A Collaborative Filtering Recommendation Methodology for Peer-to-Peer Systems

  • Hyea Kyeong Kim
  • Jae Kyeong Kim
  • Yoon Ho Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3590)

Abstract

To deal with the image recommending problems in P2P systems, this paper proposes a PeerCF-CB (Peer oriented Collaborative Filtering recommendation methodology using Contents-Based filtering). PeerCF-CB uses recent ratings of peers to adopt a change in peer preferences, and searches for nearest peers with similar preference through peer-based local information only. The performance of PeerCF-CB is evaluated with real transaction data in S content provider. Our experimental result shows that PeerCF-CB offers not only remarkably higher quality of recommendations but also dramatically faster performance than the centralized collaborative filtering recommendation systems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hyea Kyeong Kim
    • 1
  • Jae Kyeong Kim
    • 1
  • Yoon Ho Cho
    • 2
  1. 1.School of Business AdministrationKyungHee UniversitySeoulKorea
  2. 2.School of E-BusinessKookMin UniversitySeoulKorea

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