English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88866/118573 (75%)
Visitors : 23558535      Online Users : 241
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/74634
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/74634


    Title: Music recommendation based on multiple contextual similarity information
    Authors: Chen, C.-M.;Tsai, Ming-feng;Liu, J.-Y.;Yang, Y.-H.
    蔡銘峰
    Contributors: 資科系
    Keywords: Context-based similarity;Convergence speed;Factorization machines;Feature similarities;Grouping technique;Music recommendation;Similarity informations;Social information
    Date: 2013
    Issue Date: 2015-04-16 17:30:41 (UTC+8)
    Abstract: 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.
    Relation: 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
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/WI-IAT.2013.10
    DOI: 10.1109/WI-IAT.2013.10
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML651View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback