Learning from Users for a Better and Personalized Web Experience

The Internet has grown into a sophisticated set of resources providing an ever-increasing amount of information leaving users to face information overload coupled with problems of successful information retrieval. Search engines can alleviate the problem to some extent; however they are unsuitable for web sites optimization and cannot tackle the problem of recognizing users' interest domain and thus unable to deliver personalized web experience. Adaptive personalized web on the other hand allows deliver web pages accordingly to visitors' interest domains by taking advantage of systems recognizing users' intentions and modeling user and their interest profiles. In this paper we concentrate on improving visitors' web experience by modeling an anonymous web user. The latter is the main distinction of our work compared to available related studies.