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

TL;DR

This survey comprehensively reviews recent developments, methods, and future directions of deep reinforcement learning applied to recommender systems, highlighting emerging trends and open challenges.

Contribution

It provides a systematic taxonomy and summary of existing DRL-based recommender system methods, along with insights into future research opportunities.

Findings

Overview of current DRL-based recommender system methods

Identification of emerging topics and open issues

Guidance for future research directions in the domain

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.