Automatic personalization based on Web usage mining

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 is usually 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 active requests 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...

• Type: Journal
• Year: 2000
• Where: CACM
• Authors: Bamshad Mobasher, Robert Cooley, Jaideep Srivastava
• Keywords: CACM 2000, Server Normal Operations, Web Mining, Web Server