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# Deep Reinforcement Learning for Recommender Systems
Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender system
## Courses
#### UCL Course on RL
[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
#### CS 294-112 at UC Berkeley
[http://rail.eecs.berkeley.edu/deeprlcourse/](http://rail.eecs.berkeley.edu/deeprlcourse/)
#### Stanford CS234: Reinforcement Learning
[http://web.stanford.edu/class/cs234/index.html](http://web.stanford.edu/class/cs234/index.html)
## Book
1. **Reinforcement Learning: An Introduction (Second Edition)**. Richard S. Sutton and Andrew G. Barto. [book](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
## Papers
### Survey Papers
1. **A Brief Survey of Deep Reinforcement Learning**. Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath. 2017. [paper](https://arxiv.org/pdf/1708.05866.pdf)
1. **Deep Reinforcement Learing: An Overview**. Yuxi Li. 2017. [paper](https://arxiv.org/pdf/1701.07274.pdf)
### Conference Papers
1. **An MDP-Based Recommender System**. Guy Shani, David Heckerman, Ronen I. Brafman. JMLR 2005. [paper](http://www.jmlr.org/papers/volume6/shani05a/shani05a.pdf)
1. **Usage-Based Web Recommendations: A Reinforcement Learning Approach**. Nima Taghipour, Ahmad Kardan, Saeed Shiry Ghidary. Recsys 2007. [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.9640&rep=rep1&type=pdf)
1. **DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation**. Elad Liebman, Maytal Saar-Tsechansky, Peter Stone. AAMAS 2015. [paper](https://arxiv.org/pdf/1401.1880.pdf)
1. **Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning**. Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, Xiaoyan Zhu. WWW 2018. [paper](https://arxiv.org/pdf/1809.06260.pdf)
1. **Reinforcement Mechanism Design for e-commerce**. Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang. WWW 2018. [paper](https://arxiv.org/pdf/1708.07607.pdf)
1. **DRN: A Deep Reinforcement Learning Framework for News Recommendation**. Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, Zhenhui Li. WWW 2018. [paper](http://www.personal.psu.edu/~gjz5038/paper/www2018_reinforceRec/www2018_reinforceRec.pdf)
1. **Deep Reinforcement Learning for Page-wise Recommendations**. Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang. RecSys 2018. [paper](https://arxiv.org/pdf/1805.02343.pdf)
1. **Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning**. Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, Dawei Yin. KDD 2018. [paper](https://arxiv.org/pdf/1802.06501.pdf)
1. **Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation**. Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang, Hai-Hong Tang. KDD 2018. [paper](http://lamda.nju.edu.cn/yuy/GetFile.aspx?File=papers/kdd18-RobustDQN.pdf)
1. **Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application**. Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, Yinghui Xu. KDD 2018. [paper](https://arxiv.org/pdf/1803.00710.pdf)
1. **A Reinforcement Learning Framework for Explainable Recommendation**. Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, Xing Xie. ICDM 2018. [paper](https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf)
1. **Top-K Off-Policy Correction for a REINFORCE Recommender System**. Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi. WSDM 2019. [paper](https://arxiv.org/pdf/1812.02353.pdf)
1. **Generative Adversarial User Model for Reinforcement Learning Based Recommendation System**. Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song. ICML 2019. [paper](http://proceedings.mlr.press/v97/chen19f/chen19f.pdf)
1. **Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning**. Ryuichi Takanobu, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, Bo Zheng. WWW 2019. [paper](https://arxiv.org/pdf/1902.08882.pdf)
1. **Policy Gradients for Contextual Recommendations**. Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He. WWW 2019. [paper](https://arxiv.org/pdf/1802.04162.pdf)
1. **Reinforcement Knowledge Graph Reasoning for Explainable Recommendation**. Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. SIGIR 2019. [paper](http://yongfeng.me/attach/xian-sigir2019.pdf)
1. **Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems**. Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, Dawei Yin. KDD 2019. [paper](https://arxiv.org/pdf/1902.05570.pdf)
1. **Environment reconstruction with hidden confounders for reinforcement learning based recommendation**. Wenjie Shang, Yang Yu, Qingyang Li, Zhiwei Qin, Yiping Meng, Jieping Ye. KDD 2019. [paper](http://lamda.nju.edu.cn/yuy/GetFile.aspx?File=papers/kdd19-confounder.pdf)
1. **Exact-K Recommendation via Maximal Clique Optimization**. Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu. KDD 2019. [paper](https://arxiv.org/pdf/1905.07089.pdf)
1. **Hierarchical Reinforcement Learning for Course Recommendation in MOOCs**. Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun. AAAI 2019. [paper](https://xiaojingzi.github.io/publications/AAAI19-zhang-et-al-HRL.pdf)
1. **Large-scale Interactive Recommendation with Tree-structured Policy Gradient**. Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu. AAAI 2019. [paper](https://arxiv.org/pdf/1811.05869.pdf)
1. **Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning**. Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng. AAAI 2019. [paper](http://www.lamda.nju.edu.cn/yuy/GetFile.aspx?File=papers/aaai2019-virtualtaobao.pdf)
1. **A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation**. Xueying Bai, Jian Guan, Hongning Wang. NeurIPS 2019. [paper](http://papers.nips.cc/paper/9257-a-model-based-reinforcement-learning-with-adversarial-training-for-online-recommendation.pdf)
1. **Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning**. Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen, Lawrence Carin. NeurIPS 2019. [paper](http://people.ee.duke.edu/~lcarin/Ruiyi_NeurIPS2019.pdf)
1. **DRCGR: Deep reinforcement learning framework incorporating CNN and GAN-based for interactive recommendation**. Rong Gao, Haifeng Xia, Jing Li, Donghua Liu, Shuai Chen, and Gang Chun. ICDM 2019. [paper](https://ieeexplore.ieee.org/document/8970700)
1. **Pseudo Dyna-Q: A Reinforcement Learning Framework for Interactive Recommendation**. Lixin Zou, Long Xia, Pan Du, Zhuo Zhang, Ting Bai, Weidong Liu, Jian-Yun Nie, Dawei Yin. WSDM 2020. [paper](https://tbbaby.github.io/pub/wsdm20.pdf)
1. **End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding**. Feng Liu, Huifeng Guo, Xutao Li, Ruiming Tang, Yunming Ye, Xiuqiang He. WSDM 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371858)
### Preprint Papers
1. **Reinforcement Learning based Recommender System using Biclustering Technique**. Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, Sungroh Yoon. arxiv 2018. [paper](https://arxiv.org/pdf/1801.05532.pdf)
1. **Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling**. Feng Liu, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang. arxiv 2018. [paper](https://arxiv.org/pdf/1810.12027.pdf)
1. **Model-Based Reinforcement Learning for Whole-Chain Recommendations**. Xiangyu Zhao, Long Xia, Yihong Zhao, Dawei Yin, Jiliang Tang. arxiv 2019. [paper](https://arxiv.org/pdf/1902.03987.pdf)