Explainable artist recommendation based on reinforcement knowledge graph exploration

Keigo Sakurai; Ren Togo; Takahiro Ogawa; Miki Haseyama

doi:10.1117/12.2626112

30 April 2022 Explainable artist recommendation based on reinforcement knowledge graph exploration

Keigo Sakurai, Ren Togo, Takahiro Ogawa, Miki Haseyama

Author Affiliations +

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770J (2022) https://doi.org/10.1117/12.2626112
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

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 user-centered 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.

Citation Download Citation

Keigo Sakurai, Ren Togo, Takahiro Ogawa, and Miki Haseyama "Explainable artist recommendation based on reinforcement knowledge graph exploration", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770J (30 April 2022); https://doi.org/10.1117/12.2626112

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