readingList.md

Reading List on RecSys

This reading list presents some survey papers on RecSys, followed by lots of research papers corresponding to several sub-fields of recommendation.

Note: We only list those we have read or we are aware of.

Survey

• Measuring the Business Value of Recommender Systems. arxiv'19. (paper)
• A Survey on Session-based Recommender Systems. arxiv'19. (paper)
• Deep Learning-based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations. arxiv'19. (paper)
• A review on deep learning for recommender systems: challenges and remedies. AI Review'18. (paper)
• Explainable Recommendation: A Survey and New Perspectives. arxiv'18. (paper)
• Evaluation of Session-based Recommendation Algorithms. arxiv'18. (paper)
• Deep learning based recommender system: A survey and new perspectives. CSUR'18. (paper)
• Sequence-Aware Recommender Systems. arxiv'18. (paper)
• A survey of point-of-interest recommendation in location-based social networks. arxiv'16. (paper)
• Social Recommendation: A Review. SNAM'13. (paper)
• Recommender systems survey. 2013. (paper)

General Methods

• Markov Random Field for Collaborative Filtering. NeurIPS'19. (paper)
• Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. RecSys'19. (paper)(code)
• Infer Implicit Contexts in Real-time Online-to-Offline Recommendation. KDD'19. (paper)
• A Capsule Network for Recommendation and Explaining What You Like and Dislike. SIGIR'19. (paper)(code)
• Compositional Coding for Collaborative Filtering. SIGIR'19. (paper)(code)
• Joint Optimization of Tree-based Index and Deep Model for Recommender Systems. arxiv'19. (paper)
• Gated Attentive-Autoencoder for Content-Aware Recommendation. WSDM'19. (paper)
• Real-time Personalization using Embeddings for Search Ranking at Airbnb. KDD'18. (paper)
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems. KDD'18. (paper)
• Local Latent Space Models for Top-N Recommendation. KDD'18. (paper)
• Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors. KDD'18. (paper)
• Variational Autoencoders for Collaborative Filtering. WWW'18. (paper)
• Learning Tree-based Deep Model for Recommender Systems. arxiv'18. (paper)
• Collaborative Memory Network for Recommendation Systems. SIGIR'18. (paper)
• Regularizing Matrix Factorization with User and Item Embeddings for Recommendation. CIKM'18. (paper)
• Neural collaborative filtering. WWW'17. (paper)(code)
• CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. WWW'17. (paper)
• Collaborative metric learning. WWW'17. (paper)
• Collaborative denoising auto-encoders for top-n recommender systems. WSDM'16. (paper)
• Fast matrix factorization for online recommendation with implicit feedback. SIGIR'16. (paper)
• A neural autoregressive approach to collaborative filtering. ICML'16. (paper)
• Deep neural networks for youtube recommendations. RecSys'16. (paper)
• Autorec: Autoencoders meet collaborative filtering. WWW'15. (paper)
• Collaborative deep learning for recommender systems. KDD'15. (paper)
• Probabilistic matrix factorization with non-random missing data. ICML'14. (paper)
• FISM: Factored Item Similarity Models for Top-N Recommender Systems. KDD'13. (paper)
• BPR: Bayesian personalized ranking from implicit feedback. UAI'09. (paper)
• Matrix factorization techniques for recommender systems. Computer'09. (paper)
• Probabilistic matrix factorization. NIPS'08. (paper)
• Restricted Boltzmann machines for collaborative filtering. ICML'07. (paper)
• Amazon.com recommendations: Item-to-item collaborative filtering. IEEE INTERNET COMPUT'03. (paper)
• Item-based collaborative filtering recommendation algorithms. WWW'01. (paper)

Social Recommendation

• A Modular Adversarial Approach to Social Recommendation. CIKM'2019. (paper)(code)
• Deep Adversarial Social Recommendation. IJCAI'19. (paper)
• Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction. KDD'19. (paper)(code)
• A Neural Influence Diffusion Model for Social Recommendation. SIGIR'19. (paper)(code)
• An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation. (paper)
• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW'19. (paper)
• Session-based Social Recommendation via Dynamic Graph Attention Networks. WSDM'19. (paper)(code)
• Social Attentional Memory Network: Modeling Aspect- and Friend-level Diferences in Recommendation. WSDM'19. (paper)
• Graph Neural Networks for Social Recommendation. WWW'19. (paper)
• Attentive Recurrent Social Recommendation. SIGIR'19. (paper)
• Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. AAAI'18. (paper)
• SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. AAAI'18. (paper)
• Learning and Transferring Social and Item Visibilities for Personalized Recommendation. CIKM'17. (paper)
• Learning to Rank with Trust and Distrust in Recommender Systems. Recsys'17. (paper)
• Collaborative User Network Embedding for Social Recommender Systems. SDM'17. (paper)
• Social recommendation with strong and weak ties. CIKM'16. (paper)
• Context-aware collaborative topic regression with social matrix factorization for recommender systems. AAAI'14. (paper)
• Leveraging social connections to improve personalized ranking for collaborative filtering. CIKM'14. (paper)
• Social collaborative filtering for cold-start recommendations. RecSys'14. (paper)
• Social collaborative filtering by trust. IJCAI'13. (paper)
• Recommender systems with social regularization. WSDM'11. (paper)
• A matrix factorization technique with trust propagation for recommendation in social networks. RecSys'10. (paper)
• Trustwalker: a random walk model for combining trust-based and item-based recommendation. KDD'09. (paper)
• Learning to recommend with social trust ensemble. SIGIR'09. (paper)
• Learning to recommend with trust and distrust relationships. RecSys'09. (paper)
• Sorec: social recommendation using probabilistic matrix factorization. CIKM'08. (paper)

Sequential Recommendation

• A Dynamic Co-attention Network for Session-based Recommendation. CIKM'2019. (paper)
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. CIKM'2019. (paper)(code)
• Hierarchical Gating Networks for Sequential Recommendation. KDD2019. (paper)(code)
• Hierarchical Context enabled Recurrent Neural Network for Recommendation. AAAI2019. (paper)(code)
• Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction. SIGIR'19. (paper)(code)
• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems. WWW'19. (paper)
• A Simple Convolutional Generative Network for Next Item Recommendation. WSDM'19. (paper)(code)
• Sequential Variational Autoencoders for Collaborative Filtering. WSDM'19. (paper)
• Session-based Recommendation with Graph Neural Networks. AAAI'19. (paper)(code)
• Self-Attentive Sequential Recommendation. ICDM'18. (paper)(code)
• Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. WSDM'18. (paper)(code)
• Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM'18. (paper)
• Sequential Recommendation with User Memory Networks. WSDM'18. (paper)
• STAMP: Short-Term A ention/Memory Priority Model for Session-based Recommendation. KDD'18. (paper)(code)
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. CIKM'18. (paper)
• Translation-based recommendation. RecSys'17. (paper)(code)
• Neural Attentive Session-based Recommendation. CIKM'17. (paper)
• Neural Survival Recommender. WSDM'17. (paper)
• Recurrent recommender networks. WSDM'17. (paper)
• Improved Recurrent Neural Networks for Session-based Recommendations. arxiv'16. (paper)
• Session-based Recommendations with Recurrent Neural Networks. ICLR'16. (paper)(code-Theano, code-TensorFlow)
• Fusing similarity models with markov chains for sparse sequential recommendation. ICDM'16. (paper)
• Dynamic Poisson Factorization. RecSys'15. (paper)
• Factorizing personalized markov chains for next-basket recommendation. WWW'10. (paper)
• Temporal collaborative filtering with bayesian probabilistic tensor factorization. SIAM'10. (paper)
• Collaborative Filtering with Temporal Dynamics. KDD'09. (paper)

Feature-based Recommendation (CTR Prediction)

• Recommending What Video to Watch Next: A Multitask Ranking System. RecSys'19. (paper)
• AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks. CIKM'19. (paper)(code)
• Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction. KDD'19. (paper)
• Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. WWW'19. (paper)
• Interaction-aware Factorization Machines for Recommender Systems. AAAI'19. (paper)
• Deep Session Interest Network for Click-Through Rate Prediction. IJCAI'19. (paper)(code)
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. KDD'18. (paper)(code)
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation. WWW'18. (paper)
• Deepfm: A factorization-machine based neural network for CTR prediction. IJCAI'17. (paper)(code)
• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. IJCAI. (paper)(code)
• Neural Factorization Machines for Sparse Predictive Analytics. SIGIR'17. (paper)(code)
• Deep & Cross Network for Ad Click Predictions. arxiv'17. (paper)
• Product-based neural networks for user response prediction. ICDM'16. (paper)
• Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features. KDD'16. (paper)
• Field-aware factorization machines for CTR prediction. RecSys'16. (paper)
• Deep learning over multi-field categorical data. ECIR'16. (paper)
• Wide & Deep Learning for Recommender Systems. arxiv'16. (paper)

Knowledge Graph-based Recommendation

• Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning. arXiv'19. (paper)
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. SIGIR'19. (paper)
• Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems. TOIS'19. (paper)
• Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization. KDD'19. (paper)(code)
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference. WWW'19. (paper)
• Jointly Learning Explainable Rules for Recommendation with Knowledge Graph. WWW'19. (paper)
• Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. WWW'19. (paper)
• Explainable Reasoning over Knowledge Graph Paths for Recommendation. AAAI'19. (paper)
• Heterogeneous Information Network Embedding for Recommendation. TKDE'18. (paper)(code)
• Leveraging Meta-path based Context for Top-N Recommendation with A Neural Co-Attention Model. KDD'18. (paper)(code)
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems. CIKM'18. (paper)(code)
• DKN: Deep Knowledge-Aware Network for News Recommendation. WWW'18. (paper)
• SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction. WSDM'18. (paper)
• Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. Arxiv'18. (paper)
• Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. KDD'17. (paper)(code)
• Collaborative Knowledge Base Embedding for Recommender Systems. KDD'16. (paper)
• Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach.RecSys'16. (paper)
• Personalized Entity Recommendation: A Heterogeneous Information Network Approach. WSDM'14. (paper)
• PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks.VLDB'11.(paper)

Reinforcement Learning for Recommendation

• Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning. arXiv'19. (paper)
• Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. SIGIR'19. (paper)
• Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology. IJCAI'19. (paper)
• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. ICML'19. (paper)
• Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems. arxiv'19. (paper)(code)
• Top-K Off-Policy Correction for a REINFORCE Recommender System. WSDM'19. (paper)
• Deep Reinforcement Learning for Page-wise Recommendations. RecSys'18. (paper)
• DRN: A Deep Reinforcement Learning Framework for News Recommendation. KDD'18. (paper)
• Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning. KDD'18. (paper)
• Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation. KDD'18. (paper)
• Reinforcement Learning to Rank with Markov Decision Process. SIGIR'17. (paper)
• Deep Reinforcement Learning for List-wise Recommendations. arxiv'17. (paper)
• Factored MDPs for Detecting Topics of User Sessions. RecSys'14. (paper)
• Optimal Radio Channel Recommendations with Explicit and Implicit Feedback. RecSys'12. (paper)
• Improving recommender systems with adaptive conversational strategies. HT'09. (paper)
• A hybrid web recommender system based on q-learning. SAC'08. (paper)
• Usage-based web recommendations: a reinforcement learning approach. RecSys'07. (paper)
• An MDP-Based Recommender System. JMLR'05. (paper)

POI Recommendation

• Spatiotemporal Representation Learning for Translation-Based POI Recommendation. TOIS'19. (paper)
• Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation. KDD'19. (paper)
• Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. IJCAI'18. (paper)
• Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. IJCAI'18. (paper)
• A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. SIGIR'18. (paper)
• A location-sentiment-aware recommender system for both hometown and out-of-town users. KDD'17. (paper)
• What your images reveal: Exploiting visual contents for point-of-interest recommendation. WWW'17. (paper)
• POI2Vec: Geographical Latent Representation for Predicting Future Visitors. AAAI'17. (paper)
• Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking. IJCAI'17. (paper)
• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. AAAI'16. (paper)(code)
• Point-of-interest recommendations: Learning potential check-ins from friends. KDD'16. (paper)
• Gmove: Group-level mobility modeling using geo-tagged social media. KDD'16. (paper)
• Learning graph-based poi embedding for location-based recommendation. CIKM'16. (paper)
• Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. WWW'16. (paper)
• GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. SIGIR'15. (paper)
• GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. KDD'14. (paper)
• Exploiting geographical neighborhood characteristics for location recommendation. CIKM'14. (paper)
• Nlpmm: A next location predictor with markov modeling. PAKDD'14. (paper)
• Lore: Exploiting sequential influence for location recommendations. SIGSPATIAL’14. (paper)
• Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks. RecSys'13. (paper)
• Where You Like to Go Next: Successive Point-of-Interest Recommendation. AAAI'13. (paper)
• Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. AAAI'12. (paper)
• Friendship and mobility: user movement in location-based social networks. KDD'11. (paper)

Cold-Start Recommendation

• From Zero-Shot Learning to Cold-Start Recommendation. AAAI'2019. (paper)(code)
• MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. KDD'2019. (paper)(code)
• DropoutNet: Addressing Cold Start in Recommender Systems. NeurIPS'2019. (paper)(code)

Security/Robutness of RecSys

• Data Poisoning Attacks on Neighborhood-based Recommender Systems. arxiv'19. (paper)
• Adversarial recommendation attack of the learned fake users. arxiv'18. (paper)
• Poisoning Attacks to Graph-Based Recommender Systems. arxiv'18. (paper)
• Adversarial Personalized Ranking for Recommendation. SIGIR'18. (paper)
• Adversarial Collaborative Auto-encoder for Top-N Recommendation. arxiv'18. (paper)
• Fake Co-visitation Injection Attacks to Recommender Systems. NDSS'17. (paper)
• Data Poisoning Attacks on Factorization-Based Collaborative Filtering. NIPS'16. (paper)

Causal Recommendation

• Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning. arxiv'2020. (paper)
• The Blessings of Multiple Causes. JASA'19. (paper)
• Observational Data for Heterogeneous Treatment Effects with Application to Recommender Systems. EC'19. (paper)
• The Deconfounded Recommender: A Causal Inference Approach to Recommendation. arxiv'19. (paper)
• Causal Embeddings for Recommendation: An Extended Abstract. IJCAI'2019. (paper)
• Causal Embeddings for Recommendation. RecSys'18. (paper)
• Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. AAAI'2018. (paper)
• Modeling User Exposure in Recommendation. WWW'16. (paper)
• Causal Inference for Recommendation. UAI Workshop'16. (paper)
• Recommendations as Treatments: Debiasing Learning and Evaluation. ICML'16. (paper)
• Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization. JMLR'15. (paper)
• Estimating the Causal Impact of Recommendation Systems from Observational Data. EC'15. (paper)
• Consistence beats causality in recommender systems. arxiv'15. (paper)