In recent years, an increasing interest in recommendation systems has emerged both from the research and the application point of view and in both academic and commercial domains. The majority of comparison techniques used for formulating recommendations are based on set-operations over user-supplied terms or internal product computations on vectors encoding user preferences.

This paper proposes a recommendation algorithm based on user profiles and their dynamic adjustment according to user behavior, as well as dynamic management of communities, which contain "similar" and "relevant" users and which are created according to a classification algorithm. The algorithm is implemented on top of a community management mechanism.