This paper is a preliminary report which presents information filtering solutions designed within the scope of a collaboration between our laboratory and the company of broadcasting per satellite SES ASTRA. The latter have finalized a system sponsored by advertisement and supplying to users a high bandwidth access to hundreds of web sites for free. This project aims at highlighting the benefits of collaborative filtering by including such a module in the architecture of their product. The term of collaborative filtering [Goldberg, 2000] denotes techniques using the known preferences of a group of users to predict the unknown preference of a new user. Our problem has consisted in finding a way to provide scale for hundreds thousands of people, while preserving anonymity of users (personal data remain on client side). Thus, we use an existing clustering method, that we have improved so that it is distributed respectively on client and server side. Nevertheless, in the absence of numerical votes for marketing reasons, we have chosen to do an innovative combination of this decentralized collaborative filtering method with a user profiling technique. We have also been submitted to constraints such as a short answer time on client side, in order to be compliant with the ASTRA architecture.