Learning usage patterns for personalized information access in e-commerce

Abstract

The World Wide Web is a vast repository of information, much of which is valuable but very often hidden to the user. Currently, Web personalization is the most promising approach to remedy this problem, and Web usage mining, is considered a crucial component of any effective Web personalization system. Web usage mining techniques such as clustering and association rules, which rely on offline pattern discovery from user transactions, can be used to improve searching in the Web. We present the Profile Extractor, a personalization component based on machine learning techniques, which allows for the discovery of preferences and interests of users that have access to a Web site. More specifically, we present the module that exploits unsupervised learning techniques for the creation of communities of users and usage patterns applied to customers of an on-line bookshop. To support our work, we have performed several experiments and discussed the results. © Springer-Verlag 2004.