[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

• Sakurai Keigo

  Graduate School of Information Science and Technology, Hokkaido University

• Togo Ren

  Education and Research Center for Mathematical and Data Science, Hokkaido University

• Ogawa Takahiro

  Faculty of Information Science and Technology, Hokkaido University

• Haseyama Miki

  Faculty of Information Science and Technology, Hokkaido University

抄録

In this study, we propose a new deep reinforcement learning-based music recommendation method with knowledge graphs. With the rapid development of Web services, music-related content posted on platforms, such as YouTube, is increasing dramatically. Conventional recommendation methods based on knowledge graphs have struggled with the cold-start problem caused by a lack of user preference information. The proposed method can solve this problem by introducing acoustic feature edges in the constructed knowledge graph. Furthermore, we realize efficient search using a deep reinforcement learning algorithm on a dense knowledge graph introducing acoustic feature-based edges. The proposed method can make appropriate recommendations even with a small amount of user preference information by learning the optimal action of the agent. We confirm the effectiveness of the proposed method by comparing our method with several conventional and state-of-the-art recommendation methods.

収録刊行物

• 映像情報メディア学会英語論文誌

  映像情報メディア学会英語論文誌 10 (1), 8-17, 2022

  一般社団法人 映像情報メディア学会

キーワード

• Music recommendation system
• knowledge graph
• reinforcement learning
• cold-start problem
• graph analysis
• graph embedding

詳細情報

• CRID: 1390853567321137024
• NII論文ID: 130008139394
• DOI: 10.3169/mta.10.8
• ISSN: 21867364
• Web Site: https://www.jstage.jst.go.jp/article/mta/10/1/10_8/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可