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https://github.com/cszhangzhen/DRL4Recsys

Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems
https://github.com/cszhangzhen/DRL4Recsys

List: DRL4Recsys

awesome-list deep-reinforcement-learning drl drl-papers paper read-papers recommender-system reinforcement-learning

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Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems

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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)

1. **Reinforced Negative Sampling over Knowledge Graph for Recommendation**. Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua. WWW 2020. [paper](https://arxiv.org/pdf/2003.05753.pdf)

1. **A Reinforcement Learning Framework for Relevance Feedback**. Ali Montazeralghaem, Hamed Zamani, James Allan. SIGIR 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3397271.3401099)

1. **KERL: A Knowledge-Guided Reinforcement Learning Model for  Sequential Recommendation**. Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu, Jimmy Huang. SIGIR 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3397271.3401134)

1. **Self-Supervised Reinforcement Learning for Recommender Systems**. Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon Jose. SIGIR 2020. [paper](https://arxiv.org/pdf/2006.05779.pdf)

1. **Reinforcement Learning to Rank with Pairwise Policy Gradient**. Jun Xu, Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, Ji-Rong Wen. SIGIR 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3397271.3401148)

1. **MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for Recommendations**. Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan. SIGIR 2020. [paper](https://arxiv.org/pdf/1903.09374.pdf)

1. **Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs**. Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie. SIGIR 2020. [paper](https://dl.acm.org/doi/10.1145/3397271.3401171)

1. **Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning**. Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu. SIGIR 2020. [paper](https://arxiv.org/pdf/2006.10389.pdf)

1. **Adversarial Attack and Detection on Reinforcement Learning based Recommendation System**. Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Wei Emma Zhang. SIGIR 2020. [paper](https://arxiv.org/pdf/2006.07934.pdf)

1. **Reinforcement Learning based Recommendation with Graph Convolutional Q-network**. Yu Lei, Hongbin Pei, Hanqi Yan, Wenjie Li. SIGIR 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3397271.3401237)

1. **Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation**. D Hong, L Miao, Y Li. SIGIR 2020. [paper](https://dl.acm.org/doi/abs/10.1145/3397271.3401225)

1. **Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems**. Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof. RecSys 2020. [paper](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/huang-2020-keeping.pdf)

1. **Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication**. Xu He, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang. RecSys 2020. [paper](https://arxiv.org/pdf/2008.09369.pdf)

1. **Whole-Chain Recommendations**. Xiangyu Zhao, Long Xia, Yihong Zhao, Dawei Yin, Jiliang Tang. CIKM 2020. [paper](https://arxiv.org/pdf/1902.03987.pdf)

1. **User Response Models to Improve a REINFORCE Recommender System**. Minmin Chen, Bo Chang, Can Xu, Ed Chi. WSDM 2021. [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441764)

1. **Reinforcement Recommendation with User Multi-aspect Preference**. Xu Chen, Yali Du, Long Xia, Jun Wang. WWW 2021. [paper](https://dl-acm-org.libproxy1.nus.edu.sg/doi/abs/10.1145/3442381.3449846)

1. **Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities**. Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen. WWW 2021. [paper](https://dl.acm.org/doi/pdf/10.1145/3442381.3449889)

1. **Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation**. Luo Ji, Qi Qin, Bingqing Han, Hongxia Yang. CIKM 2021. [paper](https://arxiv.org/abs/2108.09141)

1. **Generative Inverse Deep Reinforcement Learning for Online Recommendation**. Xiaocong Chen, Lina Yao, Aixin Sun, Xianzhi Wang, Xiwei Xu, Liming Zhu. CIKM 2021. [paper](https://arxiv.org/abs/2011.02248)

1. **Explore, Filter and Distill: Distilled Reinforcement Learning in Recommendation**. Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, Leyu Lin. CIKM 2021. [paper](https://dl-acm-org.libproxy1.nus.edu.sg/doi/abs/10.1145/3459637.3481917)

1. **Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation**. Yaxiong Wu, Craig Macdonald, and Iadh Ounis. RecSys 2021.

1. **Choosing the Best of All Worlds: Accurate, Diverse, and Novel Recommendations through Multi-Objective Reinforcement Learning**. Duan Stamenkovi, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Kleomenis Katevas. WSDM 2022. [paper](https://arxiv.org/abs/2110.15097)

1. **Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning**. Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, Yongfeng Zhang. WSDM 2022. [paper](https://arxiv.org/abs/2201.00140)

1. **Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation**. Sung-Jun Park, Dong-Kyu Chae, Hong-Kyun Bae, Sumin Park, and Sang-Wook Kim. WSDM 2022.

1. **Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective**. Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou and Zhaochun Ren. SIGIR 2022.

1. **Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation**. Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang and Shuai Li. SIGIR 2022.

1. **State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study**. Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood and Maarten de Rijke. SIGIR 2022.

1. **Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation**. Guogang Liao, Xiaowen Shi, Ze Wang, Xiaoxu Wu, Chuheng Zhang, Yongkang Wang, Xingxing Wang and Dong Wang. SIGIR 2022.

1. **Revisiting Interactive Recommender System with Reinforcement Learning**. Hojoon Lee, Dongyoon Hwang, Kyushik Min and Jaegul Choo. SIGIR 2022.

1. **Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation**. Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King. KDD 2022.

1. **Learning Relevant Information in Conversational Search and Recommendation using Deep Reinforcement Learning**. Ali Montazeralghaem, James Allan. KDD 2022.

1. **Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems**. Qihua Zhang, Junning Liu, Yuzhuo Dai, Kunlun Zheng, Fan Huang, Yifan Yuan, Xianfeng Tan, Yiyan Qi. KDD 2022.

1. **Generative Slate Recommendation with Reinforcement Learning**. Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, Maarten de Rijke. WSDM 2023.

1. **Multi-Task Recommendations with Reinforcement Learning**. Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai. WWW 2023.

1. **RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems**. Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang, Qianlong Xie, Lebin Lin, Xingxing Wang, Dong Wang. WWW 2023.