Fab: content-based, collaborative recommendation
Communications of the ACM
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
IEEE Transactions on Knowledge and Data Engineering
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
User profiles for personalized information access
The adaptive web
The Knowledge Engineering Review
Creating peer-level video annotations for web-based multimedia
EGMM'04 Proceedings of the Seventh Eurographics conference on Multimedia
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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.