A privacy preserving Web recommender system

In this paper we propose a recommender system that helps users to navigate though the Web by providing dynamically generated links to pages that have not yet been visited and are of potential interest. To this end, traditional recom- mender systems use Web Usage Mining (WUM) techniques in order to automatically extract knowledge from Web us- age data. Thanks to WUM techniques we are able to classify users and adaptively provide useful recommendations. The drawback of a user classification approach is that it makes the system prone to privacy breaches. Our contribution here is ?SUGGEST, a privacy enhanced recommender system that allows for creating serendipity recommendations without breaching users privacy. We will show that our system does not provide malicious users with any mean to track or detect users activity or preferences.