Abstract
In the last few years, recommender systems have gained significant attention in the research community, due to the increasing availability of huge data collections, such as news archives, shopping catalogs, or virtual museums. In this scenario, there is a pressing need for applications to provide users with targeted suggestions to help them navigate this ocean of information. However, no much effort has yet been devoted to recommenders in the field of multimedia databases. In this paper, we propose a novel approach to recommendation in multimedia browsing systems, based on an importance ranking method that strongly resembles the well known PageRank ranking system. We model recommendation as a social choice problem, and propose a method that computes customized recommendations by originally combing intrinsic features of multimedia objects, past behavior of individual users and overall behavior of the entire community of users. We implemented a prototype of the proposed system and preliminary experiments have shown that our approach is promising.