On the Influence of User Characteristics on Music Recommendation Algorithms

  • Markus Schedl
  • David Hauger
  • Katayoun Farrahi
  • Marko Tkalčič
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

We investigate a range of music recommendation algorithm combinations, score aggregation functions, normalization techniques, and late fusion techniques on approximately 200 million listening events collected through Last.fm. The overall goal is to identify superior combinations for the task of artist recommendation. Hypothesizing that user characteristics influence performance on these algorithmic combinations, we consider specific user groups determined by age, gender, country, and preferred genre. Overall, we find that the performance of music recommendation algorithms highly depends on user characteristics.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Markus Schedl
    • 1
  • David Hauger
    • 1
  • Katayoun Farrahi
    • 2
  • Marko Tkalčič
    • 1
  1. 1.Department of Computational PerceptionJohannes Kepler UniversityLinzAustria
  2. 2.Department of ComputingGoldsmith’s University of LondonUK

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