A Survey on Reinforcement Learning for Recommender Systems
This is an incremental survey that serves as a reference for researchers and practitioners working on RL-based recommender systems.
The paper provides a comprehensive survey of reinforcement learning (RL) applications in recommender systems, covering four scenarios and analyzing challenges and solutions, with empirical results showing RL-based methods often surpass supervised learning approaches.
Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years, owing to the interactive nature and autonomous learning ability. Empirical results show that RL-based recommendation methods often surpass most of supervised learning methods. Nevertheless, there are various challenges of applying RL in recommender systems. To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems. To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational recommendatin, sequential recommendation, and explainable recommendation. Furthermore, we systematically analyze the challenges and relevant solutions on the basis of existing literature. Finally, under discussion for open issues of RL and its limitations of recommender systems, we highlight some potential research directions in this field.