This paper presents a method to classify the web user’s navigation patterns automatically. The proposed model of this paper classifies user’s navigation patterns and predicts his/her upcoming requirements. To create users’ profile, a new method is introduced by recording user’s settings active and user’s similarity measurement with neighboring users. The proposed model is capable of creating the profile implicitly. Besides, it updates the profile based on created changes. In fact, we try to improve the function of recommender engine using user’s navigation patterns and clustering. The method is based on user’s navigation patterns and is able to present the result of recommender engine based on user’s requirement and interest. In addition, this method has the ability to help customize websites, more efficiently.

One of existing challenges in personalization of the web is increasing the efficiency of a web in meeting the users' requirements for the contents they require in an optimal state. All the information associated with the current user behavior following in web and data obtained from pervious users’ interaction in web can provide some necessary keys to recommend presentation of services, productions, and the required information of the users. This study aims at presenting a formal model based on colored Petri nets to identify the present user's interest, which is utilized to recommend the most appropriate pages ahead. In the proposed design, recommendation of the pages is considered with respect to information obtained from pervious users' profile as well as the current session of the present user. This model offers the updated proposed pages to the user by clicking on the web pages. Moreover, an example of web is modeled using CPN Tools. The results of the simulation show that this design improves the precision factor. We explain, through evaluation where the results of this method are more objective and the dynamic recommendations demonstrate that the results of the recommended method improve the precision criterion 15% more than the static method.