Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement Learning

Sensors

Ren Togo¹  Miki Haseyama¹  Takahiro Ogawa¹  Keigo Sakurai² 

[1] Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan;Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan;

关键词: music playlist generation;    knowledge graph;    reinforcement learning;    multimedia techniques;    music recommendation;    preference sensing;   

DOI  :  10.3390/s22103722

来源: DOAJ

【 摘 要 】

In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users’ listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users’ long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users’ long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user’s unique preference.

【 授权许可】

Unknown