Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval

  • Marcin Skowron
  • Florian Lemmerich
  • Bruce Ferwerda
  • Markus Schedl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10193)

Abstract

In absence of individual user information, knowledge about larger user groups (e.g., country characteristics) can be exploited for deriving user preferences in order to provide recommendations to users. In this short paper, we study how to mitigate the cold-start problem on a country level for music retrieval. Specifically, we investigate a large-scale dataset on user listening behavior and show that we can reduce the error for predicting the popularity of genres in a country by about 16.4% over a baseline model using cultural and socio-economics indicators.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marcin Skowron
    • 1
  • Florian Lemmerich
    • 2
  • Bruce Ferwerda
    • 3
  • Markus Schedl
    • 3
  1. 1.Austrian Research Institute for Artificial IntelligenceViennaAustria
  2. 2.GESIS - Leibniz Institute for the Social SciencesUniversity of Koblenz-LandauMainzGermany
  3. 3.Johannes Kepler UniversityLinzAustria

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