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.