Distributed collaborative filtering for peer-to-peer file sharing systems

ABSTRACT

Collaborative filtering requires a centralized rating database. However, within a peer-to-peer network such a centralized database is not readily available. In this paper, we propose a fully distributed collaborative filtering method that is self-organizing and operates in a distributed way. Similarity ranks between multimedia files (items) are calculated by log-based user profiles and are stored locally at these items in so-called buddy tables. This intuitively creates a semantic overlay to organize multimedia files. Based on this semantic overlay and the items that a user has downloaded previously (indicating the profile of the user), recommendations can be performed and the recommended items can be easily located. We have tested our distributed collaborative filtering approach and compared it to centralized collaborative filtering, showing that it has similar performance. It is therefore a promising technique to facilitate filtering for relevant multimedia data in P2P networks.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software
          Subjects: Distributed systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Collaborative computing

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures

General Terms:
Algorithms, Design, Measurement

Keywords:
collaborative filtering, peer-to-peer networks, personalization, recommendation