Abstract
In this paper, we propose a multimedia recommender system which is based on user profiles enriched with peer-level annotations. Our annotation-based filtering algorithm is able to reduce the effects of two well-known problems inherent to recommender systems: the new user problem and over-specialization. In the first case, we propose a mechanism to enrich new user profiles with concepts gathered from folksonomies. In the second, our system uses genres and/or categories associated to each item in order to accomplish better recommendations. We present the results comparing our approach with other systems previously reported on literature.
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