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Hokkaido University Collection of Scholarly and Academic Papers >
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Peer-reviewed Journal Articles, etc >
Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features
| Title: | Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features |
| Authors: | Sakurai, Keigo Browse this author | | Togo, Ren Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Music recommendation system | | knowledge graph | | reinforcement learning | | cold-start problem | | graph analysis | | graph embedding |
| Issue Date: | Jan-2022 |
| Publisher: | The Institute of Image Information and Television Engineers |
| Journal Title: | ITE Transactions on Media Technology and Applications |
| Volume: | 10 |
| Issue: | 1 |
| Start Page: | 8 |
| End Page: | 17 |
| Publisher DOI: | 10.3169/mta.10.8 |
| Abstract: | 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 coldstart 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/84046 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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