Web Personalization based on Usage Mining

Personalized or recommender systems are a particular type of information filtering applications. User profiles, representing the information needs and preferences of users, can be inferred from log or clickthrough data, or the ratings that users provide on information items, through their interactions with a system. Such user profiles have been used, for example in iGoogle, to provide personalized recommendations to the users. A user model is a representation of this profile, which can be obtained implicitly through the application of web usage mining techniques. Our work aims to develop Web usage mining tasks to model an intranet or local Web site recommender system. We will focus on the users activity on a university Web site, to customize the contents and structure the presentation of a Web site according to the preferences derived from the user’s activity. The customization is based on an individual’s user profile as well as a profile representing the collective interest of the entire user community, in this case all users accessing the Web site. The outcome will be personalized recommendations and presentation of a Web site with respect to the user’s needs.