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 [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.

RecSys

I graduated three months ago, and this list has not been updated for three months (from 2017.06.21). 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~ [email protected]

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.

Some papers about OR will be added in the future.

• Clearance Pricing Optimization for a Fast-Fashion Retailer[pdf]
• Zhang, Haiyang, et al. "Weighted Matrix Factorization with Bayesian Personalized Ranking." SAI Computing Conference, London, UK. 2017.[pdf]
• Nesterov, Yurii. "Primal-dual subgradient methods for convex problems." Mathematical programming 120.1 (2009): 221-259.[pdf]
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• Hong, Liangjie, Aziz S. Doumith, and Brian D. Davison. "Co-factorization machines: modeling user interests and predicting individual decisions in twitter." Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 2013.[pdf]
• * Srebro, Nathan, Jason Rennie, and Tommi S. Jaakkola. "Maximum-margin matrix factorization." Advances in neural information processing systems. 2005.[pdf]
• * Weimer, Markus, Alexandros Karatzoglou, and Alex Smola. "Adaptive collaborative filtering." Proceedings of the 2008 ACM conference on Recommender systems. ACM, 2008.[pdf]
• * Glover, Fred. "Improved linear integer programming formulations of nonlinear integer problems." Management Science 22.4 (1975): 455-460.[pdf]
• Sembium, Vivek, et al. "Recommending Product Sizes to Customers." Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 2017.[pdf]
• Chou, Szu-Yu, et al. "Addressing Cold Start for Next-song Recommendation." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.[pdf]
• * Fernández-Tobías, Ignacio, et al. "Accuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback." Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016.[pdf]
• MLA Cheung, Wang Chi, David Simchi-Levi, and He Wang. "Dynamic pricing and demand learning with limited price experimentation." Operations Research 65.6 (2017): 1722-1731.[pdf]
• Lim, Daryl, Julian McAuley, and Gert Lanckriet. "Top-n recommendation with missing implicit feedback." Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 2015.[pdf]
• Balakrishnan, Suhrid, and Sumit Chopra. "Collaborative ranking." Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 2012.[pdf]
• Okura, Shumpei, et al. "Embedding-based news recommendation for millions of users." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.[pdf]
• Okura, Shumpei, Yukihiro Tagami, and Akira Tajima. "Article De-duplication Using Distributed Representations." Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee, 2016.[pdf]
• * Bogers, Toine, and Marijn Koolen. "Defining and Supporting Narrative-driven Recommendation." Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 2017.[pdf]

• Nambiar, Mila, David Simchi-Levi, and He Wang. "Dynamic Learning and Price Optimization with Endogeneity Effect." (2016).[pdf]
• Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, and Deborah Estrin. 2018. OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms. In Proceedings of WSDM’18, February 5–9, 2018, Marina Del Rey, CA, USA.[pdf]
• Liu, Kuan, and Prem Natarajan. "A Batch Learning Framework for Scalable Personalized Ranking." arXiv preprint arXiv:1711.04019 (2017).[pdf]
• He, Xiangnan, et al. "Fast matrix factorization for online recommendation with implicit feedback." Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2016.[pdf]
• Elahi, Mehdi, et al. "Exploring the Semantic Gap for Movie Recommendations." Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 2017.[pdf]
• Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4.[pdf]

• Zhou, Zhi-Hua. "Learnware: on the future of machine learning." Frontiers of Computer Science 10.4 (2016): 589-590.APA[pdf]
• Zhou, Guorui, et al. "Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net." stat 1050 (2017): 16.[pdf]
• * Ding, Daizong, et al. "BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network." Conference on Information and Knowledge Management (CIKM). 2017.[pdf]
• * Lin, Tsung-Yi, et al. "Focal loss for dense object detection." arXiv preprint arXiv:1708.02002 (2017).[pdf]
• * Prillo, Sebastian. "An Elementary View on Factorization Machines." Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 2017.[pdf]
• Zhou, Li. "A survey on contextual multi-armed bandits." arXiv preprint arXiv:1508.03326 (2015).[pdf]
• Liang, Dawen, et al. "Modeling user exposure in recommendation." Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2016.[pdf]
• Cremonesi, Paolo, Yehuda Koren, and Roberto Turrin. "Performance of recommender algorithms on top-n recommendation tasks." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.[pdf]

• Ayush Singhal, et al. "Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works." arXiv preprint arXiv:1712.07525 (2017). [pdf]
• * Luo, Xin, et al. "A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method." IEEE transactions on neural networks and learning systems 27.3 (2016): 579-592.[pdf]
• * Kumar, Vaibhav, et al. "Deep Neural Architecture for News Recommendation." Working Notes of the 8th International Conference of the CLEF Initiative, Dublin, Ireland. CEUR Workshop Proceedings. 2017.[pdf]
• Fisher, Marshall, Santiago Gallino, and Jun Li. "Competition-based dynamic pricing in online retailing: A methodology validated with field experiments." Management Science (2017).[pdf]
• Chaney, Allison JB, Brandon M. Stewart, and Barbara E. Engelhardt. "How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility." arXiv preprint arXiv:1710.11214 (2017).[pdf]

• Li, Lihong, et al. "A contextual-bandit approach to personalized news article recommendation." Proceedings of the 19th international conference on World wide web. ACM, 2010.[pdf]
• Chen, Ting, et al. "On Sampling Strategies for Neural Network-based Collaborative Filtering." Proceedings of the 23th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2017.[pdf]
• * Kang, Zhao, Chong Peng, and Qiang Cheng. "Top-N Recommender System via Matrix Completion." AAAI. 2016.[pdf]
• * Wu, Qingyun, et al. "Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems." (2017).[pdf]
• * Ferreira, Kris Johnson, David Simchi-Levi, and He Wang. "Online network revenue management using Thompson sampling." (2016).[pdf]

• Amatriain X, Lathia N, Pujol J M, et al. The wisdom of the few: a collaborative filtering approach based on expert opinions from the web[C]//Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009: 532-539.[pdf]
• Zhou Y, Wilkinson D, Schreiber R, et al. Large-scale parallel collaborative filtering for the netflix prize[J]. Lecture Notes in Computer Science, 2008, 5034: 337-348.[pdf]
• Simchi-Levi, David. "The New Frontier of Price Optimization." MIT Sloan Management Review 59.1 (2017): 22. [pdf]
• * Ito, Shinji, and Ryohei Fujimaki. "Optimization beyond prediction: Prescriptive price optimization." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.[pdf]
• * Ito, Shinji, and Ryohei Fujimaki. "Large-Scale Price Optimization via Network Flow." Advances in Neural Information Processing Systems. 2016.[pdf]

• Beutel, Alex, et al. "Beyond Globally Optimal: Focused Learning for Improved Recommendations." Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017.[pdf]
• Pan, Rong, et al. "One-class collaborative filtering." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 2008.[pdf]
• Ferreira K J, Lee B H A, Simchi-Levi D. Analytics for an online retailer: Demand forecasting and price optimization[J]. Manufacturing & Service Operations Management, 2015, 18(1): 69-88.[pdf]
• * Manotumruksa J, Macdonald C, Ounis I. A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation[J]. 2017.[pdf]
• Xia F, Liu T Y, Wang J, et al. Listwise approach to learning to rank: theory and algorithm[C]//Proceedings of the 25th international conference on Machine learning. ACM, 2008: 1192-1199.[pdf]

• Karatzoglou, Alexandros, Linas Baltrunas, and Yue Shi. "Learning to rank for recommender systems." Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013.[pdf]
• He, Ruining, and Julian McAuley. "VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback." AAAI. 2016.[pdf]
• * Wang, Xiang, et al. "Item Silk Road: Recommending Items from Information Domains to Social Users." arXiv preprint arXiv:1706.03205 (2017).[pdf]
• Deshpande, Mukund, and George Karypis. "Item-based top-n recommendation algorithms." ACM Transactions on Information Systems (TOIS) 22.1 (2004): 143-177.[pdf]
• * Yi, Jinfeng, et al. "Scalable Demand-Aware Recommendation." Advances in Neural Information Processing Systems. 2017.[pdf]
• * Zheng, Zhaohui, et al. "A general boosting method and its application to learning ranking functions for web search." Advances in neural information processing systems. 2008.[pdf]

• 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]
• Volkovs, Maksims, Guangwei Yu, and Tomi Poutanen. "DropoutNet: Addressing Cold Start in Recommender Systems." Advances in Neural Information Processing Systems. 2017.[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]
• Ke, Guolin, et al. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." Advances in Neural Information Processing Systems. 2017.(https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf)
• Vartak, Manasi, Hugo Larochelle, and Arvind Thiagarajan. "A Meta-Learning Perspective on Cold-Start Recommendations for Items." Advances in Neural Information Processing Systems. 2017. [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]

• 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]

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.

• 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]