Personalization on the Net using Web mining: introduction
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
ACM SIGKDD Explorations Newsletter
Privacy Risks in Recommender Systems
IEEE Internet Computing
An Online Recommender System for Large Web Sites
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
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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.