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For sequential recommendation, it is essential to capture and predict future or long-term user preference for generating accurate recommendation over time. To improve the predictive capacity, we adopt reinforcement learning (RL) for developing effective sequential recommenders. However, user-item interaction data is likely to be sparse, complicated and time-varying. It is not easy to directly apply RL techniques to improve the performance of sequential recommendation.

Inspired by the availability of knowledge graph (KG), we propose a novel Knowledge-guidEd Reinforcement Learning model (KERL for short) for fusing KG information into a RL framework for sequential recommendation. Specifically, we formalize the sequential recommendation task as a Markov Decision Process (MDP), and make three major technical extensions in this framework, including state representation, reward function and learning …