Personalized online systems for Web search, news recommendation, and e-commerce, are developed. The process of personalization of online systems consists of three main steps: determining a user's needs, classifying products or services, and matching the user's needs with suitable products or services. A multi-feature based method to automatically classify Web pages into categories of topics hierarchically representing the Web pages is proposed. An approach to modeling and quantifying a user’s interests and preferences using the user’s Web navigational data is presented. The approach is based on the premise that frequently visiting certain types of content or Web sites indicates that the user is interested in related content or retrieving information from those sites. This paper approaches a line in which we intend to design a model for integrating users interest based on Collaborative filtering for a personalized recommender system.