Clustering recommenders in collaborative filtering using explicit trust information

Georgios Pitsilis, Xiangliang Zhang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations


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. © 2011 International Federation for Information Processing.
Original languageEnglish (US)
Title of host publicationIFIP Advances in Information and Communication Technology
PublisherSpringer Nature
Number of pages16
ISBN (Print)9783642221996
StatePublished - 2011

ASJC Scopus subject areas

  • Information Systems and Management


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