PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Baraglia R., Merlo F., Silvestri F. An Effective Recommender System for Highly Dynamic and Large Web Sites. In: 15th European Conference on Machine Learning (ECML) and the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (ECML/PHDD) (Pisa, Italy, 20-24 September 2004).
 
 
Abstract
(English)
In this demo we show a recommender system, called SUGGEST, 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 o -line component that analyzes Web server access logs and generates information used by a successive online component that generates recommendations. SUGGEST collapse the two-phase into a single online Apache module. The component is able to manage very large Web sites made up of dinamically generated pages by means of an e cient 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
Subject Web Mining
Web Usage Mining
Recommender Systems
D.2.2 Design Tools and Techniques
H.3.5 Online Information Systems


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