Personalized Communities in a Distributed Recommender System

Sylvain Castagnos 1 Anne Boyer 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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Communication dans un congrès
Giambattista Amati and Claudio Carpineto and Giovanni Romano. 29th European Conference on Information Retrieval - ECIR'07, Apr 2007, Rome, Italy. Springer Berlin / Heidelberg, 4425, pp.343-355, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-71496-5_32〉
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Soumis le : jeudi 13 septembre 2007 - 11:32:04
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51
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Sylvain Castagnos, Anne Boyer. Personalized Communities in a Distributed Recommender System. Giambattista Amati and Claudio Carpineto and Giovanni Romano. 29th European Conference on Information Retrieval - ECIR'07, Apr 2007, Rome, Italy. Springer Berlin / Heidelberg, 4425, pp.343-355, 2007, Lecture Notes in Computer Science. 〈10.1007/978-3-540-71496-5_32〉. 〈inria-00171796〉

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