Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information

  • Georgios Pitsilis
  • Xiangliang Zhang
  • Wei Wang
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 358)


In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like k-Means and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust could be exploited with AP for forming clusters of high quality. We conducted a series of evaluation tests using data from a real Recommender system from which we derived conclusions about the usefulness of trust information in forming clusters of Recommenders. Moreover, from our results we conclude that the potential advantages in using clustering can be enlarged by making use of the information that Social Networks can provide.


Social Trust Clustering Recommender Systems Affinity Propagation 


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Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Georgios Pitsilis
    • 1
  • Xiangliang Zhang
    • 2
  • Wei Wang
    • 3
  1. 1.Faculty of Science, Technology and CommunicationUniversité du LuxembourgLuxembourg
  2. 2.Division of MCSEKing Abdullah University of Science and Technology (KAUST)Saudi Arabia
  3. 3.Interdisciplinary Centre for Security, Reliability and Trust (SnT Centre)Université du LuxembourgLuxembourg

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