Music Recommender Adapting Implicit Context Using `renso' Relation among Linked Data (Preprint)

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概要

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• 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 methodusing 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.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online)DOI http://dx.doi.org/10.2197/ipsjjip.22.279------------------------------

• 2014-04-15

著者

• Yasuyuki Tahara

  University of Electro-Communications

• Mian Wang

  University of Electro-Communications

• Takahiro Kawamura

  University of Electro-Communications|Toshiba Corporation

• Yuichi Sei

  University of Electro-Communications

• Hiroyuki Nakagawa

  University of Electro-Communications

• Akihiko Ohsuga

  University of Electro-Communications

関連論文

• Music Recommender Adapting Implicit Context Using `renso' Relation among Linked Data (Preprint)

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