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    Clustering recommenders in collaborative filtering using explicit trust information

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    Type
    Conference Paper
    Authors
    Pitsilis, Georgios
    Zhang, Xiangliang cc
    Wang, Wei cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/564340
    
    Metadata
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    Abstract
    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 Epinions.com 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.
    Citation
    Pitsilis, G., Zhang, X., & Wang, W. (2011). Clustering Recommenders in Collaborative Filtering Using Explicit Trust Information. IFIP Advances in Information and Communication Technology, 82–97. doi:10.1007/978-3-642-22200-9_9
    Publisher
    Springer Nature
    Journal
    IFIP Advances in Information and Communication Technology
    Conference/Event name
    5th IFIP WG 11.11 International Conference on Trust Management, IFIPTM 2011
    ISBN
    9783642221996
    DOI
    10.1007/978-3-642-22200-9_9
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-22200-9_9
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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