Fitted Q-iteration and functional networks for ubiquitous recommender systems

Ubiquitous recommender systems facilitate users on-location by personalized recommendations of items in the proximity via mobile devices. Due to a high variability of situations and preferences, an efficient resource processing is needed in order to assist the user in a proper way. In this paper, we consider a recommender system, able to learn preferences/habits of users through contextual information, such as location and time, using a new offline model-free approximate Q-iteration. Following the basic idea of Fitted Q-Iteration, the paper focuses on a computational scheme, based on functional networks, and that, unlike the well-known neural ones, does not require a large number of training samples. A preliminary case study, which deals with a shopping mall, is useful to show the approximation capabilities of the proposed approach.