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

• Item type: Journal(1)
• 公開日: 2014-04-15
• タイトル: Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data
• タイトル / 言語: en
• タイトル: Music Recommender Adapting Implicit Context Using ‘renso’ Relation among Linked Data
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：Multiagent-based Societal Systems] context awareness, music recommendation, 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
• 著者所属: University of Electro-Communications
• 著者所属: University of Electro-Communications／Toshiba Corporation
• 著者所属: University of Electro-Communications
• 著者所属: University of Electro-Communications
• 著者所属: University of Electro-Communications
• 著者所属: University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications / Toshiba Corporation
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): University of Electro-Communications
• 著者名: Mian, Wang; Takahiro, Kawamura; Yuichi, Sei; Hiroyuki, Nakagawa; Yasuyuki, Tahara; Akihiko, Ohsuga
• 著者名(英): Mian, Wang; Takahiro, Kawamura; Yuichi, Sei; Hiroyuki, Nakagawa; Yasuyuki, Tahara; Akihiko, Ohsuga
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 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 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; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 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 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; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 55, 号 4, 発行日 2014-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764