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題名 Music recommendation based on multiple contextual similarity information
作者 Chen, C.-M.;Tsai, Ming-feng;Liu, J.-Y.;Yang, Y.-H.
蔡銘峰
貢獻者 資科系
關鍵詞 Context-based similarity; Convergence speed; Factorization machines; Feature similarities; Grouping technique; Music recommendation; Similarity informations; Social information
日期 2013
上傳時間 16-Apr-2015 17:30:41 (UTC+8)
摘要 This paper proposes a music recommendation approach based on various similarity information via Factorization Machines (FM). We introduce the idea of similarity, which has been widely studied in the filed of information retrieval, and incorporate multiple feature similarities into the FM framework, including content-based and context-based similarities. The similarity information not only captures the similar patterns from the referred objects, but enhances the convergence speed and accuracy of FM. In addition, in order to avoid the noise within large similarity of features, we also adopt the grouping FM as an extended method to model the problem. In our experiments, a music-recommendation dataset is used to assess the performance of the proposed approach. The datasets is collected from an online blogging website, which includes user listening history, user profiles, social information, and music information. Our experimental results show that, with various types of feature similarities the performance of music recommendation can be enhanced significantly. Furthermore, via the grouping technique, the performance can be improved significantly in terms of Mean Average Precision, compared to the traditional collaborative filtering approach. © 2013 IEEE.
關聯 Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013,Volume 1, 2013, 論文編號 6689995, Pages 65-72 ; Atlanta, GA; United States; 17 November 2013 到 20 November 2013; 類別編號E2902; 代碼 102427
10.1109/WI-IAT.2013.10
資料類型 conference
DOI http://dx.doi.org/10.1109/WI-IAT.2013.10
dc.contributor 資科系
dc.creator (作者) Chen, C.-M.;Tsai, Ming-feng;Liu, J.-Y.;Yang, Y.-H.
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2013
dc.date.accessioned 16-Apr-2015 17:30:41 (UTC+8)-
dc.date.available 16-Apr-2015 17:30:41 (UTC+8)-
dc.date.issued (上傳時間) 16-Apr-2015 17:30:41 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74634-
dc.description.abstract (摘要) This paper proposes a music recommendation approach based on various similarity information via Factorization Machines (FM). We introduce the idea of similarity, which has been widely studied in the filed of information retrieval, and incorporate multiple feature similarities into the FM framework, including content-based and context-based similarities. The similarity information not only captures the similar patterns from the referred objects, but enhances the convergence speed and accuracy of FM. In addition, in order to avoid the noise within large similarity of features, we also adopt the grouping FM as an extended method to model the problem. In our experiments, a music-recommendation dataset is used to assess the performance of the proposed approach. The datasets is collected from an online blogging website, which includes user listening history, user profiles, social information, and music information. Our experimental results show that, with various types of feature similarities the performance of music recommendation can be enhanced significantly. Furthermore, via the grouping technique, the performance can be improved significantly in terms of Mean Average Precision, compared to the traditional collaborative filtering approach. © 2013 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - 2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013,Volume 1, 2013, 論文編號 6689995, Pages 65-72 ; Atlanta, GA; United States; 17 November 2013 到 20 November 2013; 類別編號E2902; 代碼 102427
dc.relation (關聯) 10.1109/WI-IAT.2013.10
dc.subject (關鍵詞) Context-based similarity; Convergence speed; Factorization machines; Feature similarities; Grouping technique; Music recommendation; Similarity informations; Social information
dc.title (題名) Music recommendation based on multiple contextual similarity information
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/WI-IAT.2013.10-
dc.doi.uri (DOI) http://dx.doi.org/10.1109/WI-IAT.2013.10-