Foafing the Music: Bridging the Semantic Gap in Music Recommendation

  • Òscar Celma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4273)


In this paper we give an overview of the Foafing the Music system. The system uses the Friend of a Friend (FOAF) and RDF Site Summary (RSS) vocabularies for recommending music to a user, depending on the user’s musical tastes and listening habits. Music information (new album releases, podcast sessions, audio from MP3 blogs, related artists’ news and upcoming gigs) is gathered from thousands of RSS feeds.

The presented system provides music discovery by means of: user profiling (defined in the user’s FOAF description), context based information (extracted from music related RSS feeds) and content based descriptions (extracted from the audio itself), based on a common ontology (OWL DL) that describes the music domain.

The system is available at:


Collaborative Filter Common Ontology Music Content Music System Music Recommendation System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Celma, O., Cano, P., Herrera, P.: Search sounds: An audio crawler focused on weblogs. In: Proceedings of 7th International Conference on Music Information Retrieval, Victoria, Canada (2006)Google Scholar
  2. 2.
    Garcia, R., Celma, O.: Semantic integration and retrieval of multimedia metadata. In: Proceedings of 4th International Semantic Web Conference. Knowledge Markup and Semantic Annotation Workshop, Galway, Ireland (2005)Google Scholar
  3. 3.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  4. 4.
    Linden, G., Smith, B., York, J.: recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 4(1) (2003)Google Scholar
  5. 5.
    Pachet, F.: Knowledge Management and Musical Metadata. Idea Group (2005)Google Scholar
  6. 6.
    Perik, E., de Ruyter, B., Markopoulos, P., Eggen, B.: The sensitivities of user profile information in music recommender systems. In: Proceedings of Private, Security, Trust (2004)Google Scholar
  7. 7.
    Uitdenbogerd, A., van Schnydel, R.: A review of factors affecting music recommender success. In: Proceedings of 3rd International Conference on Music Information Retrieval, Paris, France (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Òscar Celma
    • 1
  1. 1.Music Technology GroupUniversitat Pompeu FabraBarcelonaSpain

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