PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Silvestri F., Baraglia R., Palmerini P. On-line Generation of Suggestions for Web Users. The document has been submitted to Journal: JDIM, Technical report, 2004.
 
 
Abstract
(English)
One important class of Data Mining applications is the so-called "Web Mining" that analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems.These systems are aimed to extract knowledge from the analysis of historical information of a web server in order to improve the web site expressiveness in terms of readability and content availability. Typically, these systems are made up of two components. One, that isusually executed off-line with respect to the Web server normal operations, analyzes the server access logs in order to find a suitable categorization of users, the other, that is usually executed on-line with respect to the Web server normal operations, classifies the activerequests according to the previous off-line analysis. In this paper we propose SUGGEST 2.0 a recommender system that, differently from those proposed so far, does not make use of any off-line component. Moreover, in the last part of the paper, we analyze the quality of thesuggestions generated and the performance of our solution. To this purpose we also introduce a novel quality metric that tries to estimate the effectiveness of a recommender system as the capacity to anticipate user requests that could be issued farther in the future.
Subject Recommender System, Streaming Algorithm, Graph Partitioning
H.3.4 Systems and Software


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