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• 1.Collaborative Filtering based Recommendation
  • 1.1 Neighborhood-based CF
  • 1.2 Model-based CF
    • 1.2.1 Matrix Factorization
    • 1.2.2 Distance-based CF
      • 1.2.2.1 Euclidean Embedding
      • 1.2.2.2 Metric Learning
• 2.Content-based Recommendation
  • 2.1 Review-based Recommendation
• 3.Knowledge Graph based Recommendation
• 4.Hybrid Recommendation
• 5.Deep Learning based Recommendation
  • 5.1 Multi-layer Perceptron (MLP)
  • 5.2 Autoencoders (AE)
  • 5.3 Convolutional Neural Networks (CNNs)
• 6.Click-Through Rate (CTR) Prediction
• 7.Learning to Rank (LTR)
  • 7.1 Pairwise LTR
  • 7.2 Listwise LTR
• 8.Graph-based Recommendation
  • 8.1 Heterogeneous Information Networks
  • 8.2 Graph Neural Networks
• 9.Social Recommendation
• 10.Cross-domain Recommendation
• 11.Group Recommendation
• 12.Cold Start Recommendation
• 13.Point-of-Interest (POI) Recommendation
• 14.Context-aware Recommendation
• 15.Sequential Recommendation
• 16.Explainable Recommendation
• 17.Conversational Recommendation
• 18.Others
  • 18.1 Evaluation Metrics
  • 18.2 Network Embedding
  • 18.3 Survey on Recommender Systems

• [1992 CACM] Using collaborative filtering to weave an information Tapestry. [PDF]

Collaborative Filtering (CF) was firstly proposed.

• [1994 CSCW] UserCF: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. [PDF]
• [1995 CHI] Social information filtering: algorithms for automating “word of mouth”. [PDF]
• [2001 WWW] ItemCF: Item-Based Collaborative Filtering Recommendation Algorithms. [PDF]
• [2003] Amazon.com recommendations: item-to-item collaborative filtering. [PDF]
• [2004 TOIS] ItemKNN: Item-Based Top-N Recommendation Algorithms. [PDF]
• [2005] Slope One: Slope One Predictors for Online Rating-Based Collaborative Filtering. [PDF]
• [2011 ICDM] Slim: Sparse linear methods for top-n recommender systems. [PDF]
• [2013 KDD] FISM: Factored Item Similarity Models for Top-N Recommender Systems. [PDF]
• [2014 PAKDD] HOSLIM: Higher-Order Sparse LInear Method for Top-N Recommender Systems. [PDF]
• [2016 RecSys] GLSLIM: Local Item-Item Models for Top-N Recommendation. [PDF]

• [1999 IJCAI] Latent Class Models for Collaborative Filtering. [PDF]
• [2004 TOIS] pLSA: Latent Semantic Models for Collaborative Filtering. [PDF]

• [1998 ICML] SVD: Learning Collaborative Information Filters. [PDF]
• [2004 NIPS] MMMF: Maximum-Margin Matrix Factorization. [PDF]
• [2005 ICML] Fast Maximum Margin Matrix Factorization for Collaborative Prediction. [PDF]
• [2006] FunkSVD (Latent Factor Model, LFM): Netflix Update: Try This at Home. http://sifter.org/~simon/journal/20061211.html
• [2007 KDD] NSVD: Improving regularized singular value decomposition for collaborative filtering. [PDF]
• [2007 NIPS] PMF: Probabilistic matrix factorization. [PDF]
• [2008 ICML] BPMF: Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo. [PDF]
• [2008 KDD] SVD++: Factorization meets the neighborhood: a multifaceted collaborative filtering model. [PDF]
• [2008 ICDM] One-Class Collaborative Filtering. [PDF]
• [2008 ICDM] WRMF: Collaborative Filtering for Implicit Feedback Datasets. [PDF]
• [2009 KDD] timeSVD: Collaborative filtering with temporal dynamics. [PDF]
• [2009] BiasedMF: Matrix Factorization Techniques for Recommender Systems. [PDF]
• [2010] PureSVD: Performance of Recommender Algorithms on Top-N Recommendation Tasks. [PDF]
• [2011 arXiv] Feature-Based Matrix Factorization. [PDF]
• [2012 JMLR] SVDFeature: A Toolkit for Feature-based Collaborative Filtering. [PDF]
• [2014 NIPS] Logistic MF: Logistic Matrix Factorization for Implicit Feedback Data. [PDF] [Codes]
• [2016 SIGIR] eALS: Fast Matrix Factorization for Online Recommendation with Implicit Feedback. [PDF] [Codes]
• [2020 RecSys] Neural Collaborative Filtering vs. Matrix Factorization Revisited. [PDF] [Codes]

• [2010 RecSys] EE: Collaborative Filtering via Euclidean Embedding. [PDF]
• [2012 APWeb] TEE: Collaborative Filtering via Temporal Euclidean Embedding. [PDF]

• [2017 WWW] CML: Collaborative Metric Learning. [PDF]
• [2018 WWW] LRML: Latent relational metric learning via memory-based attention for collaborative ranking. [PDF]
• [2018 arXiv] MetricF: Metric Factorization: Recommendation beyond Matrix Factorization. [PDF]
• [2018 ICDM] TransCF: Collaborative Translational Metric Learning. [PDF]
• [2020 AAAI] SML: Symmetric Metric Learning with Adaptive Margin for Recommendation. [PDF]
• [2020 KDD] PMLAM: Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation. [PDF]

• [2007] Content-based Recommendation Systems. [PDF]

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

• [2016] Collaborative Knowledge Base Embedding for Recommender Systems. [PDF]
• [2019 WWW] KGCN: Knowledge Graph Convolutional Networks for Recommender Systems. [PDF] [Codes]
• [2019 KDD] KGNN-LS: Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. [PDF] [Codes]
• [2019 KDD] KGAT: Knowledge Graph Attention Network for Recommendation. [PDF]

• [2017 CIKM] JRL: Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources. [PDF]

• [2007] RBM: Restricted Boltzmann Machines for Collaborative Filtering. [PDF]
• [2018 SIGIR] CMN: Collaborative Memory Network for Recommendation Systems. [PDF]

• [2015 arXiv] NNMF: Neural Network Matrix Factorization. [PDF]
• [2016] Deep Neural Networks for YouTube Recommendations. [PDF]
• [2016 RecSys] Wide & Deep Learning for Recommender Systems. [PDF]
• [2017 WWW] NCF: Neural Collaborative Filtering. [PDF] [Codes]
• [2017 IJCAI] DMF: Deep Matrix Factorization Models for Recommender Systems. [PDF]
• [2017 CIKM] NNCF: A Neural Collaborative Filtering Model with Interaction-based Neighborhood. [PDF]
• [2018 IJCAI] DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation. [PDF]
• [2018 TKDE] NAIS: Neural Attentive Item Similarity Model for Recommendation. [PDF]
• [2019 AAAI] DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System. [PDF] [Codes]
• [2019 TOIS] DeepICF: Deep Item-based Collaborative Filtering for Top-N Recommendation. [PDF] [Codes]
• [2019 TOIS] J-NCF: Joint Neural Collaborative Filtering for Recommender Systems. [PDF]

• [2015] AutoRec: Autoencoders Meet Collaborative Filtering. [PDF]
• [2015] CDL: Collaborative Deep Learning for Recommender Systems. [PDF]
• [2016 WSDM] CDAE: Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. [PDF]
• [2017 KDD] CVAE: Collaborative Variational Autoencoder for Recommender Systems. [PDF]
• [2018 WWW] Mult-VAE: Variational Autoencoders for Collaborative Filtering. [PDF] [Codes]

• [2018 IJCAI] ONCF: Outer Product-based Neural Collaborative Filtering. [PDF]

• [2016 RecSys] FFM: Field-aware Factorization Machines for CTR Prediction. [PDF]

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

• [2018 KDD] DIN: Deep Interest Network for Click-Through Rate Prediction. [PDF]

• [2019 AAAI] DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction. [PDF]

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

• [2008] EigenRank: A Ranking-Oriented Approach to Collaborative Filtering. [PDF]
• [2009 UAI] BPR: Bayesian Personalized Ranking from Implicit Feedback. [PDF]
• [2012] Collaborative Ranking. [PDF]
• [2012 JMLR] RankSGD: Collaborative Filtering Ensemble for Ranking. [PDF]
• [2012 RecSys] RankALS: Alternating Least Squares for Personalized Ranking. [PDF]
• [2013 SDM] CoFiSet: Collaborative Filtering via Learning Pairwise Preferences over Item-sets. [PDF]
• [2014 WSDM] Improving Pairwise Learning for Item Recommendation from Implicit Feedback. [PDF]
• [2014] LCR: Local Collaborative Ranking. [PDF]
• [2014] VSRank: A Novel Framework for Ranking-Based Collaborative Filtering. [PDF]
• [2017 KDD] Large-scale Collaborative Ranking in Near-Linear Time. [PDF]
• [2018] CPLR: Collaborative pairwise learning to rank for personalized recommendation. [PDF]

• [2007] COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking. [PDF] [Codes]

• [2010] ListRankMF: List-wise Learning to Rank with Matrix Factorization for Collaborative Filtering. [PDF]

• [2012] CLiMF: Learning to Maximize Reciprocal Rank with Collaborative Less-is-More Filtering. [PDF]

• [2012] TFMAP: Optimizing MAP for Top-N Context-aware Recommendation. [PDF]

• [2015] Collaborative Ranking with a Push at the Top. [PDF]

• [2015] ListCF: Listwise Collaborative Filtering. [PDF]

• [2016] Ranking-Oriented Collaborative Filtering: A Listwise Approach. [PDF]

• [2018] SQL-Rank: A Listwise Approach to Collaborative Ranking. [PDF]

• [2020 AAAI] SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback. [PDF]

• [2003 TKDE] Personalized PageRank: Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. [PDF]
• [2007 IJCAI] ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. [PDF]
• [2012 CIKM] PathRank: A Novel Node Ranking Measure on a Heterogeneous Graph for Recommender Systems. [PDF]
• [2013 ESWA] PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems. [PDF]
• [2015 NIPS] Collaborative Filtering with Graph Information: Consistency and Scalable Methods. [PDF]

• [2013 RecSys] Recommendation in heterogeneous information networks with implicit user feedback. [PDF]

• [2014 WWW] Random Walks in Recommender Systems: Exact Computation and Simulations. [PDF]

• [2014 WSDM] HeteRec: Personalized Entity Recommendation: A Heterogeneous Information Network Approach. [PDF]

• [2016 TKDE] HeteRS: A General Recommendation Model for Heterogeneous Networks. [PDF]

• [2017 KDD] FMG: Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. [PDF] [Codes]

• [2018 WSDM] HeteLearn: Recommendation in Heterogeneous Information Networks Based on Generalized Random Walk Model and Bayesian Personalized Ranking. [PDF]

• [2018 TKDE] HERec: Heterogeneous information network embedding for recommendation. [PDF]

• [2019 IJCAI] HueRec: Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation. [PDF]

Rating Prediction

• [2018 KDD] GCMC: Graph Convolutional Matrix Completion. [PDF]
• [2019 IJCAI] STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. [PDF] [Codes]
• [2020 ICLR] IGMC: Inductive Matrix Completion Based on Graph Neural Networks. [PDF] [Codes]

Top-N Recommendation

• [2018 KDD] PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems. [PDF]
• [2018 RecSys] SpectralCF: Spectral Collaborative Filtering. [PDF] [Codes]
• [2019 SIGIR] NGCF: Neural Graph Collaborative Filtering. [PDF]
• [2019 ICDM] Multi-GCCF: Multi-Graph Convolution Collaborative Filtering. [PDF]
• [2020 AAAI] LR-GCCF: Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. [PDF] [Codes]
• [2020 SIGIR] LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. [PDF] [Codes]
• [2021 WWW] IMP-GCN: Interest-aware Message-Passing GCN for Recommendation. [PDF] [Codes]

• [2020 AAAI] MCCF: Multi-Component Graph Convolutional Collaborative Filtering. [PDF]

• [2020 SIGIR] DGCF: Disentangled Graph Collaborative Filtering. [PDF] [Codes]

• [2008] SoRec: Social Recommendation Using Probabilistic Matrix Factorization. [PDF]
• [2009] RSTE: Learning to Recommend with Social Trust Ensemble. [PDF]
• [2010] SocialMF: A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks. [PDF]
• [2011] SoReg: Recommender systems with social regularization. [PDF]
• [2013 IJCAI] LOCABAL: Exploiting Local and Global Social Context for Recommendation. [PDF]
• [2013 IJCAI] TrustMF: Social Collaborative Filtering by Trust. [PDF]
• [2015] TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings. [PDF]
• [2018 AAAI] SERec: Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation. [PDF]
• [2019 WWW] GraphRec: Graph Neural Networks for Social Recommendation. [PDF]
• [2019 SIGIR] DiffNet: A neural influence diffusion model for social recommendation. [PDF]
• [2021 TKDE] DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. [PDF]

• [2008] If You Like the Devil Wears Prada the Book, Will You also Enjoy the Devil Wears Prada the Movie? A Study of Cross-Domain Recommendations. [PDF]
• [2011 RecSys] A Generic Semantic-based Framework for Cross-domain Recommendation. [PDF]

• [2009 VLDB] Group Recommendation: Semantics and Efficiency. [PDF]
• [2018 SIGIR] AGREE: Attentive Group Recommendation. [PDF] [Codes] [Slides]

• [2002 SIGIR] Methods and Metrics for Cold-Start Recommendations. [PDF]
• [2008 ICUIMC] Addressing Cold-Start Problem in Recommendation Systems. [PDF]
• [2009 RecSys] Pairwise Preference Regression for Cold-start Recommendation. [PDF]
• [2011 SIGIR] Functional Matrix Factorizations for Cold-Start Recommendation. [PDF]
• [2014 RecSys] Social Collaborative Filtering for Cold-start Recommendations. [PDF]
• [2014 RecSys] Item Cold-Start Recommendations: Learning Local Collective Embeddings. [PDF]

• [2010 RecSys] Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering. [PDF]
• [2011 RecSys] Matrix Factorization Techniques for Context Aware Recommendation. [PDF]
• [2016 RecSys] Convolutional Matrix Factorization for Document Context-Aware Recommendation. [PDF] [Codes]

• [2011 SIGIR] Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. [PDF]
• [2013 SIGIR] Time-aware Point-of-interest Recommendation. [PDF] [Datasets]
• [2013 KDD] Learning Geographical Preferences for Point-of-Interest Recommendation. [PDF] [Slides]
• [2013 CIKM] Personalized Point-of-Interest Recommendation by Mining Users’ Preference Transition. [PDF]
• [2014 KDD] GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation. [PDF]
• [2014 CIKM] Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences. [PDF] [Datasets]
• [2015 SIGIR] Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation. [PDF] [Datasets]
• [2016 KDD] Point-of-Interest Recommendations: Learning Potential Check-ins from Friends. [PDF]

aka. Next-item Recommendation or Next-basket Recommendation

• [2017 RecSys] TransRec: Translation-based Recommendation. [PDF]
• [2018 WSDM] Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. [PDF]
• [2018 ICDM] SASRec: Self-Attentive Sequential Recommendation. [PDF]

• [2014 SIGIR] EFM: Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. [PDF]

• [2013 CIKM] Interactive Collaborative Filtering. [PDF]
• [2018 SIGIR] Conversational Recommender System. [PDF]

• [2021 WSDM] Denoising Implicit Feedback for Recommendation. [PDF]

• [2004 TOIS] Evaluating Collaborative Filtering Recommender Systems. [PDF]
• [2020 KDD Best paper] On Sampled Metrics for Item Recommendation. [PDF]
• [2020 KDD] On Sampling Top-K Recommendation Evaluation. [PDF]
• [2021 AAAI] On Estimating Recommendation Evaluation Metrics under Sampling. [PDF]

• [2014] DeepWalk: Online Learning of Social Representations. [PDF]
• [2016] Item2Vec: Neural Item Embedding for Collaborative Filtering. [PDF]
• [2016] node2vec: Scalable Feature Learning for Networks. [PDF]
• [2017 KDD] metapath2vec: Scalable Representation Learning for Heterogeneous Networks. [PDF]
• [2018] BiNE: Bipartite Network Embedding. [PDF]
• [2020] A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources. [PDF]

• [2002] Hybrid Recommender Systems: Survey and Experiments. [PDF]
• [2005 TKDE] Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. [PDF]
• [2009] A Survey of Collaborative Filtering Techniques. [PDF]
• [2012] Context-aware Recommender Systems for Learning: a Survey and Future Challenges. [PDF]
• [2012] Cross-domain recommender systems: A survey of the State of the Art. [PDF]
• [2013] A survey of collaborative filtering based social recommender systems. [PDF]
• [2013 KBS] Recommender systems survey. [PDF]
• [2014] Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. [PDF]
• [2014] Group Recommendations: Survey and Perspectives. [PDF]
• [2014] Active Learning in Collaborative Filtering Recommender Systems. [PDF]
• [2015] Survey on Learning-to-Rank Based Recommendation Algorithms. [PDF]
• [2016] A Survey of Point-of-interest Recommendation in Location-based Social Networks. [PDF]
• [2018] Explainable Recommendation: A Survey and New Perspectives. [PDF]
• [2018] Deep Learning based Recommender System: A Survey and New Perspectives. [PDF]
• [2019] A Survey on Session-based Recommender Systems. [PDF]
• [2019] Research Commentary on Recommendations with Side Information: A Survey and Research Directions. [PDF]
• [2020] A Survey on Knowledge Graph-Based Recommender Systems. [PDF]
• [2020] Survey of Privacy-Preserving Collaborative Filtering. [PDF]
• [2020] Deep Learning on Knowledge Graph for Recommender System: A Survey. [PDF]
• [2020] A Survey on Conversational Recommender Systems. [PDF]
• [2020] Graph Neural Networks in Recommender Systems: A Survey. [PDF]
• [2021 TKDE] Bias and Debias in Recommender System: A Survey and Future Directions. [PDF]