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With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning. In Proceedings of the 2018 World Wide Web Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 1939–1948
Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, and Xiaoyan Zhu. 2018 · 1948
Earlier work this paper cites.
R-max-a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I Brafman and Moshe Tennenholtz. 2002 · 2002
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
Earlier work this paper cites.
Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh. 2002 · 2002
Earlier work this paper cites.
User performance versus precision measures for simple search tasks. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval . 11–18
Andrew Turpin and Falk Scholer. 2006 · 2006
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013 · 2013
Earlier work this paper cites.
Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems . 2177–2185
Omer Levy and Yoav Goldberg. 2014 · 2014
Earlier work this paper cites.
Deep reinforcement learning in large discrete action spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin. 2015 · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
DeepFM: a factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence . 1725–1731
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Cited alongside, same era.
Multi-agent actor-critic for mixed cooperative-competitive environments. In Advances in neural information processing systems . 6379–6390
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017 · 2017
Cited alongside, same era.
A Reinforcement Learning Framework for Explainable Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) . IEEE, 587–596
Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie. 2018 · 2018
Later among the works it cites.
DRN: A Deep Reinforcement Learning Framework for News Recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 167–176
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018 · 2018
Later among the works it cites.
Deep Reinforcement Learning for Online Advertising in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiaobing Liu, Xiwang Yang, and Jiliang Tang. 2019a · 2019
Closest in time.
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2019b · 2019
Closest in time.
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Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Dawei Yin, Yihong Zhao, and Jiliang Tang. 2017 · 2017
Cited alongside, same era.
Reinforcement Mechanism Design for e-commerce. In Proceedings of the 2018 World Wide Web Conference . International World Wide Web Conferences Steering Committee, 1339–1348
Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, and Yiwei Zhang. 2018a · 2018
Cited alongside, same era.
Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, and Yong Yu. 2018b · 2018
Cited alongside, same era.
Top-K Off-Policy Correction for a REINFORCE Recommender System
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed Chi. 2018a · 2018
Cited alongside, same era.
Neural Model-Based Reinforcement Learning for Recommendation
Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2018c · 2018
Cited alongside, same era.
Reinforcement Learning based Recommender System using Biclustering Technique
Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, and Sungroh Yoon. 2018 · 2018
Cited alongside, same era.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization. In Analysis, and Application. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. London, UK
Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, and Yinghui Xu. 2018 · 2018
Cited alongside, same era.
Reinforcement mechanism design for fraudulent behaviour in e-commerce. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence
Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, and Yiwei Zhang. 2018b
Cited in the paper.
Deep reinforcement learning for search, recommendation, and online advertising: a survey by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator
Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin. 2019c · 2019
Closest in time.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2810–2818
Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019 · 2019
Closest in time.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jianping Wang, Jiliang Tang, and Qing Li. 2020 · 2020
Closest in time.
A Joint Neural Network for Session-Aware Recommendation
Yupu Guo, Duolong Zhang, Yanxiang Ling, and Honghui Chen. 2020 · 2020
Closest in time.
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 2468–2471
Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang, and Alex Beutel. 2020 · 2020
Closest in time.
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, and Bo Long. 2020a · 2020
Closest in time.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang. 2020b · 2020
Closest in time.