• Òscar CelmaEmail author


This chapter presents two implemented prototypes that are related with the main topics presented in the book; music discovery and recommendation. The first system, named, Searchsounds, is a music search engine based on text keyword searches, as well as a more like this button, that allows users to discover music by means of audio similarity. Thus, Searchsounds allows users to dig into the Long Tail, by providing music discovery using audio content-based similarity. The second system, named FOAFing the Music, is a music recommender system that focuses on the Long Tail of popularity, promoting unknown artists. The system also provides related information about the recommended artists, using information available on the web gathered from music related RSS feeds.


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  1. 1.
    I. Knopke, “Aroooga: An audio search engine for the world wide web,” in Proceedings of 5th International Conference on Music Information Retrieval, (Barcelona, Spain), 2004.Google Scholar
  2. 2.
    R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Boston, MA: Addison-Wesley, 1st edn., 1999.Google Scholar
  3. 3.
    S. Vembu and S. Baumann, “A self-organizing map based knowledge discovery for music recommendation systems,” in Proceedings of the 2nd International Symposium on Computer Music Modeling and Retrieval, (Esbjerg, Denmark), 2004.Google Scholar
  4. 4.
    M. F. Porter, “An algorithm for suffix stripping,” Program, vol. 14, pp. 130–137, 1980.Google Scholar
  5. 5.
    P. Cano, M. Koppenberger, and N. Wack, “An industrial-strength content-based music recommendation system,” in Proceedings of 28th International ACM SIGIR Conference, (Salvador, Brazil), 2005.Google Scholar
  6. 6.
    M. Sordo, C. Laurier, and O. Celma, “Annotating music collections how content-based similarity helps to propagate labels,” in Proceedings of the 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  7. 7.
    J. Golbeck and B. Parsia, “Trust network-based filtering of aggregated claims,” International Journal of Metadata, Semantics and Ontologies, vol. 1, no. 1, 2005.Google Scholar
  8. 8.
    J. Golbeck, Computing and Applying Trust in Web-based Social Networks. PhD thesis, College Park, MD, 2005.Google Scholar
  9. 9.
    E. Perik, B. de Ruyter, P. Markopoulos, and B. Eggen, “The sensitivities of user profile information in music recommender systems,” in Proceedings of Private, Security, Trust, 2004.Google Scholar
  10. 10.
    T. R. Gruber, “Towards principles for the design of ontologies used for knowledge sharing,” in Formal Ontology in Conceptual Analysis and Knowledge Representation (N. Guarino and R. Poli, eds.). Deventer, The Netherlands: Kluwer Academic Publishers, 1993.Google Scholar
  11. 11.
    R. Garcia and O. Celma, “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
  12. 12.
    Y. Raimond, S. A. Abdallah, M. Sandler, and F. Giasson, “The music ontology,” in Proceedings of the 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.BMATBarcelonaSpain

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