@article{oai:ipsj.ixsq.nii.ac.jp:00100831,
 author = {Mian, Wang and Takahiro, Kawamura and Yuichi, Sei and Hiroyuki, Nakagawa and Yasuyuki, Tahara and Akihiko, Ohsuga and Mian, Wang and Takahiro, Kawamura and Yuichi, Sei and Hiroyuki, Nakagawa and Yasuyuki, Tahara and Akihiko, Ohsuga},
 issue = {4},
 journal = {情報処理学会論文誌},
 month = {Apr},
 note = {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
------------------------------},
 title = {Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data},
 volume = {55},
 year = {2014}
}