  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.
