Dynamic link suggestion by a neuro-fuzzy web recommendation system

In this paper we explore the use of a neuro-fuzzy strategy to develop a Web personalization system that dynamically suggests interesting URLs for the current user according to a collaborative filtering approach. 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 hybrid approach based on the combination of the fuzzy reasoning and the connectionist paradigm is applied in order to derive fuzzy associations between user profiles and relevant Web pages to be suggested to users. The derived knowledge is ultimately used by an online recommendation module to dynamically suggest links to Web pages judged interesting for the current user. Some preliminary experimental results are presented on a real life web log dataset.