A competitive learning strategy for adapting fuzzy user profiles

In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles a competitive learning process in which the user profile is continuously updated in order to make its components as similar as possible to the description of the accessed items. On the same time a mechanism to forget outdated user preferences is proposed in order to describe changes in user interests over time. The strategy was applied on the MovieLens dataset and the obtained results show its effectiveness to learn user profiles reflecting the current preferences of users.