A fuzzy recommender system for dynamic prediction of user's behavior

Analyzing and predicting navigational behavior of Web users can lead to more user friendly and efficient websites which is an important issue in Electronic Commerce. Web personalization is a common way for adapting the content of a website to the needs of each specific user. In this work, a model for dynamic recommendation based on fuzzy clustering techniques, applicable to currently on-line users is proposed. The model concentrates on both aspects of web content mining and web usage mining. Applying fuzzy web mining techniques, the model infers the user's preferences from IIS web server's access logs. The fuzzy clustering approach, in this study, provides the possibility of capturing the uncertainty among Web user's behaviors. The model is implemented and tested as a recommender system for personalizing website of “Information and Communication Technology Center” of Isfahan municipality in Iran. The results shown are promising and proved that integrating fuzzy approach provide us with more interesting and useful patterns which consequently making the recommender system more functional and robust.