A Web personalization system based on a neuro-fuzzy strategy

In this paper we investigate the use of a neuro-fuzzy strategy to develop a Web personalization system that dynamically suggests interesting URLs for the current user. As a preliminary step, user access logs are analyzed to identify user sessions. Then, groups of users which exhibit a common browser behavior (i.e. user profiles) are discovered by applying a fuzzy clustering algorithm to the user sessions. Finally, a knowledge extraction process is carried out to derive associations between user profiles and relevant Web pages to be suggested to users. In particular, a hybrid approach based on the combination of the fuzzy reasoning and the connectionist paradigm is proposed in order to derive knowledge from session data and represent it in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used by an online recommendation module to dynamically suggest links to Web pages judged interesting for the current user.