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

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


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.


Random Forest Power Distance Uncertainty Avoidance Music Genre Music Preference 
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.



This research is partially funded by the Austrian Science Fund (FWF) under grant no. P 27530.


  1. 1.
    Cheng, Z., Shen, J.: Just-for-me: an adaptive personalization system for location-aware social music recommendation. In: Proceedings of the 2014 ACM International Conference on Multimedia Retrieval, Glasgow, UK, April 2014Google Scholar
  2. 2.
    Ferwerda, B., Schedl, M.: Investigating the relationship between diversity in music consumption behavior and cultural dimensions: a cross-country analysis. In: Workshop on S, Halifax, Canada, July 2016Google Scholar
  3. 3.
    Ferwerda, B., Vall, A., Tkalčič, M., Schedl, M.: Exploring music diversity needs across countries. In: User Modeling, Adaptation and Personalization, Halifax, Canada (2016)Google Scholar
  4. 4.
    Hauger, D., Schedl, M., Košir, A., Tkalčič, M.: The million musical tweets dataset: what can we learn from microblogs. In: Proceedings of the 14th International Society for Music Information Retrieval Conference, Brazil, November 2013Google Scholar
  5. 5.
    Hofstede, G., Hofstede, G.J., Minkov, M.: Cultures and Organizations: Software of the Mind, 3rd edn. McGraw-Hill, New York (2010)Google Scholar
  6. 6.
    Hu, X., Lee, J.H.: A cross-cultural study of music mood perception between American and Chinese listeners. In: Proceedings of the 13th International Society for Music Information Retrieval Conference, Porto, Portugal, October 2012Google Scholar
  7. 7.
    Hu, X., Yang, Y.H.: Cross-dataset and cross-cultural music mood prediction: a case on Western and Chinese pop songs. IEEE Trans. Affect. Comput. (99) (2016)Google Scholar
  8. 8.
    Hu, Y., Ogihara, M.: NextOne player: A music recommendation system based on user behavior. In: Proceedings of the 12th International Society for Music Information Retrieval Conference, Miami, FL, USA, October 2011Google Scholar
  9. 9.
    Schedl, M.: The LFM-1b dataset for music retrieval and recommendation. In: Proceedings of the International Conference on Multimedia Retrieval, USA (2016)Google Scholar
  10. 10.
    Schedl, M., Stober, S., Gómez, E., Orio, N., Liem, C.C.: User-aware music retrieval. In: Müller, M., Goto, M., Schedl, M. (eds.) Multimodal Music Processing. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, Germany (2012)Google Scholar
  11. 11.
    Singhi, A., Brown, D.G.: On cultural, textual and experiential aspects of music mood. In: Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), Taipei, Taiwan, October 2014Google Scholar
  12. 12.
    Teorell, J., Dahlberg, S., Holmberg, S., Rothstein, B., Khomenko, A., Svensson, R.: The Quality of Government Standard Dataset, version Jan16. University of Gothenburg, The Quality of Government Institute (2016)Google Scholar
  13. 13.
    Wang, J.-C., Yang, Y.-H., Wang, H.-M.: Affective music information retrieval. In: Tkalčič, M., De Carolis, B., de Gemmis, M., Odić, A., Košir, A. (eds.) Emotions and Personality in Personalized Services, pp. 227–261. Springer, Heidelberg (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marcin Skowron
    • 1
    Email author
  • 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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