The topic of my dissertation is recommendation system. I collected some classic and awesome papers here. Good luck to every RecSys-learner.

Awesome Recommendation System Papers

My email is ZhangYuyang4d@163.com. If you find any mistakes, or you have some suggestions, just send a email to me.

By the way, the RecSys is one of the most important conference in recommendation.

Deep Learning and Recommendations

• Restricted Boltzmann Machines for Collaborative Filtering (2007),R Salakhutdinov, A Mnih, G Hinton. [pdf]

• A Hybrid Approach with Collaborative Filtering for Recommender Systems (2013), G Badaro, H Hajj, et al. [pdf]

• AutoRec- Autoencoders Meet Collaborative Filtering (2015), Suvash Sedhain, Aditya Krishna Menon, et al. [pdf]

• Collaborative Deep Learning for Recommender Systems (2015), Hao Wang, N Wang, Dityan Yeung. [pdf]

• Deep Neural Networks for YouTube Recommendations (2016), Paul Covington, Jay Adams, Emre Sargin. [pdf]

• Deep content-based music recommendation (2013), A Van den Oord, S Dieleman. [pdf]

• Hybrid Collaborative Filtering with Autoencoders (2016), F Strub, J Mary, R Gaudel. [pdf]

• Wide & Deep Learning for Recommender Systems (2016),HT Cheng, L Koc, J Harmsen, T Shaked. [pdf]

• A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017),Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang.[pdf]

• Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.[pdf]

• Recurrent Recommender Networks (2017), Chao-Yuan Wu.[pdf]

Matrix Factorization

• SVD-based collaborative filtering with privacy (2005), Polat H, Du W. [pdf]

• Feature-Based Matrix Factorization (2011), T Chen, Z Zheng, Q Lu, W Zhang, Y Yu. [pdf]

• F2M Scalable Field-Aware Factorization Machines (2016),C Ma, Y Liao, Y Wang, Z Xiao. [pdf]

• Factorization Machines with libFM (2012),S Rendle. [pdf]

• Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence (2016), D Liang, J Altosaar, L Charlin, DM Blei. [pdf]

Click-Through-Rate(CTR) Prediction

• Predicting Clicks Estimating the click-through rate for new ads (2007),M Richardson, E Dominowska. [pdf]

• Click-Through Rate Estimation for Rare Events in Online Advertising (2010),X Wang, W Li, Y Cui, R Zhang. [pdf]

• Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine (2010), T Graepel, JQ Candela. [pdf]

• Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction (2012), M Jahrer, A Toscher, JY Lee, J Deng [pdf]

• A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 (2012),KW Wu, CS Ferng, CH Ho, AC Liang, CH Huang. [pdf]

• Combining Factorization Model and Additive Forest for Collaborative Followee Recommendation (2012), T Chen, L Tang, Q Liu, D Yang, S Xie, X Cao, C Wu. [pdf]

• Practical Lessons from Predicting Clicks on Ads at Facebook(2014), X He, J Pan, O Jin, T Xu, B Liu, T Xu, Y Shi. [pdf]

• Simple and scalable response prediction for display advertising (2015),O Chapelle, E Manavoglu, R Rosales. [pdf]

Recommendations

Survey Review

• Toward the next generation of recommender systems：A survey of the state-of-the-art and possiblie extensions (2005), Adomavicius G, Tuzhilin A. [pdf]

• (BOOK)Recommender systems: an introduction (2011), Zanker M, Felfernig A, Friedrich G. [pdf]

Collaborative Filtering Recommendations

• Recommender system (1997), P Resnick, HR Varian. [pdf]

• Empirical analysis of predictive algorithms for collaborative filtering (1998), John S Breese, David Heckerman, Carl M Kadie. [pdf]

• Clustering methods for collaborative filtering (1998), Ungar, L. H., D. P. Foster. [pdf]

• A bayesian model for collaborative filtering (1999),Chien Y H, George E I. [pdf]

• Using probabilistic relational models for collaborative filtering (1999), Lise Getoor, Mehran Sahami [pdf]

• Item-based Collaborative Filtering Recommendation Algorithms (2001), Badrul M Sarwar, George Karypis, Joseph A Konstan, John Riedl. [pdf]

• Amazon Recommendations Item-to-Item Collaborative Filtering (2003), G Linden, B Smith, et al. [pdf]

• A maximum entropy approach for collaborative filtering (2004), Browning J, Miller D J. [pdf]

• Improving regularized singular value decomposition for collaborative filtering (2007), A Paterek. [pdf]

• Factorization Meets the Neighborhood- a Multifaceted Collaborative Filtering Model (2008),Y Koren. [pdf]

• Factor in the Neighbors- Scalable and Accurate Collaborative Filtering (2010), Y Koren. [pdf]

Content-based Recommendations

• Utility-based repair of inconsistent requirements (2009), Felfernig A, Mairitsch M, Mandl M, et al. [pdf]

Probability Graph Model and Byesian Inference

• Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (2008),R Salakhutdinov, et al. [pdf]

• Bayesian Personalized Ranking from Implicit Feedback (2009), S Rendle, C Freudenthaler, Z Gantner. [pdf]

Other methods for Recommendations

• Supporting user query relaxation in a recommender system (2004),Mirzadeh N, Ricci F, Bansal M. [pdf]

• Case-based recommender systems: a unifying view.Intelligent Techniques for Web Personalization (2005),Lorenzi F, Ricci F. [pdf]

• Fast computation of query relaxations for knowledge-based recommenders (2009),Jannach D. [pdf]

• Tag-aware recommender systems: a state-of-the-art survey (2011), Zhang Z K, Zhou T, Zhang Y C. [pdf]

Hybrid Recommendations

• Hybrid recommender systems: Survey and experiments (2002), Burke R. [pdf]
• A hybrid approach to item recommendation in folksonomies (2009), Wetzker R, Umbrath W, Said A. [pdf]

原文：https://github.com/YuyangZhangFTD/awesome-RecSys-papers