On Weighted Hybrid Track Recommendations

  • Simon Franz
  • Thomas Hornung
  • Cai-Nicolas Ziegler
  • Martin Przyjaciel-Zablocki
  • Alexander Schätzle
  • Georg Lausen
Conference paper

DOI: 10.1007/978-3-642-39200-9_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7977)
Cite this paper as:
Franz S., Hornung T., Ziegler CN., Przyjaciel-Zablocki M., Schätzle A., Lausen G. (2013) On Weighted Hybrid Track Recommendations. In: Daniel F., Dolog P., Li Q. (eds) Web Engineering. ICWE 2013. Lecture Notes in Computer Science, vol 7977. Springer, Berlin, Heidelberg

Abstract

Music is a highly subjective domain, which makes it a challenging research area for recommender systems. In this paper, we present our TRecS (Track Recommender System) prototype, a hybrid recommender that blends three different recommender techniques into one score. Since traceability is an important issue for the acceptance of recommender systems by users, we have implemented a detailed explanation feature that supports transparency about the contribution of each sub-recommender for the overall result. To avoid overspecialization, TRecS peppers the result list with recommendations that are based on a serendipity metric. This way, users can benefit from both recommendations aligned with their current taste while gaining some diversification.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simon Franz
    • 1
  • Thomas Hornung
    • 1
  • Cai-Nicolas Ziegler
    • 2
  • Martin Przyjaciel-Zablocki
    • 1
  • Alexander Schätzle
    • 1
  • Georg Lausen
    • 1
  1. 1.Institute of Computer ScienceAlbert-Ludwigs-Universität FreiburgGermany
  2. 2.American Express, PAYBACK GmbHMünchenGermany

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