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Personalized Communities in a Distributed Recommender System

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Advances in Information Retrieval (ECIR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4425))

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Abstract

The amount of data exponentially increases in information systems and it becomes more and more difficult to extract the most relevant information within a very short time. Among others, collaborative filtering processes help users to find interesting items by modeling their preferences and by comparing them with users having the same tastes. Nevertheless, there are a lot of aspects to consider when implementing such a recommender system. The number of potential users and the confidential nature of some data are taken into account. This paper introduces a new distributed recommender system based on a user-based filtering algorithm. Our model has been transposed for Peer-to-Peer architectures. It has been especially designed to deal with problems of scalability and privacy. Moreover, it adapts its prediction computations to the density of the user neighborhood.

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Authors and Affiliations

  1. LORIA - Université Nancy 2, Campus Scientifique - B.P.239, 54506 Vandoeuvre-lès-Nancy Cedex, France

    Sylvain Castagnos & Anne Boyer

Authors
  1. Sylvain Castagnos
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  2. Anne Boyer
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Giambattista Amati Claudio Carpineto Giovanni Romano

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Castagnos, S., Boyer, A. (2007). Personalized Communities in a Distributed Recommender System. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_32

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  • DOI: https://doi.org/10.1007/978-3-540-71496-5_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71494-1

  • Online ISBN: 978-3-540-71496-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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