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.
I graduated three months ago, and this list has not been updated for three months. And now I have become a machine learning engineer in Suning, majoring in recommendation system and other machine learning fields. So I decide to continue to update this list in the future. But I will change the format for convenience.
I will record the papers here which I have read, and the I will update the list once a week. Some papers can't be downloaded if you don't have access for some digital library, like acm digital library and so on. So if you want to read these papers, email me~ ZhangYuyang4d@163.com
By the way, I won't classify papers into several categories, and just list their names and download links here. The papers with * is those that I just look through without too much attention for I'm a too busy now to give my whole attention to read each paper.
Gook luck to every rec-sys learner.
- Mnih, Andriy, and Yee Whye Teh. "Learning Item Trees for Probabilistic Modelling of Implicit Feedback." arXiv preprint arXiv:1109.5894 (2011).[pdf]
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- Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 2008.[pdf]
*Koren, Yehuda, and Joseph Sill. "Collaborative Filtering on Ordinal User Feedback." IJCAI. 2013.[pdf]*Ferreira, Kris Johnson, Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an online retailer: Demand forecasting and price optimization." Manufacturing & Service Operations Management 18.1 (2015): 69-88.[pdf]- Davidson, James, et al. "The YouTube video recommendation system." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.[pdf]
- Mnih, Andriy, and Yee Whye Teh. "Learning Item Trees for Probabilistic Modelling of Implicit Feedback." arXiv preprint arXiv:1109.5894 (2011).[pdf]
- Wang, Zizhuo. Dynamic Learning Mechanisms in Revenue Management Problems. Diss. Stanford University, 2012.[pdf]
- (Pre-Processings, nips2017) DropoutNet: Addressing Cold Start in Recommender Systems. [pdf]
- Gallego G, Van Ryzin G J. Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons[J]. Management Science, 1994, 40(8): 999-1020.[pdf]
- Davidson, James, et al. "The YouTube video recommendation system." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.[pdf]
- (Pre-Processings, nips2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree [pdf]
- (Pre-Processings, nips2017) A Meta-Learning Perspective on Cold-Start Recommendations for Items [pdf]
- Rendle S, Freudenthaler C. Improving pairwise learning for item recommendation from implicit feedback[C]// ACM International Conference on Web Search and Data Mining. ACM, 2014:273-282.[pdf]
- Zhou G, Song C, Zhu X, et al. Deep Interest Network for Click-Through Rate Prediction[J]. 2017.[pdf]
*Zhai, Shuangfei, et al. "Deepintent: Learning attentions for online advertising with recurrent neural networks." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.[pdf]
- Ning Y, Shi Y, Hong L, et al. A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation[C]// the Eleventh ACM Conference. ACM, 2017:23-31.[pdf]
- Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]// Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2009:452-461.[pdf]
- Zhang T, Zhang T, Zhang T, et al. Gradient boosting factorization machines[C]// ACM Conference on Recommender Systems. ACM, 2014:265-272.[pdf]
- He X, Chua T S. Neural Factorization Machines for Sparse Predictive Analytics[J]. 2017:355-364.[pdf]
- Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680.[pdf]
- Boyd S, Parikh N, Chu E, et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[J]. Foundations & Trends in Machine Learning, 2010, 3(1):1-122.[pdf]
- Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1) (2008)[pdf]
- Van den Oord A, Dieleman S, Schrauwen B. Deep content-based music recommendation[C]//Advances in neural information processing systems. 2013: 2643-2651.[pdf]
- Rendle, Steffen. "Factorization machines with libfm." ACM Transactions on Intelligent Systems and Technology (TIST) 3.3 (2012): 57.[pdf]
- Juan Y, Zhuang Y, Chin W S, et al. Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 43-50.[pdf]
- Rendle S, Schmidt-Thieme L. Pairwise interaction tensor factorization for personalized tag recommendation[C]//Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010: 81-90.[pdf]
- Blondel M, Fujino A, Ueda N, et al. Higher-order factorization machines[C]//Advances in Neural Information Processing Systems. 2016: 3351-3359.[pdf]
- Rendle S. Factorization machines with libfm[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3): 57.[pdf]
- Yin Lou, Mikhail Obukhov. BDT- Boosting Decision Tables for High Accuracy and Scoring Efficiency. KDD2017.[pdf]
- Ning Y, Shi Y, Hong L, et al. A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation[J]. 2017. [pdf]
- Qu Y, Cai H, Ren K, et al. Product-Based Neural Networks for User Response Prediction[C]// IEEE, International Conference on Data Mining. IEEE, 2017:1149-1154.[pdf]
- Zhang W, Du T, Wang J, et al. Deep Learning over Multi-field Categorical Data[C]. european conference on information retrieval, 2016: 45-57.
- Guo H, Tang R, Ye Y, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]// Twenty-Sixth International Joint Conference on Artificial Intelligence. 2017:1725-1731.[pdf]
- Xiao J, Ye H, He X, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks[J]. 2017.[pdf]
- Chen J, Sun B, Li H, et al. Deep ctr prediction in display advertising[C]//Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016: 811-820.[pdf]
- Shan Y, Hoens T R, Jiao J, et al. Deep Crossing: Web-scale modeling without manually crafted combinatorial features[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 255-262.[pdf]
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
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.
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Restricted Boltzmann Machines for Collaborative Filtering (2007),R Salakhutdinov, A Mnih, G Hinton. [pdf]
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A Hybrid Approach with Collaborative Filtering for Recommender Systems (2013), G Badaro, H Hajj, et al. [pdf]
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AutoRec- Autoencoders Meet Collaborative Filtering (2015), Suvash Sedhain, Aditya Krishna Menon, et al. [pdf]
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Collaborative Deep Learning for Recommender Systems (2015), Hao Wang, N Wang, Dityan Yeung. [pdf]
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Deep Neural Networks for YouTube Recommendations (2016), Paul Covington, Jay Adams, Emre Sargin. [pdf]
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Deep content-based music recommendation (2013), A Van den Oord, S Dieleman. [pdf]
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Hybrid Collaborative Filtering with Autoencoders (2016), F Strub, J Mary, R Gaudel. [pdf]
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Wide & Deep Learning for Recommender Systems (2016),HT Cheng, L Koc, J Harmsen, T Shaked. [pdf]
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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]
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Collaborative Deep Embedding via Dual Networks (2017), Yilei Xiong, Dahua Lin, et al.[pdf]
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Recurrent Recommender Networks (2017), Chao-Yuan Wu.[pdf]
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SVD-based collaborative filtering with privacy (2005), Polat H, Du W. [pdf]
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Feature-Based Matrix Factorization (2011), T Chen, Z Zheng, Q Lu, W Zhang, Y Yu. [pdf]
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F2M Scalable Field-Aware Factorization Machines (2016),C Ma, Y Liao, Y Wang, Z Xiao. [pdf]
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Factorization Machines with libFM (2012),S Rendle. [pdf]
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Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence (2016), D Liang, J Altosaar, L Charlin, DM Blei. [pdf]
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Predicting Clicks Estimating the click-through rate for new ads (2007),M Richardson, E Dominowska. [pdf]
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Click-Through Rate Estimation for Rare Events in Online Advertising (2010),X Wang, W Li, Y Cui, R Zhang. [pdf]
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Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine (2010), T Graepel, JQ Candela. [pdf]
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Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction (2012), M Jahrer, A Toscher, JY Lee, J Deng [pdf]
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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]
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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]
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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]
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Simple and scalable response prediction for display advertising (2015),O Chapelle, E Manavoglu, R Rosales. [pdf]
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Toward the next generation of recommender systems：A survey of the state-of-the-art and possiblie extensions (2005), Adomavicius G, Tuzhilin A. [pdf]
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(BOOK)Recommender systems: an introduction (2011), Zanker M, Felfernig A, Friedrich G. [pdf]
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Recommender system (1997), P Resnick, HR Varian. [pdf]
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Empirical analysis of predictive algorithms for collaborative filtering (1998), John S Breese, David Heckerman, Carl M Kadie. [pdf]
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Clustering methods for collaborative filtering (1998), Ungar, L. H., D. P. Foster. [pdf]
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A bayesian model for collaborative filtering (1999),Chien Y H, George E I. [pdf]
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Using probabilistic relational models for collaborative filtering (1999), Lise Getoor, Mehran Sahami [pdf]
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Item-based Collaborative Filtering Recommendation Algorithms (2001), Badrul M Sarwar, George Karypis, Joseph A Konstan, John Riedl. [pdf]
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Amazon Recommendations Item-to-Item Collaborative Filtering (2003), G Linden, B Smith, et al. [pdf]
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A maximum entropy approach for collaborative filtering (2004), Browning J, Miller D J. [pdf]
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Improving regularized singular value decomposition for collaborative filtering (2007), A Paterek. [pdf]
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Factorization Meets the Neighborhood- a Multifaceted Collaborative Filtering Model (2008),Y Koren. [pdf]
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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]
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Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo (2008),R Salakhutdinov, et al. [pdf]
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Bayesian Personalized Ranking from Implicit Feedback (2009), S Rendle, C Freudenthaler, Z Gantner. [pdf]
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Supporting user query relaxation in a recommender system (2004),Mirzadeh N, Ricci F, Bansal M. [pdf]
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Case-based recommender systems: a unifying view.Intelligent Techniques for Web Personalization (2005),Lorenzi F, Ricci F. [pdf]
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Fast computation of query relaxations for knowledge-based recommenders (2009),Jannach D. [pdf]
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Tag-aware recommender systems: a state-of-the-art survey (2011), Zhang Z K, Zhou T, Zhang Y C. [pdf]