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 recommender systems use Web Usage Mining (WUM) techniques in order to automatically extract knowledge from Web usage 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 πSUGGEST, 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. Categories and Subject Descriptors K.4.1 [Public Policy Issues]: Privacy; I.5.1 [Models]: Statistical General Terms Algorithms, Security Keywords Web Recommen...

• Type: Conference
• Year: 2006
• Where: SAC
• Authors: Ranieri Baraglia, Claudio Lucchese, Salvatore Orlando, Massimo Serranó, Fabrizio Silvestri
• Keywords: Applied Computing, Privacy Enhanced Recommender, Recommender Systems, SAC 2006, Traditional Recommender Systems