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oai:ipsj.ixsq.nii.ac.jp:00100831 2025-01-20T06:46:26Z 581:7397:7561

Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data Mian, Wang Takahiro, Kawamura Yuichi, Sei Hiroyuki, Nakagawa Yasuyuki, Tahara Akihiko, Ohsuga Mian, Wang Takahiro, Kawamura Yuichi, Sei Hiroyuki, Nakagawa Yasuyuki, Tahara Akihiko, Ohsuga [特集：Multiagent-based Societal Systems] context awareness, music recommendation, Linked Data 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 of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online) DOI　http://dx.doi.org/10.2197/ipsjjip.22.279 ------------------------------ 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 of Information Processing(JIP). This preprint should not be cited. This article 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 eng journal article https://ipsj.ixsq.nii.ac.jp/records/100831 1882-7764 AN00116647 情報処理学会論文誌 55 4 https://ipsj.ixsq.nii.ac.jp/record/100831/files/IPSJ-JNL5504014.pdf application/pdf 2.8 MB 2016-04-15