for Diversified Complementary Product Recommendation", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/P-Companion - A Principled Framework for Diversified Complementary Product Recommendation.pdf", "year": 1900, "id": 89}, {"tag": ["Diversity"], "name": "UNDERSTANDING DIVERSITY IN SESSION-BASED RECOMMENDATION", "category": "Diversity", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/UNDERSTANDING DIVERSITY IN SESSION-BASED RECOMMENDATION.pdf", "year": 1900, "id": 90}, {"tag": ["Diversity", "pDPP"], "name": "[2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems", "category": "Diversity", "authors": [], "company": "Huawei", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Diversity/[2020][Huawei][pDPP] Personalized Re-ranking for Improving Diversity in Live Recommender Systems.pdf", "year": 2020, "id": 91}, {"tag": ["Fairness", "FairCo"], "name": "[2020][FairCo] Controlling Fairness and Bias in Dynamic Learning-to-Rank", "category": "Fairness", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Fairness/[2020][FairCo] Controlling Fairness and Bias in Dynamic Learning-to-Rank.pdf", "year": 2020, "id": 92}, {"tag": ["Feedback-Delay"], "name": "A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback.pdf", "year": 1900, "id": 93}, {"tag": ["Feedback-Delay"], "name": "A Nonparametric Delayed Feedback Model for Conversion Rate Prediction", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/A Nonparametric Delayed Feedback Model for Conversion Rate Prediction.pdf", "year": 1900, "id": 94}, {"tag": ["Feedback-Delay"], "name": "Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction.pdf", "year": 1900, "id": 95}, {"tag": ["Feedback-Delay"], "name": "An Attention-based Model for CVR with Delayed Feedback via Post-Click Calibration", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/An Attention-based Model for CVR with Delayed Feedback via Post-Click Calibration.pdf", "year": 1900, "id": 96}, {"tag": ["Feedback-Delay"], "name": "Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction.pdf", "year": 1900, "id": 97}, {"tag": ["Feedback-Delay"], "name": "Capturing Delayed Feedback in Conversion Rate Predictionvia Elapsed-Time Sampling", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Capturing Delayed Feedback in Conversion Rate Predictionvia Elapsed-Time Sampling.pdf", "year": 1900, "id": 98}, {"tag": ["Feedback-Delay"], "name": "Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback.pdf", "year": 1900, "id": 99}, {"tag": ["Feedback-Delay"], "name": "Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Delayed Feedback Model with Negative Binomial Regression for Multiple Conversions.pdf", "year": 1900, "id": 100}, {"tag": ["Feedback-Delay"], "name": "Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction.pdf", "year": 1900, "id": 101}, {"tag": ["Feedback-Delay"], "name": "Dual Learning Algorithm for Delayed Conversions", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Dual Learning Algorithm for Delayed Conversions.pdf", "year": 1900, "id": 102}, {"tag": ["Feedback-Delay"], "name": "Handling many conversions per click in modeling delayed feedback", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Handling many conversions per click in modeling delayed feedback.pdf", "year": 1900, "id": 103}, {"tag": ["Feedback-Delay"], "name": "Modeling Delayed Feedback in Display Advertising", "category": "Feedback-Delay", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/Modeling Delayed Feedback in Display Advertising.pdf", "year": 1900, "id": 104}, {"tag": ["Feedback-Delay"], "name": "[2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling", "category": "Feedback-Delay", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Feedback-Delay/[2021][Alibaba] Real Negatives Matter - Continuous Training with Real Negatives for Delayed Feedback Modeling.pdf", "year": 2021, "id": 105}, {"tag": ["Industry"], "name": "Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction.pdf", "year": 1900, "id": 106}, {"tag": ["Industry"], "name": "Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint.pdf", "year": 1900, "id": 107}, {"tag": ["Industry"], "name": "CAEN - A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/CAEN - A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment.pdf", "year": 1900, "id": 108}, {"tag": ["Industry"], "name": "CAN - Revisiting Feature Co-Action for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/CAN - Revisiting Feature Co-Action for Click-Through Rate Prediction.pdf", "year": 1900, "id": 109}, {"tag": ["Industry"], "name": "Category-Specific CNN for Visual-aware CTR Prediction at JD.com", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Category-Specific CNN for Visual-aware CTR Prediction at JD.com.pdf", "year": 1900, "id": 110}, {"tag": ["Industry"], "name": "ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/ContextNet - A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding.pdf", "year": 1900, "id": 111}, {"tag": ["Industry"], "name": "Curriculum Disentangled Recommendation with Noisy Multi-feedback", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Curriculum Disentangled Recommendation with Noisy Multi-feedback.pdf", "year": 1900, "id": 112}, {"tag": ["Industry"], "name": "Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation.pdf", "year": 1900, "id": 113}, {"tag": ["Industry"], "name": "Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction.pdf", "year": 1900, "id": 114}, {"tag": ["Industry"], "name": "Deep Learning Recommendation Model for Personalization and Recommendation System", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Deep Learning Recommendation Model for Personalization and Recommendation System.pdf", "year": 1900, "id": 115}, {"tag": ["Industry"], "name": "Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction.pdf", "year": 1900, "id": 116}, {"tag": ["Industry"], "name": "Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback.pdf", "year": 1900, "id": 117}, {"tag": ["Industry"], "name": "Denoising User-aware Memory Network for Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Denoising User-aware Memory Network for Recommendation.pdf", "year": 1900, "id": 118}, {"tag": ["Industry"], "name": "Dual Graph enhanced Embedding Neural Network for CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Dual Graph enhanced Embedding Neural Network for CTR Prediction.pdf", "year": 1900, "id": 119}, {"tag": ["Industry"], "name": "End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model.pdf", "year": 1900, "id": 120}, {"tag": ["Industry"], "name": "EXTR - Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/EXTR - Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search.pdf", "year": 1900, "id": 121}, {"tag": ["Industry"], "name": "FeedRec - News Feed Recommendation with Various User Feedbacks", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeedRec - News Feed Recommendation with Various User Feedbacks.pdf", "year": 1900, "id": 122}, {"tag": ["Industry"], "name": "Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Fi-GNN - Modeling Feature Interactions via Graph Neural Networks for CTR Prediction.pdf", "year": 1900, "id": 123}, {"tag": ["Industry"], "name": "FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FiBiNet++ - Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction.pdf", "year": 1900, "id": 124}, {"tag": ["Industry"], "name": "FLEN - Leveraging Field for Scalable CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FLEN - Leveraging Field for Scalable CTR Prediction.pdf", "year": 1900, "id": 125}, {"tag": ["Industry"], "name": "FM2 - Field-matrixed Factorization Machines for Recommender Systems", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FM2 - Field-matrixed Factorization Machines for Recommender Systems.pdf", "year": 1900, "id": 126}, {"tag": ["Industry"], "name": "GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/GateNet - Gating-Enhanced Deep Network for Click-Through Rate Prediction.pdf", "year": 1900, "id": 127}, {"tag": ["Industry"], "name": "HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/HIEN - Hierarchical Intention Embedding Network for Click-Through Rate Prediction.pdf", "year": 1900, "id": 128}, {"tag": ["Industry"], "name": "Hybrid Interest Modeling for Long-tailed Users", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Hybrid Interest Modeling for Long-tailed Users.pdf", "year": 1900, "id": 129}, {"tag": ["Industry"], "name": "Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads.pdf", "year": 1900, "id": 130}, {"tag": ["Industry"], "name": "Improving Deep Learning For Airbnb Search", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Improving Deep Learning For Airbnb Search.pdf", "year": 1900, "id": 131}, {"tag": ["Industry"], "name": "Improving Recommendation Quality in Google Drive", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Improving Recommendation Quality in Google Drive.pdf", "year": 1900, "id": 132}, {"tag": ["Industry"], "name": "Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction.pdf", "year": 1900, "id": 133}, {"tag": ["Industry"], "name": "Long Short-Term Temporal Meta-learning in Online Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Long Short-Term Temporal Meta-learning in Online Recommendation.pdf", "year": 1900, "id": 134}, {"tag": ["Industry"], "name": "MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/MaskNet - Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask.pdf", "year": 1900, "id": 135}, {"tag": ["Industry"], "name": "Modeling Users\u2019 Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Modeling Users\u2019 Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search.pdf", "year": 1900, "id": 136}, {"tag": ["Industry"], "name": "MRIF - Multi-resolution Interest Fusion for Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/MRIF - Multi-resolution Interest Fusion for Recommendation.pdf", "year": 1900, "id": 137}, {"tag": ["Industry"], "name": "Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction.pdf", "year": 1900, "id": 138}, {"tag": ["Industry"], "name": "News Recommendation with Candidate-aware User Modeling", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/News Recommendation with Candidate-aware User Modeling.pdf", "year": 1900, "id": 139}, {"tag": ["Industry"], "name": "Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data.pdf", "year": 1900, "id": 140}, {"tag": ["Industry"], "name": "Recommender Transformers with Behavior Pathways", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Recommender Transformers with Behavior Pathways.pdf", "year": 1900, "id": 141}, {"tag": ["Industry"], "name": "Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling.pdf", "year": 1900, "id": 142}, {"tag": ["Industry"], "name": "Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction.pdf", "year": 1900, "id": 143}, {"tag": ["Industry"], "name": "Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce.pdf", "year": 1900, "id": 144}, {"tag": ["Industry"], "name": "TencentRec - Real-time Stream Recommendation in Practice", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/TencentRec - Real-time Stream Recommendation in Practice.pdf", "year": 1900, "id": 145}, {"tag": ["Industry"], "name": "TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/TiSSA - A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors.pdf", "year": 1900, "id": 146}, {"tag": ["Industry"], "name": "Triangle Graph Interest Network for Click-through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Triangle Graph Interest Network for Click-through Rate Prediction.pdf", "year": 1900, "id": 147}, {"tag": ["Industry"], "name": "User Behavior Retrieval for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/User Behavior Retrieval for Click-Through Rate Prediction.pdf", "year": 1900, "id": 148}, {"tag": ["Industry"], "name": "[2016][Microsoft] User Fatigue in Online News Recommendation", "category": "Industry", "authors": [], "company": "Microsoft", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2016][Microsoft] User Fatigue in Online News Recommendation.pdf", "year": 2016, "id": 149}, {"tag": ["Industry"], "name": "[2016][Youtube] Deep Neural Networks for YouTube Recommendations", "category": "Industry", "authors": [], "company": "Youtube", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2016][Youtube] Deep Neural Networks for YouTube Recommendations.pdf", "year": 2016, "id": 150}, {"tag": ["Industry"], "name": "[2017][Alibaba][ATRank] ATRank - An Attention-Based User Behavior Modeling Framework for Recommendation", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2017][Alibaba][ATRank] ATRank - An Attention-Based User Behavior Modeling Framework for Recommendation.pdf", "year": 2017, "id": 151}, {"tag": ["Industry"], "name": "[2017][Alibaba][DIN] Deep Interest Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2017][Alibaba][DIN] Deep Interest Network for Click-Through Rate Prediction.pdf", "year": 2017, "id": 152}, {"tag": ["Industry"], "name": "[2018][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb", "category": "Industry", "authors": [], "company": "Airbnb", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2018][Airbnb] Real-time Personalization using Embeddings for Search Ranking at Airbnb.pdf", "year": 2018, "id": 153}, {"tag": ["Industry"], "name": "[2018][Alibaba][DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2018][Alibaba][DIEN] Deep Interest Evolution Network for Click-Through Rate Prediction.pdf", "year": 2018, "id": 154}, {"tag": ["Industry"], "name": "[2018][FwFM] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2018][FwFM] Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising.pdf", "year": 2018, "id": 155}, {"tag": ["Industry"], "name": "[2019][Airbnb] Applying Deep Learning To Airbnb Search", "category": "Industry", "authors": [], "company": "Airbnb", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2019][Airbnb] Applying Deep Learning To Airbnb Search.pdf", "year": 2018, "id": 156}, {"tag": ["Industry"], "name": "[2019][Alibaba][BST] Behavior Sequence Transformer for E-commerceRecommendation in Alibaba", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2019][Alibaba][BST] Behavior Sequence Transformer for E-commerceRecommendation in Alibaba.pdf", "year": 2019, "id": 157}, {"tag": ["Industry"], "name": "[2019][Alibaba][DSIN] Deep Session Interest Network for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2019][Alibaba][DSIN] Deep Session Interest Network for Click-Through Rate Prediction.pdf", "year": 2019, "id": 158}, {"tag": ["Industry"], "name": "[2019][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2019][Alibaba][MIMN] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction.pdf", "year": 2019, "id": 159}, {"tag": ["Industry"], "name": "[2019][Weibo][FiBiNET] FiBiNET - Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Weibo", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2019][Weibo][FiBiNET] FiBiNET - Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction.pdf", "year": 2019, "id": 160}, {"tag": ["Industry"], "name": "[2020][Alibaba][DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2020][Alibaba][DMR] Deep Match to Rank Model for Personalized Click-Through Rate Prediction.pdf", "year": 2020, "id": 161}, {"tag": ["Industry"], "name": "[2020][Alibaba][ESAM] ESAM - Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2020][Alibaba][ESAM] ESAM - Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance.pdf", "year": 2020, "id": 162}, {"tag": ["Industry"], "name": "[2020][Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2020][Alibaba][SIM] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction.pdf", "year": 2020, "id": 163}, {"tag": ["Industry"], "name": "[2021][Alibaba][DINMP] A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction", "category": "Industry", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2021][Alibaba][DINMP] A Non-sequential Approach to Deep User Interest Model for Click-Through Rate Prediction.pdf", "year": 2021, "id": 164}, {"tag": ["Industry"], "name": "[2021][Fliggy] [DMSN] Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning", "category": "Industry", "authors": [], "company": "Fliggy", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2021][Fliggy] [DMSN] Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning.pdf", "year": 2021, "id": 165}, {"tag": ["Industry"], "name": "[2021][Google] Bootstrapping Recommendations at Chrome Web Store", "category": "Industry", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2021][Google] Bootstrapping Recommendations at Chrome Web Store.pdf", "year": 2021, "id": 166}, {"tag": ["Industry", "AutoDis"], "name": "[2021][Huawei][AutoDis] An Embedding Learning Framework for Numerical Features in CTR Prediction", "category": "Industry", "authors": [], "company": "Huawei", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/[2021][Huawei][AutoDis] An Embedding Learning Framework for Numerical Features in CTR Prediction.pdf", "year": 2021, "id": 167}, {"tag": ["Look-Alike"], "name": "A Sub-linear, Massive-scale Look-alike Audience Extension System", "category": "Look-Alike", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Look-Alike/A Sub-linear, Massive-scale Look-alike Audience Extension System.pdf", "year": 2021, "id": 168}, {"tag": ["Look-Alike"], "name": "Audience Expansion for Online Social Network Advertising", "category": "Look-Alike", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Look-Alike/Audience Expansion for Online Social Network Advertising.pdf", "year": 1900, "id": 169}, {"tag": ["Look-Alike"], "name": "Effective Audience Extension in Online Advertising", "category": "Look-Alike", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Look-Alike/Effective Audience Extension in Online Advertising.pdf", "year": 1900, "id": 170}, {"tag": ["Look-Alike"], "name": "Finding Users Who Act Alike - Transfer Learning for Expanding", "category": "Look-Alike", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Look-Alike/Finding Users Who Act Alike - Transfer Learning for Expanding.pdf", "year": 1900, "id": 171}, {"tag": ["Look-Alike", "RALM"], "name": "[2019][Tencent][RALM] Real-time Attention Based Look-alike Model for Recommender System", "category": "Look-Alike", "authors": [], "company": "Tencent", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Look-Alike/[2019][Tencent][RALM] Real-time Attention Based Look-alike Model for Recommender System.pdf", "year": 2019, "id": 172}, {"tag": ["Match"], "name": "A Dual Augmented Two-tower Model for Online Large-scale Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/A Dual Augmented Two-tower Model for Online Large-scale Recommendation.pdf", "year": 1900, "id": 173}, {"tag": ["Match"], "name": "A User-Centered Concept Mining System for Query and Document Understanding at Tencent", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/A User-Centered Concept Mining System for Query and Document Understanding at Tencent.pdf", "year": 1900, "id": 174}, {"tag": ["Match"], "name": "CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/CROLoss - Towards a Customizable Loss for Retrieval Models in Recommender Systems.pdf", "year": 1900, "id": 175}, {"tag": ["Match"], "name": "Cross-Batch Negative Sampling for Training Two-Tower Recommenders", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Cross-Batch Negative Sampling for Training Two-Tower Recommenders.pdf", "year": 1900, "id": 176}, {"tag": ["Match"], "name": "Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Deep Retrieval - Learning A Retrievable Structure for Large-Scale Recommendations.pdf", "year": 1900, "id": 177}, {"tag": ["Match"], "name": "Disentangled Self-Supervision in Sequential Recommenders", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Disentangled Self-Supervision in Sequential Recommenders.pdf", "year": 1900, "id": 178}, {"tag": ["Match"], "name": "Efficient Training on Very Large Corpora via Gramian Estimation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Efficient Training on Very Large Corpora via Gramian Estimation.pdf", "year": 1900, "id": 179}, {"tag": ["Match"], "name": "Extreme Multi-label Learning for Semantic Matching in Product Search", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Extreme Multi-label Learning for Semantic Matching in Product Search.pdf", "year": 1900, "id": 180}, {"tag": ["Match"], "name": "Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Factorization Meets the Neighborhood - a Multifaceted Collaborative Filtering Model.pdf", "year": 1900, "id": 181}, {"tag": ["Match"], "name": "Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching.pdf", "year": 1900, "id": 182}, {"tag": ["Match"], "name": "Itinerary-aware Personalized Deep Matching at Fliggy", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Itinerary-aware Personalized Deep Matching at Fliggy.pdf", "year": 1900, "id": 183}, {"tag": ["Match"], "name": "Joint Optimization of Tree-based Index and Deep Model for Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Joint Optimization of Tree-based Index and Deep Model for Recommender Systems.pdf", "year": 1900, "id": 184}, {"tag": ["Match"], "name": "Learning Deep Structured Semantic Models for Web Search using Clickthrough Data", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Learning Deep Structured Semantic Models for Web Search using Clickthrough Data.pdf", "year": 1900, "id": 185}, {"tag": ["Match"], "name": "Learning Tree-based Deep Model for Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Learning Tree-based Deep Model for Recommender Systems.pdf", "year": 1900, "id": 186}, {"tag": ["Match"], "name": "Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Octopus - Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates.pdf", "year": 1900, "id": 187}, {"tag": ["Match"], "name": "Path-based Deep Network for Candidate Item Matching in Recommenders", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Path-based Deep Network for Candidate Item Matching in Recommenders.pdf", "year": 1900, "id": 188}, {"tag": ["Match"], "name": "Self-Attentive Sequential Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Self-Attentive Sequential Recommendation.pdf", "year": 1900, "id": 189}, {"tag": ["Match"], "name": "Sparse-Interest Network for Sequential Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Sparse-Interest Network for Sequential Recommendation.pdf", "year": 1900, "id": 190}, {"tag": ["Match"], "name": "Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Towards Personalized and Semantic Retrieval - An End-to-End Solution for E-commerce Search via Embedding Learning.pdf", "year": 1900, "id": 191}, {"tag": ["Match"], "name": "Uni-Retriever - Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Uni-Retriever - Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search.pdf", "year": 1900, "id": 192}, {"tag": ["Match"], "name": "XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/XDM - Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System.pdf", "year": 1900, "id": 193}, {"tag": ["Match"], "name": "[2015][Microsoft][DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems", "category": "Match", "authors": [], "company": "Microsoft", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2015][Microsoft][DSSM in Recsys] A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems.pdf", "year": 2015, "id": 194}, {"tag": ["Match"], "name": "[2016][Yahoo][App2Vec] App2Vec - Vector Modeling of Mobile Apps and Applications", "category": "Match", "authors": [], "company": "Yahoo", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2016][Yahoo][App2Vec] App2Vec - Vector Modeling of Mobile Apps and Applications.pdf", "year": 2016, "id": 195}, {"tag": ["Match"], "name": "[2018][TC-CML] Loss Aversion in Recommender Systems - Utilizing Negative User Preference to Improve Recommendation Quality", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2018][TC-CML] Loss Aversion in Recommender Systems - Utilizing Negative User Preference to Improve Recommendation Quality.pdf", "year": 2018, "id": 196}, {"tag": ["Match"], "name": "[2019][Alibaba][SDM] SDM - Sequential Deep Matching Model for Online Large-scale Recommender System", "category": "Match", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2019][Alibaba][SDM] SDM - Sequential Deep Matching Model for Online Large-scale Recommender System.pdf", "year": 2019, "id": 197}, {"tag": ["Match"], "name": "[2019][Baidu][MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu\u2019s Sponsored Search", "category": "Match", "authors": [], "company": "Baidu", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2019][Baidu][MOBIUS] MOBIUS - Towards the Next Generation of Query-Ad Matching in Baidu\u2019s Sponsored Search.pdf", "year": 2019, "id": 198}, {"tag": ["Match"], "name": "[2019][Google] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations", "category": "Match", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2019][Google] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations.pdf", "year": 2019, "id": 199}, {"tag": ["Match"], "name": "[2020][Alibaba][Swing&Surprise] Large Scale Product Graph Construction for Recommendation in E-commerce", "category": "Match", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2020][Alibaba][Swing&Surprise] Large Scale Product Graph Construction for Recommendation in E-commerce.pdf", "year": 2020, "id": 200}, {"tag": ["Match"], "name": "[2020][Facebook][EBR] Embedding-based Retrieval in Facebook Search", "category": "Match", "authors": [], "company": "Facebook", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2020][Facebook][EBR] Embedding-based Retrieval in Facebook Search.pdf", "year": 2020, "id": 201}, {"tag": ["Match"], "name": "[2020][Google][MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations", "category": "Match", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2020][Google][MNS] Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations.pdf", "year": 2020, "id": 202}, {"tag": ["Match"], "name": "[2020][Weixin][UTPM] Learning to Build User-tag Profile in Recommendation System", "category": "Match", "authors": [], "company": "Weixin", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2020][Weixin][UTPM] Learning to Build User-tag Profile in Recommendation System.pdf", "year": 2020, "id": 203}, {"tag": ["Match"], "name": "[2021][Alibaba][MGDSPR] Embedding-based Product Retrieval in Taobao Search", "category": "Match", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2021][Alibaba][MGDSPR] Embedding-based Product Retrieval in Taobao Search.pdf", "year": 2021, "id": 204}, {"tag": ["Match"], "name": "[2021][Google] Self-supervised Learning for Large-scale Item Recommendations", "category": "Match", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/[2021][Google] Self-supervised Learning for Large-scale Item Recommendations.pdf", "year": 2021, "id": 205}, {"tag": ["Multi-Modal"], "name": "Adversarial Multimodal Representation Learning for Click-Through Rate Prediction", "category": "Multi-Modal", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Modal/Adversarial Multimodal Representation Learning for Click-Through Rate Prediction.pdf", "year": 1900, "id": 206}, {"tag": ["Multi-Modal"], "name": "Pretraining Representations of Multi-modal Multi-query E-commerce Search", "category": "Multi-Modal", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Modal/Pretraining Representations of Multi-modal Multi-query E-commerce Search.pdf", "year": 1900, "id": 207}, {"tag": ["Multi-Scenario"], "name": "A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/A Survey on Cross-domain Recommendation - Taxonomies, Methods, and Future Directions.pdf", "year": 1900, "id": 208}, {"tag": ["Multi-Scenario"], "name": "AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/AdaSparse - Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction.pdf", "year": 1900, "id": 209}, {"tag": ["Multi-Scenario"], "name": "APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/APG - Adaptive Parameter Generation Network for Click-Through Rate Prediction.pdf", "year": 1900, "id": 210}, {"tag": ["Multi-Scenario"], "name": "Automatic Expert Selection for Multi-Scenario and Multi-Task Search", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Automatic Expert Selection for Multi-Scenario and Multi-Task Search.pdf", "year": 1900, "id": 211}, {"tag": ["Multi-Scenario"], "name": "Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao.pdf", "year": 1900, "id": 212}, {"tag": ["Multi-Scenario"], "name": "Cross-Domain Recommendation - An Embedding and Mapping Approach", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Cross-Domain Recommendation - An Embedding and Mapping Approach.pdf", "year": 1900, "id": 213}, {"tag": ["Multi-Scenario"], "name": "Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation.pdf", "year": 1900, "id": 214}, {"tag": ["Multi-Scenario"], "name": "Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space.pdf", "year": 1900, "id": 215}, {"tag": ["Multi-Scenario"], "name": "KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/KEEP - An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging.pdf", "year": 1900, "id": 216}, {"tag": ["Multi-Scenario"], "name": "Leaving No One Behind- A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Leaving No One Behind- A Multi-Scenario Multi-Task Meta Learning Approach for Advertiser Modeling.pdf", "year": 1900, "id": 217}, {"tag": ["Multi-Scenario"], "name": "Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services.pdf", "year": 1900, "id": 218}, {"tag": ["Multi-Scenario"], "name": "Personalized Transfer of User Preferences for Cross-domain Recommendation", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Personalized Transfer of User Preferences for Cross-domain Recommendation.pdf", "year": 1900, "id": 219}, {"tag": ["Multi-Scenario"], "name": "Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation.pdf", "year": 1900, "id": 220}, {"tag": ["Multi-Scenario"], "name": "Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce.pdf", "year": 1900, "id": 221}, {"tag": ["Multi-Scenario"], "name": "Self-Supervised Learning on Users\u2019 Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce", "category": "Multi-Scenario", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/Self-Supervised Learning on Users\u2019 Spontaneous Behaviors for Multi-Scenario Ranking in E-commerce.pdf", "year": 1900, "id": 222}, {"tag": ["Multi-Scenario"], "name": "[2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network", "category": "Multi-Scenario", "authors": [], "company": "JD", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/[2020][JD][DADNN] DADNN - Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network.pdf", "year": 1900, "id": 223}, {"tag": ["Multi-Scenario"], "name": "[2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction", "category": "Multi-Scenario", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Scenario/[2021][Alibaba][STAR] One Model to Serve All - Star Topology Adaptive Recommenderfor Multi-Domain CTR Prediction.pdf", "year": 1900, "id": 224}, {"tag": ["Multi-Task"], "name": "Can Small Heads Help Understanding and Improving Multi-Task Generalization", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Can Small Heads Help Understanding and Improving Multi-Task Generalization.pdf", "year": 1900, "id": 225}, {"tag": ["Multi-Task"], "name": "DSelect-k - Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/DSelect-k - Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning.pdf", "year": 1900, "id": 226}, {"tag": ["Multi-Task"], "name": "Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction.pdf", "year": 1900, "id": 227}, {"tag": ["Multi-Task"], "name": "GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/GemNN - Gating-Enhanced Multi-Task Neural Networks with Feature Interaction Learning for CTR Prediction.pdf", "year": 1900, "id": 228}, {"tag": ["Multi-Task"], "name": "Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction.pdf", "year": 1900, "id": 229}, {"tag": ["Multi-Task"], "name": "HyperGrid Transformers - Towards A Single Model for Multiple Tasks", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/HyperGrid Transformers - Towards A Single Model for Multiple Tasks.pdf", "year": 1900, "id": 230}, {"tag": ["Multi-Task"], "name": "MetaBalance - Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/MetaBalance - Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks.pdf", "year": 1900, "id": 231}, {"tag": ["Multi-Task"], "name": "Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling.pdf", "year": 1900, "id": 232}, {"tag": ["Multi-Task"], "name": "MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/MSSM - A Multiple-level Sparse Sharing Model for Efficient Multi-Task Learning.pdf", "year": 1900, "id": 233}, {"tag": ["Multi-Task"], "name": "Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation.pdf", "year": 1900, "id": 234}, {"tag": ["Multi-Task"], "name": "Multi-Task Learning as Multi-Objective Optimization - slide", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Multi-Task Learning as Multi-Objective Optimization - slide.pdf", "year": 1900, "id": 235}, {"tag": ["Multi-Task"], "name": "Multi-Task Learning for Dense Prediction Tasks - A Survey", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Multi-Task Learning for Dense Prediction Tasks - A Survey.pdf", "year": 1900, "id": 236}, {"tag": ["Multi-Task"], "name": "Pareto Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Pareto Multi-Task Learning.pdf", "year": 1900, "id": 237}, {"tag": ["Multi-Task"], "name": "Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Perceive Your Users in Depth - Learning Universal User Representations from Multiple E-commerce Tasks.pdf", "year": 1900, "id": 238}, {"tag": ["Multi-Task"], "name": "Personalized Approximate Pareto-Efficient Recommendation", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Personalized Approximate Pareto-Efficient Recommendation.pdf", "year": 1900, "id": 239}, {"tag": ["Multi-Task"], "name": "SNR - Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/SNR - Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning.pdf", "year": 1900, "id": 240}, {"tag": ["Multi-Task"], "name": "Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.pdf", "year": 1900, "id": 241}, {"tag": ["Multi-Task"], "name": "Why I like it - multi-task learning for recommendation and explanation", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/Why I like it - multi-task learning for recommendation and explanation.pdf", "year": 1900, "id": 242}, {"tag": ["Multi-Task", "MGDA"], "name": "[2012][MGDA] Multiple-gradient descent algorithm (MGDA) for multiobjective optimization", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2012][MGDA] Multiple-gradient descent algorithm (MGDA) for multiobjective optimization.pdf", "year": 2012, "id": 243}, {"tag": ["Multi-Task", "ESMM"], "name": "[2018][Alibaba][ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate", "category": "Multi-Task", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2018][Alibaba][ESMM] Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate.pdf", "year": 2018, "id": 244}, {"tag": ["Multi-Task"], "name": "[2018][Cambridge] Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics", "category": "Multi-Task", "authors": [], "company": "Cambridge", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2018][Cambridge] Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.pdf", "year": 2018, "id": 245}, {"tag": ["Multi-Task", "MMOE"], "name": "[2018][Google][MMOE] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts", "category": "Multi-Task", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2018][Google][MMOE] Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.pdf", "year": 2018, "id": 246}, {"tag": ["Multi-Task", "GradNorm"], "name": "[2018][MagicLeap][GradNorm] GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks", "category": "Multi-Task", "authors": [], "company": "MagicLeap", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2018][MagicLeap][GradNorm] GradNorm - Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks.pdf", "year": 2018, "id": 247}, {"tag": ["Multi-Task"], "name": "[2019][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation", "category": "Multi-Task", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2019][Alibaba] A Pareto-Efficient Algorithm for Multiple Objective Optimization in E-Commerce Recommendation.pdf", "year": 2019, "id": 248}, {"tag": ["Multi-Task"], "name": "[2019][Alibaba][DBMTL] Deep Bayesian Multi-Target Learning for Recommender Systems", "category": "Multi-Task", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2019][Alibaba][DBMTL] Deep Bayesian Multi-Target Learning for Recommender Systems.pdf", "year": 2019, "id": 249}, {"tag": ["Multi-Task"], "name": "[2019][Intel] Multi-Task Learning as Multi-Objective Optimization", "category": "Multi-Task", "authors": [], "company": "Intel", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2019][Intel] Multi-Task Learning as Multi-Objective Optimization.pdf", "year": 2019, "id": 250}, {"tag": ["Multi-Task"], "name": "[2019][Youtube] Recommending What Video to Watch Next - A Multitask Ranking System", "category": "Multi-Task", "authors": [], "company": "Youtube", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2019][Youtube] Recommending What Video to Watch Next - A Multitask Ranking System.pdf", "year": 2019, "id": 251}, {"tag": ["Multi-Task"], "name": "[2020][Alibaba][Multi-IPW&Multi-DR] LARGE-SCALE CAUSAL APPROACHES TO DEBIASING POST-CLICK CONVERSION RATE ESTIMATION WITH MULTI-TASK LEARNING", "category": "Multi-Task", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2020][Alibaba][Multi-IPW&Multi-DR] LARGE-SCALE CAUSAL APPROACHES TO DEBIASING POST-CLICK CONVERSION RATE ESTIMATION WITH MULTI-TASK LEARNING.pdf", "year": 2020, "id": 252}, {"tag": ["Multi-Task"], "name": "[2020][Google][MoSE] Multitask Mixture of Sequential Experts for User Activity Streams", "category": "Multi-Task", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2020][Google][MoSE] Multitask Mixture of Sequential Experts for User Activity Streams.pdf", "year": 2020, "id": 253}, {"tag": ["Multi-Task"], "name": "[2020][JD][DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems", "category": "Multi-Task", "authors": [], "company": "JD", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2020][JD][DMT] Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems.pdf", "year": 2020, "id": 254}, {"tag": ["Multi-Task"], "name": "[2020][PCGrad] Gradient Surgery for Multi-Task Learning", "category": "Multi-Task", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2020][PCGrad] Gradient Surgery for Multi-Task Learning.pdf", "year": 2020, "id": 255}, {"tag": ["Multi-Task"], "name": "[2020][Tencent][PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations", "category": "Multi-Task", "authors": [], "company": "Tencent", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2020][Tencent][PLE] Progressive Layered Extraction (PLE) - A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations.pdf", "year": 2020, "id": 256}, {"tag": ["Multi-Task"], "name": "[2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising", "category": "Multi-Task", "authors": [], "company": "Meituan", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2021][Meituan][AITM] Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising.pdf", "year": 2021, "id": 257}, {"tag": ["Multi-Task"], "name": "[2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation", "category": "Multi-Task", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Multi-Task/[2022][Alibaba][ESCM2] ESCM2 - Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation.pdf", "year": 2022, "id": 258}, {"tag": ["Pre-Rank"], "name": "AutoFAS - Automatic Feature and Architecture Selection for Pre-Ranking System", "category": "Pre-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/AutoFAS - Automatic Feature and Architecture Selection for Pre-Ranking System.pdf", "year": 1900, "id": 259}, {"tag": ["Pre-Rank"], "name": "Cascade Ranking for Operational E-commerce Search", "category": "Pre-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/Cascade Ranking for Operational E-commerce Search.pdf", "year": 1900, "id": 260}, {"tag": ["Pre-Rank"], "name": "Contrastive Information Transfer for Pre-Ranking Systems", "category": "Pre-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/Contrastive Information Transfer for Pre-Ranking Systems.pdf", "year": 1900, "id": 261}, {"tag": ["Pre-Rank"], "name": "EENMF - An End-to-End Neural Matching Framework for E-Commerce Sponsored Search", "category": "Pre-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/EENMF - An End-to-End Neural Matching Framework for E-Commerce Sponsored Search.pdf", "year": 1900, "id": 262}, {"tag": ["Pre-Rank"], "name": "[2020][Alibaba][COLD] COLD - Towards the Next Generation of Pre-Ranking System", "category": "Pre-Rank", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/[2020][Alibaba][COLD] COLD - Towards the Next Generation of Pre-Ranking System.pdf", "year": 2020, "id": 263}, {"tag": ["Pre-Rank"], "name": "[2021][Alibaba] Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking - A Learnable Feature Selection based Approach", "category": "Pre-Rank", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/[2021][Alibaba] Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking - A Learnable Feature Selection based Approach.pdf", "year": 2021, "id": 264}, {"tag": ["Pre-Rank"], "name": "[2022] On Ranking Consistency of Pre-ranking Stage", "category": "Pre-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Pre-Rank/[2022] On Ranking Consistency of Pre-ranking Stage.pdf", "year": 2022, "id": 265}, {"tag": ["Rank"], "name": "SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS", "category": "Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS.pdf", "year": 1900, "id": 266}, {"tag": ["Rank"], "name": "[2009][BPR] Bayesian Personalized Ranking from Implicit Feedback", "category": "Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2009][BPR] Bayesian Personalized Ranking from Implicit Feedback.pdf", "year": 2009, "id": 267}, {"tag": ["Rank"], "name": "[2010][FM] Factorization Machines", "category": "Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2010][FM] Factorization Machines.pdf", "year": 2010, "id": 268}, {"tag": ["Rank"], "name": "[2014][Facebook][GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook", "category": "Rank", "authors": [], "company": "Facebook", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2014][Facebook][GBDT+LR] Practical Lessons from Predicting Clicks on Ads at Facebook.pdf", "year": 2014, "id": 269}, {"tag": ["Rank"], "name": "[2016][Google][Wide&Deep] Wide & Deep Learning for Recommender Systems", "category": "Rank", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2016][Google][Wide&Deep] Wide & Deep Learning for Recommender Systems.pdf", "year": 2016, "id": 270}, {"tag": ["Rank"], "name": "[2016][Microsft][Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features", "category": "Rank", "authors": [], "company": "Microsoft", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2016][Microsft][Deep Crossing] Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features.pdf", "year": 2016, "id": 271}, {"tag": ["Rank"], "name": "[2016][NTU][FFM] Field-aware Factorization Machines for CTR Prediction", "category": "Rank", "authors": [], "company": "NTU", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2016][NTU][FFM] Field-aware Factorization Machines for CTR Prediction.pdf", "year": 2016, "id": 272}, {"tag": ["Rank"], "name": "[2016][SJTU][PNN] Product-based Neural Networks for User Response Prediction", "category": "Rank", "authors": [], "company": "SJTU", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2016][SJTU][PNN] Product-based Neural Networks for User Response Prediction.pdf", "year": 2016, "id": 273}, {"tag": ["Rank"], "name": "[2016][UCL][FNN] Deep Learning over Multi-field Categorical Data", "category": "Rank", "authors": [], "company": "UCL", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2016][UCL][FNN] Deep Learning over Multi-field Categorical Data.pdf", "year": 2016, "id": 274}, {"tag": ["Rank"], "name": "[2017][Alibaba][MLR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction", "category": "Rank", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][Alibaba][MLR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction.pdf", "year": 2017, "id": 275}, {"tag": ["Rank"], "name": "[2017][Huawei][DeepFM] A Factorization-Machine based Neural Network for CTR Prediction", "category": "Rank", "authors": [], "company": "Huawei", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][Huawei][DeepFM] A Factorization-Machine based Neural Network for CTR Prediction.pdf", "year": 2017, "id": 276}, {"tag": ["Rank"], "name": "[2017][NUS][NCF] Neural Collaborative Filtering", "category": "Rank", "authors": [], "company": "NUS", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][NUS][NCF] Neural Collaborative Filtering.pdf", "year": 2017, "id": 277}, {"tag": ["Rank"], "name": "[2017][NUS][NFM] Neural Factorization Machines for Sparse Predictive Analytics", "category": "Rank", "authors": [], "company": "NUS", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][NUS][NFM] Neural Factorization Machines for Sparse Predictive Analytics.pdf", "year": 2017, "id": 278}, {"tag": ["Rank"], "name": "[2017][Stanford][DCN] Deep & Cross Network for Ad Click Predictions", "category": "Rank", "authors": [], "company": "Stanford", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][Stanford][DCN] Deep & Cross Network for Ad Click Predictions.pdf", "year": 2017, "id": 279}, {"tag": ["Rank"], "name": "[2017][ZJU][AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks", "category": "Rank", "authors": [], "company": "ZJU", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2017][ZJU][AFM] Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks.pdf", "year": 2017, "id": 280}, {"tag": ["Rank"], "name": "[2018][USTC][xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems", "category": "Rank", "authors": [], "company": "USTC", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2018][USTC][xDeepFM] xDeepFM - Combining Explicit and Implicit Feature Interactions for Recommender Systems.pdf", "year": 2018, "id": 281}, {"tag": ["Rank"], "name": "[2019][AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks", "category": "Rank", "authors": [], "company": "AutoInt", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2019][AutoInt] AutoInt - Automatic Feature Interaction Learning via Self-Attentive Neural Networks.pdf", "year": 2019, "id": 282}, {"tag": ["Rank", "DFN"], "name": "[2020][Tencent][DFN] Deep Feedback Network for Recommendation", "category": "Rank", "authors": [], "company": "Tencent", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Rank/[2020][Tencent][DFN] Deep Feedback Network for Recommendation.pdf", "year": 2020, "id": 283}, {"tag": ["Re-Rank"], "name": "Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems.pdf", "year": 1900, "id": 284}, {"tag": ["Re-Rank"], "name": "Cross DQN - Cross Deep Q Network for Ads Allocation in Feed", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Cross DQN - Cross Deep Q Network for Ads Allocation in Feed.pdf", "year": 1900, "id": 285}, {"tag": ["Re-Rank"], "name": "GenDeR - A Generic Diversified Ranking Algorithm", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/GenDeR - A Generic Diversified Ranking Algorithm.pdf", "year": 1900, "id": 286}, {"tag": ["Re-Rank"], "name": "Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search.pdf", "year": 1900, "id": 287}, {"tag": ["Re-Rank"], "name": "GRN - Generative Rerank Network for Context-wise Recommendation", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/GRN - Generative Rerank Network for Context-wise Recommendation.pdf", "year": 1900, "id": 288}, {"tag": ["Re-Rank"], "name": "Learning a Deep Listwise Context Model for Ranking Refinement", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Learning a Deep Listwise Context Model for Ranking Refinement.pdf", "year": 1900, "id": 289}, {"tag": ["Re-Rank"], "name": "Neural Re-ranking in Multi-stage Recommender Systems - A Review", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Neural Re-ranking in Multi-stage Recommender Systems - A Review.pdf", "year": 1900, "id": 290}, {"tag": ["Re-Rank"], "name": "Personalized Click Shaping through Lagrangian Duality for Online Recommendation", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Personalized Click Shaping through Lagrangian Duality for Online Recommendation.pdf", "year": 1900, "id": 291}, {"tag": ["Re-Rank"], "name": "Personalized Re-ranking for Recommendation", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Personalized Re-ranking for Recommendation.pdf", "year": 1900, "id": 292}, {"tag": ["Re-Rank"], "name": "Personalized Re-ranking with Item Relationships for E-commerce", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Personalized Re-ranking with Item Relationships for E-commerce.pdf", "year": 1900, "id": 293}, {"tag": ["Re-Rank"], "name": "Practical Diversified Recommendations on YouTube with Determinantal Point Processes", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Practical Diversified Recommendations on YouTube with Determinantal Point Processes.pdf", "year": 1900, "id": 294}, {"tag": ["Re-Rank"], "name": "Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System.pdf", "year": 1900, "id": 295}, {"tag": ["Re-Rank"], "name": "Revisit Recommender System in the Permutation Prospective", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Revisit Recommender System in the Permutation Prospective.pdf", "year": 1900, "id": 296}, {"tag": ["Re-Rank"], "name": "Seq2slate - Re-ranking and slate optimization with rnns", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Seq2slate - Re-ranking and slate optimization with rnns.pdf", "year": 1900, "id": 297}, {"tag": ["Re-Rank"], "name": "SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/SLATEQ - A Tractable Decomposition for Reinforcement Learning with Recommendation Sets.pdf", "year": 1900, "id": 298}, {"tag": ["Re-Rank"], "name": "Sliding Spectrum Decomposition for Diversified Recommendation", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/Sliding Spectrum Decomposition for Diversified Recommendation.pdf", "year": 1900, "id": 299}, {"tag": ["Re-Rank"], "name": "The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries.pdf", "year": 1900, "id": 300}, {"tag": ["Re-Rank"], "name": "User Response Models to Improve a REINFORCE Recommender System", "category": "Re-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/User Response Models to Improve a REINFORCE Recommender System.pdf", "year": 1900, "id": 301}, {"tag": ["Re-Rank"], "name": "[2018][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity", "category": "Re-Rank", "authors": [], "company": "Hulu", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/[2018][Hulu] Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity.pdf", "year": 2018, "id": 302}, {"tag": ["Re-Rank"], "name": "[2020][LinkedIn] Ads Allocation in Feed via Constrained Optimization", "category": "Re-Rank", "authors": [], "company": "LinkedIn", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Re-Rank/[2020][LinkedIn] Ads Allocation in Feed via Constrained Optimization.pdf", "year": 2020, "id": 303}, {"tag": ["Reinforce"], "name": "Jointly Learning to Recommend and Advertise", "category": "Reinforce", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Reinforce/Jointly Learning to Recommend and Advertise.pdf", "year": 1900, "id": 304}, {"tag": ["Reinforce"], "name": "Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology", "category": "Reinforce", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Reinforce/Reinforcement Learning for Slate-based Recommender Systems - A Tractable Decomposition and Practical Methodology.pdf", "year": 1900, "id": 305}, {"tag": ["Reinforce"], "name": "Top-K Off-Policy Correctionfor a REINFORCE Recommender System", "category": "Reinforce", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Reinforce/Top-K Off-Policy Correctionfor a REINFORCE Recommender System.pdf", "year": 1900, "id": 306}, {"tag": ["Cold-Start", "Exploration&Exploitation"], "name": "A Contextual-Bandit Approach to Personalized News Article Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Exploration&Exploitation/A Contextual-Bandit Approach to Personalized News Article Recommendation.pdf", "year": 1900, "id": 46}, {"tag": ["Cold-Start", "Exploration&Exploitation"], "name": "Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Exploration&Exploitation/Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction.pdf", "year": 1900, "id": 47}, {"tag": ["Cold-Start", "Exploration&Exploitation"], "name": "An Empirical Evaluation of Thompson Sampling", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Exploration&Exploitation/An Empirical Evaluation of Thompson Sampling.pdf", "year": 1900, "id": 48}, {"tag": ["Cold-Start", "Exploration&Exploitation"], "name": "Comparison-based Conversational Recommender System with Relative Bandit Feedback", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/Exploration&Exploitation/Comparison-based Conversational Recommender System with Relative Bandit Feedback.pdf", "year": 1900, "id": 49}, {"tag": ["Cold-Start", "MetaLearning"], "name": "A Meta-Learning Perspective on Cold-Start Recommendations for Items", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/A Meta-Learning Perspective on Cold-Start Recommendations for Items.pdf", "year": 1900, "id": 50}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction.pdf", "year": 1900, "id": 51}, {"tag": ["Cold-Start", "MetaLearning"], "name": "MeLU - Meta-Learned User Preference Estimator for Cold-Start Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/MeLU - Meta-Learned User Preference Estimator for Cold-Start Recommendation.pdf", "year": 1900, "id": 52}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks.pdf", "year": 1900, "id": 53}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation.pdf", "year": 1900, "id": 54}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Personalized Adaptive Meta Learning for Cold-start User Preference Prediction", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Personalized Adaptive Meta Learning for Cold-start User Preference Prediction.pdf", "year": 1900, "id": 55}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Preference-Adaptive Meta-Learning for Cold-Start Recommendation.pdf", "year": 1900, "id": 56}, {"tag": ["Cold-Start", "MetaLearning"], "name": "Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users", "category": "Cold-Start", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Cold-Start/MetaLearning/Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users.pdf", "year": 1900, "id": 57}, {"tag": ["Industry", "Bundle"], "name": "Bundle Recommendation with Graph Convolutional Networks", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Bundle/Bundle Recommendation with Graph Convolutional Networks.pdf", "year": 1900, "id": 180}, {"tag": ["Industry", "Bundle"], "name": "CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Bundle/CrossCBR - Cross-view Contrastive Learning for Bundle Recommendation.pdf", "year": 1900, "id": 181}, {"tag": ["Industry", "Bundle"], "name": "Hierarchical Fashion Graph Network for Personalized Outfit Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Bundle/Hierarchical Fashion Graph Network for Personalized Outfit Recommendation.pdf", "year": 1900, "id": 182}, {"tag": ["Industry", "Dataset"], "name": "KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Dataset/KuaiRand - An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos.pdf", "year": 1900, "id": 183}, {"tag": ["Industry", "Dataset"], "name": "KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Dataset/KuaiRec - A Fully-observed Dataset and Insights for Evaluating Recommender Systems.pdf", "year": 1900, "id": 184}, {"tag": ["Industry", "Edge"], "name": "Real-time Short Video Recommendation on Mobile Devices", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Edge/Real-time Short Video Recommendation on Mobile Devices.pdf", "year": 1900, "id": 185}, {"tag": ["Industry", "FeatureHashing"], "name": "Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems.pdf", "year": 1900, "id": 186}, {"tag": ["Industry", "FeatureHashing"], "name": "Feature Hashing for Large Scale Multitask Learning", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/Feature Hashing for Large Scale Multitask Learning.pdf", "year": 1900, "id": 187}, {"tag": ["Industry", "FeatureHashing"], "name": "Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/Getting Deep Recommenders Fit - Bloom Embeddings for Sparse Binary Input Output Networks.pdf", "year": 1900, "id": 188}, {"tag": ["Industry", "FeatureHashing"], "name": "Hash Embeddings for Efficient Word Representations", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/Hash Embeddings for Efficient Word Representations.pdf", "year": 1900, "id": 189}, {"tag": ["Industry", "FeatureHashing"], "name": "Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems.pdf", "year": 1900, "id": 190}, {"tag": ["Industry", "FeatureHashing"], "name": "[2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation", "category": "Industry", "authors": [], "company": "Google", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/FeatureHashing/[2021][Google][DHE] Learning to Embed Categorical Features without Embedding Tables for Recommendation.pdf", "year": 1900, "id": 191}, {"tag": ["Industry", "Intent"], "name": "Automatically Discovering User Consumption Intents in Meituan", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Intent/Automatically Discovering User Consumption Intents in Meituan.pdf", "year": 1900, "id": 192}, {"tag": ["Industry", "Intent"], "name": "FINN - Feedback Interactive Neural Network for Intent Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Intent/FINN - Feedback Interactive Neural Network for Intent Recommendation.pdf", "year": 1900, "id": 193}, {"tag": ["Industry", "Intent"], "name": "Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Intent/Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation.pdf", "year": 1900, "id": 194}, {"tag": ["Industry", "POI"], "name": "A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals.pdf", "year": 1900, "id": 195}, {"tag": ["Industry", "POI"], "name": "A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations.pdf", "year": 1900, "id": 196}, {"tag": ["Industry", "POI"], "name": "Empowering Next POI Recommendation with Multi-Relational Modeling", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Empowering Next POI Recommendation with Multi-Relational Modeling.pdf", "year": 1900, "id": 197}, {"tag": ["Industry", "POI"], "name": "Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation.pdf", "year": 1900, "id": 198}, {"tag": ["Industry", "POI"], "name": "LightMove - A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/LightMove - A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising.pdf", "year": 1900, "id": 199}, {"tag": ["Industry", "POI"], "name": "Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation.pdf", "year": 1900, "id": 200}, {"tag": ["Industry", "POI"], "name": "Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences.pdf", "year": 1900, "id": 201}, {"tag": ["Industry", "POI"], "name": "Online POI Recommendation - Learning Dynamic Geo-Human Interactions in Streams", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Online POI Recommendation - Learning Dynamic Geo-Human Interactions in Streams.pdf", "year": 1900, "id": 202}, {"tag": ["Industry", "POI"], "name": "Point-of-Interest Recommender Systems based on Location-Based Social Networks - A Survey from an Experimental Perspective", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Point-of-Interest Recommender Systems based on Location-Based Social Networks - A Survey from an Experimental Perspective.pdf", "year": 1900, "id": 203}, {"tag": ["Industry", "POI"], "name": "POINTREC - A Test Collection for Narrative-driven Point of Interest Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/POINTREC - A Test Collection for Narrative-driven Point of Interest Recommendation.pdf", "year": 1900, "id": 204}, {"tag": ["Industry", "POI"], "name": "TADSAM - A Time-Aware Dynamic Self-Attention Model for Next Point-of-Interest Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/TADSAM - A Time-Aware Dynamic Self-Attention Model for Next Point-of-Interest Recommendation.pdf", "year": 1900, "id": 205}, {"tag": ["Industry", "POI"], "name": "Where to Go Next - Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Where to Go Next - Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation.pdf", "year": 1900, "id": 206}, {"tag": ["Industry", "POI"], "name": "Why We Go Where We Go - Profiling User Decisions on Choosing POIs", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/Why We Go Where We Go - Profiling User Decisions on Choosing POIs.pdf", "year": 1900, "id": 207}, {"tag": ["Industry", "POI"], "name": "You Are What and Where You Are - Graph Enhanced Attention Network for Explainable POI Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/You Are What and Where You Are - Graph Enhanced Attention Network for Explainable POI Recommendation.pdf", "year": 1900, "id": 208}, {"tag": ["Industry", "POI", "STGCN", "Meituan"], "name": "[2020][meituan][STGCN] STGCN - A Spatial-Temporal Aware Graph Learning Method for POI Recommendation", "category": "Industry", "authors": [], "company": "Meituan", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/POI/[2020][meituan][STGCN] STGCN - A Spatial-Temporal Aware Graph Learning Method for POI Recommendation.pdf", "year": 2020, "id": 209}, {"tag": ["Industry", "Reciprocal"], "name": "MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Reciprocal/MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING.pdf", "year": 1900, "id": 210}, {"tag": ["Industry", "Regression"], "name": "Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation", "category": "Industry", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Regression/Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.pdf", "year": 1900, "id": 211}, {"tag": ["Industry", "Representation", "Pinterest"], "name": "[2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest", "category": "Industry", "authors": [], "company": "Pinterest", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Industry/Representation/[2022][Pinterest][PinnerFormer] PinnerFormer - Sequence Modeling for User Representation at Pinterest.pdf", "year": 2022, "id": 212}, {"tag": ["Learning-to-Rank", "List-wise"], "name": "AdaRank - A Boosting Algorithm for Information Retrieval", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/List-wise/AdaRank - A Boosting Algorithm for Information Retrieval.pdf", "year": 1900, "id": 213}, {"tag": ["Learning-to-Rank", "List-wise"], "name": "From RankNet to LambdaRank to LambdaMART", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/List-wise/From RankNet to LambdaRank to LambdaMART.pdf", "year": 1900, "id": 214}, {"tag": ["Learning-to-Rank", "List-wise"], "name": "LambdaMART - Adapting Boosting for Information Retrieval Measures", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/List-wise/LambdaMART - Adapting Boosting for Information Retrieval Measures.pdf", "year": 1900, "id": 215}, {"tag": ["Learning-to-Rank", "List-wise"], "name": "ListNet - Learning to Rank - From Pairwise Approach to Listwise Approach", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/List-wise/ListNet - Learning to Rank - From Pairwise Approach to Listwise Approach.pdf", "year": 1900, "id": 216}, {"tag": ["Learning-to-Rank", "Pair-wise"], "name": "LambdaRank - Learning to Rank with Nonsmooth Cost Functions", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/Pair-wise/LambdaRank - Learning to Rank with Nonsmooth Cost Functions.pdf", "year": 1900, "id": 217}, {"tag": ["Learning-to-Rank", "Pair-wise"], "name": "RankBoost - An Effcient Boosting Algorithm for Combining Preferences", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/Pair-wise/RankBoost - An Effcient Boosting Algorithm for Combining Preferences.pdf", "year": 1900, "id": 218}, {"tag": ["Learning-to-Rank", "Pair-wise"], "name": "RankNET - Learning to Rank Using Gradient Descent", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/Pair-wise/RankNET - Learning to Rank Using Gradient Descent.pdf", "year": 1900, "id": 219}, {"tag": ["Learning-to-Rank", "Point-wise"], "name": "Learning to Rank Using Classification and Gradient", "category": "Learning-to-Rank", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Learning-to-Rank/Point-wise/Learning to Rank Using Classification and Gradient.pdf", "year": 1900, "id": 220}, {"tag": ["Match", "Classic"], "name": "Collaborative Filtering Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Classic/Collaborative Filtering Recommender Systems.pdf", "year": 1900, "id": 259}, {"tag": ["Match", "Classic"], "name": "GroupLens - An open architecture for collaborative filtering of Netnews", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Classic/GroupLens - An open architecture for collaborative filtering of Netnews.pdf", "year": 1900, "id": 260}, {"tag": ["Match", "Classic"], "name": "Item-Based Collaborative Filtering Recommendation Algorithms", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Classic/Item-Based Collaborative Filtering Recommendation Algorithms.pdf", "year": 1900, "id": 261}, {"tag": ["Match", "GNN"], "name": "ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/ATBRG - Adaptive Target-Behavior Relational Graph Network for Effective Recommendation.pdf", "year": 1900, "id": 262}, {"tag": ["Match", "GNN"], "name": "Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View.pdf", "year": 1900, "id": 263}, {"tag": ["Match", "GNN"], "name": "Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems.pdf", "year": 1900, "id": 264}, {"tag": ["Match", "GNN"], "name": "Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items.pdf", "year": 1900, "id": 265}, {"tag": ["Match", "GNN"], "name": "Disentangled Graph Collaborative Filtering", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Disentangled Graph Collaborative Filtering.pdf", "year": 1900, "id": 266}, {"tag": ["Match", "GNN"], "name": "Embedding-based News Recommendationfor Millions of Users", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Embedding-based News Recommendationfor Millions of Users.pdf", "year": 1900, "id": 267}, {"tag": ["Match", "GNN"], "name": "Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction.pdf", "year": 1900, "id": 268}, {"tag": ["Match", "GNN"], "name": "Friend Recommendations with Self-Rescaling Graph Neural Networks", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Friend Recommendations with Self-Rescaling Graph Neural Networks.pdf", "year": 1900, "id": 269}, {"tag": ["Match", "GNN"], "name": "Graph Convolutional Matrix Completion", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Graph Convolutional Matrix Completion.pdf", "year": 1900, "id": 270}, {"tag": ["Match", "GNN"], "name": "Graph Intention Network for Click-through Rate Prediction in Sponsored Search", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Graph Intention Network for Click-through Rate Prediction in Sponsored Search.pdf", "year": 1900, "id": 271}, {"tag": ["Match", "GNN"], "name": "Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation.pdf", "year": 1900, "id": 272}, {"tag": ["Match", "GNN"], "name": "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Graph Neural Networks for Friend Ranking in Large-scale Social Platforms.pdf", "year": 1900, "id": 273}, {"tag": ["Match", "GNN"], "name": "Graph Neural Networks for Social Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Graph Neural Networks for Social Recommendation.pdf", "year": 1900, "id": 274}, {"tag": ["Match", "GNN"], "name": "GraphSAIL - Graph Structure Aware Incremental Learning for Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/GraphSAIL - Graph Structure Aware Incremental Learning for Recommender Systems.pdf", "year": 1900, "id": 275}, {"tag": ["Match", "GNN"], "name": "IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/IntentGC - a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation.pdf", "year": 1900, "id": 276}, {"tag": ["Match", "GNN"], "name": "metapath2vec - Scalable Representation Learning for Heterogeneous Networks", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/metapath2vec - Scalable Representation Learning for Heterogeneous Networks.pdf", "year": 1900, "id": 277}, {"tag": ["Match", "GNN"], "name": "MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/MMGCN - Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video.pdf", "year": 1900, "id": 278}, {"tag": ["Match", "GNN"], "name": "Neighbor Interaction Aware Graph Convolution Networks for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Neighbor Interaction Aware Graph Convolution Networks for Recommendation.pdf", "year": 1900, "id": 279}, {"tag": ["Match", "GNN"], "name": "Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Network Embedding as Matrix Factorization - Unifying DeepWalk, LINE, PTE, and node2vec.pdf", "year": 1900, "id": 280}, {"tag": ["Match", "GNN"], "name": "Package Recommendation with Intra- and Inter-Package Attention Networks", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Package Recommendation with Intra- and Inter-Package Attention Networks.pdf", "year": 1900, "id": 281}, {"tag": ["Match", "GNN"], "name": "PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/PinnerSage - Multi-Modal User Embedding Framework for Recommendations at Pinterest.pdf", "year": 1900, "id": 282}, {"tag": ["Match", "GNN"], "name": "ProNE - Fast and Scalable Network Representation Learning", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/ProNE - Fast and Scalable Network Representation Learning.pdf", "year": 1900, "id": 283}, {"tag": ["Match", "GNN"], "name": "Representation Learning for Attributed Multiplex Heterogeneous Network", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Representation Learning for Attributed Multiplex Heterogeneous Network.pdf", "year": 1900, "id": 284}, {"tag": ["Match", "GNN"], "name": "Revisiting Item Promotion in GNN-based Collaborative Filtering - A Masked Targeted Topological Attack Perspective", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Revisiting Item Promotion in GNN-based Collaborative Filtering - A Masked Targeted Topological Attack Perspective.pdf", "year": 1900, "id": 285}, {"tag": ["Match", "GNN"], "name": "Self-supervised Graph Learning for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Self-supervised Graph Learning for Recommendation.pdf", "year": 1900, "id": 286}, {"tag": ["Match", "GNN"], "name": "Self-Supervised Hypergraph Transformer for Recommender Systems", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/Self-Supervised Hypergraph Transformer for Recommender Systems.pdf", "year": 1900, "id": 287}, {"tag": ["Match", "GNN"], "name": "struc2vec - Learning Node Representations from Structural Identity", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/struc2vec - Learning Node Representations from Structural Identity.pdf", "year": 1900, "id": 288}, {"tag": ["Match", "GNN"], "name": "SVD-GCN - A Simplified Graph Convolution Paradigm for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/SVD-GCN - A Simplified Graph Convolution Paradigm for Recommendation.pdf", "year": 1900, "id": 289}, {"tag": ["Match", "GNN"], "name": "TwHIN - Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/TwHIN - Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation.pdf", "year": 1900, "id": 290}, {"tag": ["Match", "GNN", "DeepWalk"], "name": "[2014][DeepWalk] DeepWalk - Online Learning of Social Representations", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2014][DeepWalk] DeepWalk - Online Learning of Social Representations.pdf", "year": 2014, "id": 291}, {"tag": ["Match", "GNN", "word2vec"], "name": "[2014][word2vec] Negative-Sampling Word-Embedding Method", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2014][word2vec] Negative-Sampling Word-Embedding Method.pdf", "year": 2014, "id": 292}, {"tag": ["Match", "GNN", "Line"], "name": "[2015][Microsoft][LINE] LINE - Large-scale Information Network Embedding", "category": "Match", "authors": [], "company": "Microsoft", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2015][Microsoft][LINE] LINE - Large-scale Information Network Embedding.pdf", "year": 2015, "id": 293}, {"tag": ["Match", "GNN"], "name": "[2016][item2vec] ITEM2VEC - NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2016][item2vec] ITEM2VEC - NEURAL ITEM EMBEDDING FOR COLLABORATIVE FILTERING.pdf", "year": 2016, "id": 294}, {"tag": ["Match", "GNN"], "name": "[2016][SDNE] Structural Deep Network Embedding", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2016][SDNE] Structural Deep Network Embedding.pdf", "year": 2016, "id": 295}, {"tag": ["Match", "GNN"], "name": "[2016][Stanford][node2vec] node2vec - Scalable Feature Learning for Networks", "category": "Match", "authors": [], "company": "Stanford", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2016][Stanford][node2vec] node2vec - Scalable Feature Learning for Networks.pdf", "year": 2016, "id": 296}, {"tag": ["Match", "GNN"], "name": "[2016][word2vec] word2vec Parameter Learning Explained", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2016][word2vec] word2vec Parameter Learning Explained.pdf", "year": 2016, "id": 297}, {"tag": ["Match", "GNN"], "name": "[2017][GCN] SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2017][GCN] SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS.pdf", "year": 2017, "id": 298}, {"tag": ["Match", "GNN"], "name": "[2017][Stanford][GraphSage] Inductive Representation Learning on Large Graphs", "category": "Match", "authors": [], "company": "Stanford", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2017][Stanford][GraphSage] Inductive Representation Learning on Large Graphs.pdf", "year": 2017, "id": 299}, {"tag": ["Match", "GNN"], "name": "[2018][Alibaba] Learning and Transferring IDs Representation in E-commerce", "category": "Match", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2018][Alibaba] Learning and Transferring IDs Representation in E-commerce.pdf", "year": 2018, "id": 300}, {"tag": ["Match", "GNN"], "name": "[2018][Alibaba][EGES] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba", "category": "Match", "authors": [], "company": "Alibaba", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2018][Alibaba][EGES] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf", "year": 2018, "id": 301}, {"tag": ["Match", "GNN"], "name": "[2018][Etsy] Learning Item-Interaction Embeddings for User Recommendations", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2018][Etsy] Learning Item-Interaction Embeddings for User Recommendations.pdf", "year": 2018, "id": 302}, {"tag": ["Match", "GNN"], "name": "[2018][GAT] GRAPH ATTENTION NETWORKS", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2018][GAT] GRAPH ATTENTION NETWORKS.pdf", "year": 2018, "id": 303}, {"tag": ["Match", "GNN"], "name": "[2018][Pinterest][PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems", "category": "Match", "authors": [], "company": "Pinterest", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2018][Pinterest][PinSage] Graph Convolutional Neural Networks for Web-Scale Recommender Systems.pdf", "year": 2018, "id": 304}, {"tag": ["Match", "GNN"], "name": "[2019][NGCF]Neural Graph Collaborative Filtering", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2019][NGCF]Neural Graph Collaborative Filtering.pdf", "year": 2019, "id": 305}, {"tag": ["Match", "GNN"], "name": "[2019][SR-GNN] Session-based Recommendation with Graph Neural Networks", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2019][SR-GNN] Session-based Recommendation with Graph Neural Networks.pdf", "year": 2019, "id": 306}, {"tag": ["Match", "GNN"], "name": "[2020][LightGCN] LightGCN - Simplifying and Powering Graph Convolution Network for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/GNN/[2020][LightGCN] LightGCN - Simplifying and Powering Graph Convolution Network for Recommendation.pdf", "year": 2020, "id": 307}, {"tag": ["Match", "Mulit-Interset"], "name": "Every Preference Changes Differently - Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Mulit-Interset/Every Preference Changes Differently - Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation.pdf", "year": 1900, "id": 308}, {"tag": ["Match", "Mulit-Interset"], "name": "Improving Multi-Interest Network with Stable Learning", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Mulit-Interset/Improving Multi-Interest Network with Stable Learning.pdf", "year": 1900, "id": 309}, {"tag": ["Match", "Mulit-Interset"], "name": "Multiple Interest and Fine Granularity Network for User Modeling", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Mulit-Interset/Multiple Interest and Fine Granularity Network for User Modeling.pdf", "year": 1900, "id": 310}, {"tag": ["Match", "Mulit-Interset"], "name": "[2019][Alibaba][MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Mulit-Interset/[2019][Alibaba][MIND] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall.pdf", "year": 2019, "id": 311}, {"tag": ["Match", "Mulit-Interset"], "name": "[2020][Alibaba][ComiRec] Controllable Multi-Interest Framework for Recommendation", "category": "Match", "authors": [], "company": "", "url": "https://github.com/tangxyw/RecSysPapers/blob/main/Match/Mulit-Interset/[2020][Alibaba][ComiRec] Controllable Multi-Interest Framework for Recommendation.pdf", "year": 2020, "id": 312}]