Deep reinforcement learning in recommender systems: A survey and new perspectives

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 recent trends of deep reinforcement learning in recommender systems. We start by motivating the application of DRL in recommender systems, followed by a taxonomy of current DRLbased recommender systems and a summary of existing methods. We discuss emerging topics, open issues, and provide our perspective on advancing the domain. The survey serves as introductory material for readers from academia and industry to the topic and identifies notable opportunities for further research. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND

Automated summary

This article provides a timely and comprehensive overview of recent trends in deep reinforcement learning (DRL) in recommender systems. It discusses the motivation for applying DRL in recommender systems, presents a taxonomy and summary of current DRL-based recommender systems, and explores emerging topics and open issues. The survey serves as an introductory material for readers from academia and industry and identifies notable opportunities for further research.