Abstract
One of the most important challenge in the information access field is information overload. To cope with this problem, in this paper, we present a strategy for a semantic multimedia recommender system that computes customized recommendations using semantic contents and low-level features of multimedia objects, past behavior of individual users and behavior of the users' community as a whole. We have implemented a recommender prototype for browsing the Uffizi Gallery digital picture collection. Then, we investigated the effectiveness of the proposed approach, based on the users satisfaction. The obtained preliminary experimental results show that our approach is quite promising and encourages further research in this direction.