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.
