• DocumentCode
    2515184
  • Title

    Two-Phase Clustering-based Collaborative Filtering Algorithm

  • Author

    Zhang, Chen ; Dai, Jun ; Li, Pei ; Li, Qing ; Luo, XuBin

  • Author_Institution
    Southwestern Univ. of Finance & Econ., Chengdu, China
  • fYear
    2011
  • fDate
    5-6 Nov. 2011
  • Firstpage
    19
  • Lastpage
    23
  • Abstract
    Internet and E-Commerce are becoming an integral part of everyday life as we accumulate more and more knowledge that demands for some personalized recommendation technology. Collaborative filtering recommendation is the most successful personalized recommendation algorithm among current technologies. The paper suggests the two-phase clustering-based collaborative filtering algorithm. which not only reduces the sparsity of data and improves the accuracy of the nearest neighbor, but also improves the recommendation accuracy and reduces the time complexity compared with the traditional algorithms.
  • Keywords
    Internet; collaborative filtering; computational complexity; electronic commerce; pattern clustering; recommender systems; Internet; complexity reduction; e-commeree; nearest neighbor; personalized recommendation technology; two phase clustering based collaborative filtering algorithm; Accuracy; Clustering algorithms; Collaboration; Correlation; Motion pictures; Prediction algorithms; Smoothing methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management of e-Commerce and e-Government (ICMeCG), 2011 Fifth International Conference on
  • Conference_Location
    Hubei
  • Print_ISBN
    978-1-4577-1659-1
  • Type

    conf

  • DOI
    10.1109/ICMeCG.2011.33
  • Filename
    6092624