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.