English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 92604/122928 (75%)
Visitors : 26826757      Online Users : 345
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 140.119/98247
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/98247


    Title: Leverage Item Popularity and Recommendation Quality via Cost-sensitive Factorization Machines
    Authors: Chen, Chih-Ming;Chen, Hsin-Ping;Tsai, Ming-Feng;Yang, Yi-Hsuan
    陳志明;陳心蘋;蔡銘峰
    Contributors: 經濟系;資科系
    Date: 2014-12
    Issue Date: 2016-06-22 17:19:59 (UTC+8)
    Abstract: The accuracy of recommendation trends to be worse towards the long tail of the popularity distribution of items, but items in the long tail are generally considered to be valuable as they occupy a majority part of entire data. In this paper, we develop an instance-level cost-sensitive Factorization Machine (FM) to tackle the problem. The new algorithm allows the FM model to automatically leverage the trade-off between item popularity and recommendation quality. Specifically, by adding a cost criterion to the loss function, the FM model is now able to discriminate the relative importance of popularity from massive data. In addition, we convert several well-known functions into the popularity weighting functions, thereby demonstrating that the proposed method can fit the model parameters to various kinds of measurements. In the experiments, we assess the performance on a real-world music dataset which is collected from an online music streaming service, KKBOX. The dataset contains 1,800,000 listening records that cover 5,000 users and 30,000 songs. The results show that, the proposed method not only keeps the performance as primitive model but also avoids retrieving too much popular music in the top recommendations.
    Relation: Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM '14), 1158-1162, 2014
    國際標準書號9781479942756
    Data Type: conference
    Appears in Collections:[資訊科學系] 會議論文
    [經濟學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML659View/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