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 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.

RecSys

Lastest Paper

I will continue to update this section for a while till I finish my dissertation. Maybe some papers of this section can't be downloaded, please email me and I will send the pdf to you.

Email again: ZhangYuyang4d@163.com

ICLR 2017

Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.
Exponential Machines (2017), Alexander Novikov, Mikhail Trofimov, Ivan Oseledets.
Recurrent Recommender Networks (2017), Chao-Yuan Wu.

AAAI 2017

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

Recent Papers

The papers published in recent years are collected here. The deep learning are widely used in recommendations system in recent years. And I use the same method in my dissertation. That's why I put these papers ahead. I also did some research about the ctr prediction, which is the main direction of my work in the future.

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]

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]

waveletbob / awesome-RecSys-papers