A neuro-fuzzy collaborative filtering approach for Web recommendation

Due to the growing variety and quantity of information available on the Web, there is urgent need for developing web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web recommendation, that finds in Soft Computing techniques a valid tool to handle uncertainty in web usage data and develop web-based applications tailored on users preferences. In this context, we propose a neuro-fuzzy strategy that combines soft computing techniques to develop a Web recommendation 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 know-ledge from session data and represent it in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest links to Web pages judged interesting for the current user.