Peer-to-Peer clustering of Web-browsing users
Patrizio Dazzi, Pascal A. Felber, Lorenzo Leonini, Matteo Mordacchini, Raffaele Perego, Martin Rajman, Étienne Rivière · 2009
For most users, Web-based centralized search engines are the access point to distributed resources such as Web pages, items shared in file sharing-systems, etc. Unfortunately, existing search engines compute their results on the basis of structural information only, e.g., the Web graph structure or query-document similarity estimations. Users expectations are rarely considered to enhance the subjective relevance of returned results. However, exploiting such information can help search engines satisfy users by tailoring search results. Interestingly, user interests typically follow the clustering property: users who were interested in the same topics in the past are likely to be interested in these same topics 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 novel peerto-peer system exploiting collaboratively built search mechanisms. The paper discusses the challenges associated with a system based on the interest clustering principle. The objective is to provide a self-organized network of users, grouped according to the interests they share, that can be leveraged to enhance the quality of the experience perceived by users searching the Web.