Explainable Artist Recommendation Based on Reinforcement Knowledge Graph Exploration

This paper presents a novel artist recommendation method based on knowledge graph and reinforcement learning. In the field of music services, online platforms based on subscriptions are becoming the mainstream, and the recommendation technology needs to be updated accordingly. In this field, it is desirable to achieve usercentered recommendation that satisfies various user preferences, rather than the recommendation that is biased toward popular songs and artists. Our method realizes highly accurate and explainable artist recommendation by exploring the knowledge graph constructed from users' listening histories and artist metadata. We have confirmed the effectiveness of our method by comparing it with an existing state-of-the-art method.