Recommender Systems Paperlist

Survey Papers

• Deep Learning based Recommender System: A Survey and New Perspectives [2017][PDF]
• 基于深度学习的推荐系统研究综述 [2018] [PDF]
• Explainable Recommendation: A Survey and New Perspectives [2018] [PDF]
• Sequence-Aware Recommender Systems [2018] [PDF]
• DeepRec: An Open-source Toolkit for Deep Learning based Recommendation [IJCAI 2019] [PDF]
• Graph Embedding Techniques, Applications, and Performance: A Survey [2018] [PDF]
• A survey on session-based recommender systems [2021] [PDF]
• Graph Learning based Recommender Systems: A Review [IJCAI 2021] [PDF]

Recommender Systems with Content Information

Review-based Approaches

• Convolutional Matrix Factorization for Document Context-Aware Recommendation [RecSys 2016] [PDF] [code]

• Joint Deep Modeling of Users and Items Using Reviews for Recommendation [WSDM 2017][PDF][code]

• Multi-Pointer Co-Attention Networks for Recommendation [KDD 2018][PDF][code]

• Gated attentive-autoencoder for content-aware recommendation [WSDM 2019][PDF][code]

Collaborative Filtering Recommendations

• Neural Collaborative Filtering [WWW 2017][PDF][code]
• Collaborative Denoising Auto-Encoders for Top-N Recommender Systems [pdf] [code]
• Outer Product-based Neural Collaborative Filtering [IJCAI 2018][PDF][code]
• Neural Graph Collaborative Filtering [SIGIR 2019] [PDF][code]
• Transnets: Learning to transform for recommendation [RecSys 2017][PDF][code]
• Metric Factorization: Recommendation beyond Matrix Factorization [PDF][code]
• Improving Top-K Recommendation via Joint Collaborative Autoencoders [PDF][code]
• Collaborative Metric Learning [WWW2017][code][PDF]
• NeuRec : On Nonlinear Transformation for Personalized Ranking [IJACA 2018] [PDF][code]
• DeepCF : A Unified Framework of Representation Learning and Matching Function Learning in Recommender System [AAAI2019 oral] [PDF][code]
• Try This Instead:Personalized and Interpretable Substitute Recommendation [[SIGIR2020]] [PDF][code]
• STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [[IJCAI2019]] [PDF] [code]
• Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks [[ICTIR2019]] [PDF] [code]
• Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020] [PDF][code]
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [AAAI2020] [PDF][code]
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation [SIGIR2020] [PDF][code]
• Disentangled Graph Collaborative Filtering [SIGIR2020][PDF] [code]
• HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering [WWW2021] [PDF][code]
• Contrastive Learning for Recommender System [2021] [PDF]

Explainable Recommender Systems

• Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [SIGIR 2018][PDF]
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation [WWW 2018][PDF]
• Neural Attentional Rating Regression with Review-level Explanations [WWW 2018] [PDF][code]

Sequence-Aware Recommender Systems

Session-based Recommender Systems

• Session-based Recommendations with Recurrent Neural Networks [ICLR 2016] [PDF][code]

• Neural Attentive Session-based Recommendation [CIKM 2017] [PDF][code]

• When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation [RecSys 2017][PDF]

• STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation [KDD 2018] [PDF][code]

• RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation [AAAI 2019][PDF][code]

• Session-based Recommendation with Graph Neural Networks [AAAI 2019][PDF][code]

• Streaming Session-based Recommendation [KDD 2019] [PDF]

• Session-based Social Recommendation via Dynamic Graph Attention Networks [WSDM 2019][PDF][code]

• Sequence and Time Aware Neighborhood for Session-based Recommendations [SIGIR 2019] [PDF]

• Performance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation [RecSys 2019][PDF]

• Predictability Limits in Session-based Next Item Recommendation [RecSys 2019][PDF]

• Empirical Analysis of Session-Based Recommendation Algorithms [2019] [PDF][code]

• A Collaborative Session-based Recommendation Approach with Parallel Memory Modules [SIGIR2019][PDF] [code]

• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks [CIKM2019][PDF]

• Session-based Recommendation with Hierarchical Memory Networks [CIKM2019] [PDF]

• ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation [IJCAI2019][PDF]

• TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [SIGIR2020][PDF][code]

• Star Graph Neural Networks for Session-based Recommendation[CIKM2020][PDF]

• Session-based Recommendation with Hierarchical Leaping Networks[SIGIR2020]][PDF]

• Handling Information Loss of Graph Neural Networks for Session-based Recommendation[KDD2020][PDF][code]

• Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation[SIGIR2020][PDF][code]

• Session-aware Linear Item-Item Models for Session-based Recommendation [WWW2021] [PDF][code]

Sequential recommendations

• Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding [WSDM 2018][PDF][code]
• Self-Attentive Sequential Recommendation [ICDM 2018] [PDF][code]
• Hierarchical Gating Networks for Sequential Recommendation [KDD 2019][PDF][code]
• Next Item Recommendation with Self-Attention [ACM 2018][PDF][code]
• Time Interval Aware Self-Attention for Sequential Recommendation [WSDM2020][PDF]
• Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce [WSDM2020][PDF]
• Disentangled Self-Supervision in Sequential Recommenders [KDD2020][PDF]
• DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation [CIKM2020][PDF][code]
• S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [CIKM2020][PDF][code]

Long and short-term Sequential Recommendations

• Collaborative Memory Network for Recommendation Systems [SIGIR 2018][PDF][code]
• Sequential Recommender System based on Hierarchical Attention Network [IJCAI 2018] [PDF][code]
• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems [WWW 2019] [PDF]
• A Large-scale Sequential Deep Matching Model for E-commerce Recommendation[CIKM 2019][PDF][code]
• Recurrent Neural Networks for Long and Short-Term Sequential Recommendation [RecSys 2018] [PDF]
• A Dynamic Co-attention Network for Session-based Recommendation [CIKM 2019][PDF]
• A Long-Short Demands-Aware Model for Next-Item Recommendation [CoRR 2019][PDF]
• Learning from History and Present : Next-item Recommendation via Discriminatively Exploiting User Behaviors [KDD 2018][PDF][JD]
• A Review-Driven Neural Model for Sequential Recommendation [IJCAI 2019] [PDF]
• Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation [IJCAI 2019] [PDF][code][Microsoft]
• Long- and Short-term Preference Learning for Next POI Recommendation [CIKM 2019] [PDF]
• Neural News Recommendation with Long- and Short-term User Representations [ACL 2019][Microsoft][PDF]
• CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation [PDF]
• Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation [SIGIR2020][PDF]
• MRIF: Multi-resolution Interest Fusion for Recommendation [SIGIR2020] [PDF]

Context-Aware Sequential Recommendations

• Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks [WWW 2019][PDF][code]

Knowledge Graph-based Recommendations

• Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks [SIGIR 2018] [PDF]
  • dataset and code : https://github.com/RUCDM/KB4Rec
• DKN: Deep Knowledge-Aware Network for News Recommendation [WWW 2018] [PDF][code]
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems [CIKM 2018] [PDF][code]
• Knowledge Graph Convolutional Networks for Recommender Systems [WWW 2019] [PDF][code]
• KGAT: Knowledge Graph Attention Network for Recommendation [KDD2019][PDF][code]

Reinforcement Learning Approaches

• DRN: A Deep Reinforcement Learning Framework for News Recommendation [WWW 2018] [PDF]
• Top-K Off-Policy Correction for a REINFORCE Recommender System [WSDM 2019] [PDF][[Youtube]]

Multi-behavior learning for Recommendation

• Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [AAAI2020][PDF][code]

Multi-Interest and Diversity learning for Recommendation

• Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction [SIGIR2020][PDF][code]
• Controllable Multi-Interest Framework for Recommendation [KDD2020] [PDF][code]
• Multi-Interest Network with Dynamic Routing for Recommendation at Tmall [PDF]
• Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks (IJCAI2020)[PDF]
• Sequential and Diverse Recommendation with Long Tail [IJCAI2019][PDF]
• Improving End-to-End Sequential Recommendations with Intent-aware Diversification [CIKM2020) [PDF][code]
• A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks [KDD2020][PDF]
• M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems[KDD2020][PDF][Alibaba Match]
• Deep Multi-Interest Network for Click-through Rate Prediction [CIKM2020][PDF]
• Recent Advances in Diversified Recommendation
• Diversified Interactive Recommendation with Implicit Feedback [AAAI2020]

Multi-task learning for Recommendation

• [ESMM]Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018][PDF]
• [ESM2]Conversion Rate Prediction via Post-Click Behaviour Modeling
• [MMoE]Modeling task relationships in multi-task learning with multi-gate mixture-of-experts [KDD2018][PDF]
• Recommending What Video to Watch Next: A Multitask Ranking System [RecSys2019][PDF]
• [RecSys2020 best paper]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations[Recsys2020][PDF]

Re-ranking

• Personalized Re-ranking for Recommendation [RecSys2019][PDF][code][dataset]
• Learning a Deep Listwise Context Model for Ranking Refinement [SIGIR2018][PDF][code]

Industry

CTR Prediction

• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [[IJCAI 2017] [PDF] [Huawei]

• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems] [KDD2018] [PDF] [Microsoft]

• Order-aware Embedding Neural Network for CTR Prediction][SIGIR 2019] [PDF] [Huawei]

• Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction [WWW 2019] [PDF] [Huawei]

• Interaction-aware Factorization Machines for Recommender Systems [AAAI2019] [PDF][code][Tencent]

Match

• [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017][PDF]

• [Embedding] DeepWalk- Online Learning of Social Representations [KDD 2014][PDF]

• [Embedding] LINE - Large-scale Information Network Embedding [Microsoft 2015][PDF]

• [Embedding] Node2vec - Scalable Feature Learning for Networks [Stanford 2016][PDF]

• [Embedding] Structural Deep Network Embedding [KDD2016] [PDF]

• [Embedding] Item2Vec-Neural Item Embedding for Collaborative Filtering [Microsoft 2017][PDF]

• [Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb [KDD 2018] [PDF]

• [Embedding] Graph Convolutional Neural Networks for Web-Scale Recommender Systems [KDD 2018] [PDF][Pinterest]

• Is a Single Embedding Enough ? Learning Node Representations that Capture Multiple Social Contexts [WWW 2019] [PDF]

• [Embedding] Representation Learning for Attributed Multiplex Heterogeneous Network [KDD 2019] [PDF]

• [DNN Match] Deep Neural Networks for YouTube Recommendations [RecSys 2016] [PDF][Youtube]

• [DNN Match] Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations[RecSys 2019] [PDF]

• [Semantic Match] Deep Semantic Matching for Amazon Product Search [WSDM 2019][PDF][Amazon]

• [Tree Match] Joint Optimization of Tree-based Index and Deep Model for Recommender Systems [NeurIPS 2019] [PDF][Tencent]

Others

• Latent Cross: Making Use of Context in Recurrent Recommender Systems [WSDM 2018][PDF][Youtube]

• Learning from History and Present: Next-item Recommendation via Discriminatively Exploting Users Behaviors [KDD 2018][PDF]

• Real-time Attention Based Look-alike Model for Recommender System [KDD 2019] [PDF] [Tencent]

Alibaba papers-continuous updating

• [Match] TDM：Learning Tree-based Deep Model for Recommender Systems [KDD2018] [PDF]

• [Match] Multi-Interest Network with Dynamic Routing for Recommendation at Tmall [2019][PDF]

• [Long and short-term] SDM: Sequential Deep Matching Model for Online Large-scale Recommender System [CIKM 2019][PDF]

• [Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba [KDD 2018][PDF]

• [Embedding] Learning and Transferring IDs Representation in E-commerce [KDD 2018] [PDF]

• [Representations] ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation [AAAI 2018] [PDF]

• [Representations] Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks [KDD2018][PDF]

• [exact-K recommendation] Exact-K Recommendation via Maximal Clique Optimization [KDD 2019][PDF]

• [Explain]A Capsule Network for Recommendation and Explaining What You Like and Dislike [SIGIR2019][PDF][code]

• [CTR] Privileged Features Distillation for E-Commerce Recommendations [Woodstock ’18][PDF]

• [CTR] Representation Learning-Assisted Click-Through Rate Prediction [IJCAI 2019] [PDF]

• [CTR] Deep Session Interest Network for Click-Through Rate Prediction [IJCAI 2019] [PDF]

• [CTR] Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction] [KDD2019] [PDF] [code]

• [CTR] Graph Intention Network for Click-through Rate Prediction in Sponsored Search [SIGIR2019] [PDF]

• [CTR] Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction [MLR][PDF]

• [CTR] Deep Interest Evolution Network for Click-Through Rate Prediction [AAAI2019][PDF]

• [CTR] Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction[KDD2019] [PDF][code]

• [CTR] Behavior Sequence Transformer for E-commerce Recommendation in Alibaba [PDF]

• [CVR] Entire Space Multi-Task Model: An E ective Approach for Estimating Post-Click Conversion Rate [SIGIR2018][PDF]

• [CTR] Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction [SIGIR2019] [PDF] [code]

• [CTR] Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction[Interpretation]