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Adaptive Predictions in a User-Centered Recommender System

Anne Boyer 1 Sylvain Castagnos 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The size of available data on Internet is growing faster than human ability to treat it. Therefore, it becomes more and more difficult to identify the most relevant information, even for skilled people using efficient search engines. A way to cope with this problem is to automatically recommend data in accordance with users' preferences. Among others, collaborative filtering processes help users to find interesting items by comparing them with users having the same tastes. This paper describes a new user-centered recommender system relying on collaborative filtering and grid computing. Our model has been implemented in a Peer-to-Peer architecture. 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.
Keywords : Privacy Distributed Collaborative Filtering Recommender Systems Personalization Grid Computing Scalability Privacy.
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Conference papers
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https://hal.inria.fr/inria-00171786
Contributor : Sylvain Castagnos <>
Submitted on : Thursday, September 13, 2007 - 11:17:19 AM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM

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  • HAL Id : inria-00171786, version 1

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INRIA | CNRS | LORIA | UNIV-LORRAINE

Citation

Anne Boyer, Sylvain Castagnos. Adaptive Predictions in a User-Centered Recommender System. 3rd International Conference on Web Information Systems and Technologies (Webist 2007), INSTICC and Open University of Catalonia, Mar 2007, Barcelona, Spain. ⟨inria-00171786⟩

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