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

2021-09-08 10:44:55
Xiaocong Chen, Lina Yao, Julian McAuley, Guangling Zhou, Xianzhi Wang
arXiv_AI
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2109.03540

PDF

https://arxiv.org/pdf/2109.03540.pdf

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