Most users retrieve and access resources in complex systems, like Distributed Virtual Environments (DVE), or the Web by querying centralized search engines. Such systems normally compute their answers by estimating query-document similarities to rank the results, but also global ranks of the result pages by exploiting the hyperlink Web structure. User interests typically follow a sort of clustering property: users interested in a topic in the past are likely to be interested in these same topic also in the future. It follows that search results considered relevant by a user belonging to a group of homogeneous users will likely also be of interest to other users from the same group. In this paper, we propose the architecture of a peer-to-peer system that exploits a collaborative search mechanism, based on interest similarities among users. The paper discusses the challenges associated with a system in scenarios like DVEs and the Web and based on a self-organized network of users, grouped according to the interests detected by the queries they previously submitted to search engines. The final aim is to enhance the quality of both the results and the experience perceived by users.
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