  The wide development of mobile applications provides a considerable amount of
data of all types (images, texts, sounds, videos, etc.). Thus, two main issues
have to be considered: assist users in finding information and reduce search
and navigation time. In this sense, context-based recommender systems (CBRS)
propose the user the adequate information depending on her/his situation. Our
work consists in applying machine learning techniques and reasoning process in
order to bring a solution to some of the problems concerning the acceptance of
recommender systems by users, namely avoiding the intervention of experts,
reducing cold start problem, speeding learning process and adapting to the
user's interest. To achieve this goal, we propose a fundamental modification in
terms of how we model the learning of the CBRS. Inspired by models of human
reasoning developed in robotic, we combine reinforcement learning and
case-based reasoning to define a contextual recommendation process based on
different context dimensions (cognitive, social, temporal, geographic). This
paper describes an ongoing work on the implementation of a CBRS based on a
hybrid Q-learning (HyQL) algorithm which combines Q-learning, collaborative
filtering and case-based reasoning techniques. It also presents preliminary
results by comparing HyQL and the standard Q-Learning w.r.t. solving the cold
start problem.
