A good way to help users finding relevant items on document platforms consists in suggesting content in accordance with their preferences. When implementing such a recommender system, the number of potential users and the confidential nature of some data should be taken into account. This paper introduces a new P2P recommender system which models individual preferences and exploits them through a user-centered filtering algorithm. The latter has been designed to deal with problems of scalability, reactivity, and privacy. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Castagnos, S., & Boyer, A. (2007). Modeling preferences in a distributed recommender system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4511 LNCS, pp. 400–404). Springer Verlag. https://doi.org/10.1007/978-3-540-73078-1_53
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