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  1. 学術論文等

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

https://uec.repo.nii.ac.jp/records/9187
https://uec.repo.nii.ac.jp/records/9187
2fb606ab-0875-4ea7-9b06-990598ad7ac0
名前 / ファイル ライセンス アクション
22_279.pdf 22_279 (2.8 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2019-05-09
タイトル
言語 en
タイトル Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data
言語
言語 eng
キーワード
言語 en
主題Scheme Other
主題 context awareness
キーワード
言語 en
主題Scheme Other
主題 music recommendation
キーワード
言語 en
主題Scheme Other
主題 Linked Data
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者 Wang, Mian

× Wang, Mian

WEKO 25130

en Wang, Mian

Search repository
Kawamura, Takahiro

× Kawamura, Takahiro

WEKO 25131

en Kawamura, Takahiro

Search repository
Sei, Yuichi

× Sei, Yuichi

WEKO 25132

en Sei, Yuichi

Search repository
Nakagawa, Hiroyuki

× Nakagawa, Hiroyuki

WEKO 25133

en Nakagawa, Hiroyuki

Search repository
Tahara, Yasuyuki

× Tahara, Yasuyuki

WEKO 25134

en Tahara, Yasuyuki

Search repository
Ohsuga, Akihiko

× Ohsuga, Akihiko

WEKO 25135

en Ohsuga, Akihiko

Search repository
抄録
内容記述タイプ Abstract
内容記述 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.
書誌情報 en : Journal of Information Processing

巻 22, 号 2, p. 279-288, 発行日 2014-04
出版者
出版者 Information Processing Society of Japan (情報処理学会)
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-6652
DOI
関連タイプ isIdenticalTo
識別子タイプ DOI
関連識別子 10.2197/ipsjjip.22.279
権利
権利情報 ©2014 Information Processing Society of Japan. Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof.
関連サイト
識別子タイプ DOI
関連識別子 https://doi.org/10.2197/ipsjjip.22.279
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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