Personalized Web recommendations: supporting epistemic information about end-users

The online recommendations are a popular presence in the Web sites world due to their potential to increase the customers´ satisfaction. The ability to represent epistemic information about the clients´ beliefs is important to understand their needs. This paper presents a recommender system based on reinforcement learning. The system represents concepts presented on a Web site by epistemic logical programs and uses a similarity measure between programs in order to facilitate generalization. A prototype of this system and experiments are presented.