A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

• URL: http://arxiv.org/abs/2109.03540v2
• Date: Thu, 9 Sep 2021 10:16:31 GMT
• Title: A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
• Authors: Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang
• Abstract summary: This survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We provide a taxonomy of current DRL-based recommender systems and a summary of existing methods.
• Score: 40.73124164815037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems. We start with the motivation of applying DRL in recommender systems. Then, we provide a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics and open issues, and provide our perspective on advancing the domain. This survey serves as introductory material for readers from academia and industry into the topic and identifies notable opportunities for further research.

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