Ontology-based music recommender system



Publication Details

Rodríguez-García, M., Colombo-Mendoza, L. Omar., Valencia-Garcia, R., Lopez Lorca, A. A. & Beydoun, G. (2015). Ontology-based music recommender system. In S. Omatu, Q. M. Malluhi, S. Rodriguez Gonzalez, G. Bocewicz, E. Bucciarelli, G. Giulioni & F. Iqba (Eds.), Distributed Computing and Artificial Intelligence, 12th International Conference (pp. 39-46). Switzerland: Springer International Publishing.


Recommender systems are modern applications that make suggestions to their users on a variety of items taking into account their preferences in many domains. These systems use people's opinions to recommend to their end users items that are likely to be of their interest. They are designed to help users to decide on appropriate items and facilitate finding them in a very large collection of items. Traditional syntactic-based recommender systems suffer from several disadvantages, such as polysemy or synonymy, that limit its effectiveness. Semantic technologies provide a consistent and reliable basis for dealing with data at knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a recommender system based on a Music ontology is presented. A preliminary evaluation of the system shows promising results.

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