Automatic web content personalization through reinforcement learning

This paper deals with the automatic adaptation of Web contents. It is recognized that quite often users need some personalized adaptations to access Web contents. This is more evident when we focus on people with some accessibility needs. Based on the user profile, it is possible to transcode or modify contents (e.g., adapt text fonts) so as to meet the user preferences. The problem is that applying such a kind of transformations to the whole content might significantly alter Web pages that might become unreadable, hence making matters worse. We present a system that employs Web intelligence to perform automatic adaptations on single elements composing a Web page. A reinforcement learning algorithm is utilized to manage user profiles. We evaluate our system through simulation and a real assessment where elderly users where asked to use for a time period our system prototype. Results confirm the feasibility of the proposal.