An Effective Recommender System for Highly Dynamic and Large Web Sites (Demo Paper) Ranieri Baraglia 1 , Francesco Merlo 2 , and Fabrizio Silvestri 1 1 Istituto di Scienze e Tecnologie dell’Informazione (A. Faedo) ISTI–CNR - Pisa, Italy {ranieri.baraglia,fabrizio.silvestri}@isti.cnr.it 2 LIASES - Laboratorio di Informatica Applicata alle Scienze Economiche e Sociali (G. Rota) Universit´a degli studi di Torino Facolt´a di Economia merlo@econ.unito.it Abstract. In this demo we show a recommender system, called SUG- GEST, that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Usually other recommender systems exploit a kind of two-phase architecture composed by an off-line component that analyzes Web server access logs and gen- erates information used by a successive online component that generates recommendations. SUGGEST collapse the two-phase into a single on- line Apache module. The component is able to manage very large Web sites made up of dinamically generated pages by means of an efficient LRU-based database management strategy. The demo will show the way SUGGEST is able to anticipate users’ requests that will be made farther in the future, introducing a limited overhead on the Web server activity 3 . The continuous and rapid growth of the Web has led to the development of new methods and tools in the Web recommender or personalization domain [1], [2]. Web Mining has shown to be a viable technique to discover information “hidden” into Web-related data. In particular, Web Usage Mining (WUM) is the process of extracting knowledge from Web users access data (or clikstream) by exploiting Data Mining (DM) technologies. It can be used for different purposes such as personalization, system improvement and site modification. In this demo, we present a recommender system, called SUGGEST, which is designed to dynamically generated personalized content of potential interest for users of a Web Site. It is based on an incremental personalization procedure, tightly coupled with the Web server. It is able to update incrementally and automatically the knowledge base obtained from historical usage data and to 3 This work was funded by the Italian MIUR as part of the National Project Legge 449/97, 1999, settore Societ`a dell’Informazione: Technologies and Services for En- hanced Contents Delivery (2002-2004)