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Hey! Ho! Let’s Go! Explanatory Music Recommendations with dbrec

  • Alexandre Passant
  • Stefan Decker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6089)

Abstract

In this demo paper, we present dbrec ( http://dbrec.net ), a music recommendation system using Linked Data, where recommendation are computed from DBpedia using an algorithm for Linked Data Semantic Distance (LDSD). We describe how the system can be used to get recommendations for approximately 40,000 artists and bands, and in particular how it provides explanatory recommendations to the end-user. In addition, we discuss the research background of dbrec, including the LDSD algorithm and its related ontology.

Keywords

Link Data SPARQL Query Research Background Related Ontology Link Open Data 
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.

References

  1. 1.
    Berners-Lee, T.: Linked Data. In: Design Issues for the World Wide Web, World Wide Web Consortium (2006), http://www.w3.org/DesignIssues/LinkedData.html
  2. 2.
    Jacobson, K., Raimond, Y., Sandler, M.: An Ecosystem for Transparent Music Similarity in an Open World. In: International Symposium on Music Information Retrieval (2009)Google Scholar
  3. 3.
    Passant, A.: Measuring Semantic Distance on Linking Data and Using it for Resources Recommendations. In: Linked AI: AAAI Spring Symposium Linked Data Meets Artificial Intelligence. AIII (2010)Google Scholar
  4. 4.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19, 17–30 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexandre Passant
    • 1
  • Stefan Decker
    • 1
  1. 1.Digital Enterprise Research InstituteNational University of IrelandGalway

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