awesome-RecSys-papers

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

My email is [email protected]. 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]

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]

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]

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]

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]

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

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]

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 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]