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.

翻译：推荐系统已广泛应用于各种现实场景中，帮助我们发现有用信息。特别是基于强化学习的推荐系统，因其交互特性和自主学习能力，近年来已成为一个新兴的研究热点。实证结果表明，基于强化学习的推荐方法通常优于大多数监督学习方法。然而，在推荐系统中应用强化学习仍面临诸多挑战。为了理解这些挑战及相应解决方案，需要为从事基于强化学习推荐系统的研究人员和从业者提供参考。为此，我们首先全面概述、比较和总结了应用于四种典型推荐场景的强化学习方法，包括交互式推荐、对话式推荐、序列推荐和可解释推荐。进而，基于现有文献系统分析了相关挑战及解决方案。最后，在探讨强化学习在推荐系统中的开放问题及局限性时，我们指出了该领域若干潜在的研究方向。