Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data

• Wang Mian

  University of Electro-Communications

• Kawamura Takahiro

  University of Electro-Communications Toshiba Corporation

• Sei Yuichi

  University of Electro-Communications

• Nakagawa Hiroyuki

  University of Electro-Communications

• Tahara Yasuyuki

  University of Electro-Communications

• Ohsuga Akihiko

  University of Electro-Communications

抄録

The existing music recommendation systems rely on user's contexts or content analysis to satisfy the users' music playing needs. They achieved a certain degree of success and inspired future researches to get more progress. However, a cold start problem and the limitation to the similar music have been pointed out. Therefore, this paper proposes a unique recommendation method using a ‘renso’ alignment among Linked Data, aiming to realize the music recommendation agent in smartphone. We first collect data from Last.fm, Yahoo! Local, Twitter and LyricWiki, and create a large scale of Linked Open Data (LOD), then create the ‘renso’ relation on the LOD and select the music according to the context. Finally, we confirmed an evaluation result demonstrating its accuracy and serendipity.

収録刊行物

• Journal of Information Processing

  Journal of Information Processing 22 (2), 279-288, 2014

  一般社団法人 情報処理学会

キーワード

• context awareness
• music recommendation
• Linked Data

詳細情報

• CRID: 1390282680271498624
• NII論文ID: 130003394474
• NII書誌ID: AA00700121
• DOI: 10.2197/ipsjjip.22.279
• ISSN: 18826652
• Web Site: http://id.nii.ac.jp/1001/00100906/
• Web Site: http://id.nii.ac.jp/1438/00009119/
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/22/2/22_279/_pdf
• 本文言語コード: en
• データソース種別:
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• 抄録ライセンスフラグ: 使用不可