Dynamic personalization of Web sites without user intervention

Web Personalization (WP) or recommender systems are typical applications of Web Usage Mining (WUM), which are introduced to improve Web site usage by customizing the contents of a Web site with respect to the users' need. They provide mechanisms that collect information describing user activity and elaborate this information in order to extract a user profile based on their navigational behaviors that can be employed to provide personalized navigational information. The WUM-based personalization process is typically structured according to three phases consisting Preprocessing, Pattern Discovery, and Pattern Analysis. SUGGEST is a novel solution to implement WP as a single online module that performs user profiling, model updating, and recommendation building. It is designed to dynamically generate personalized contents of potential interest for users of large Web sites made up of pages dynamically generated. It is able to provide significant suggestions and good system performance.