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European Conference on Information Retrieval

ECIR 2017: Advances in Information Retrieval pp 561–567Cite as

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

Part of the Lecture Notes in Computer Science book series (LNISA,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.

Keywords

  • 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.

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  • DOI: 10.1007/978-3-319-56608-5_49
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Notes

  1. 1.

    http://www.cp.jku.at/datasets/LFM-1b.

  2. 2.

    http://www.last.fm/api/show/artist.getTopTags.

  3. 3.

    http://www.allmusic.com.

  4. 4.

    https://geert-hofstede.com/countries.html.

  5. 5.

    http://qog.pol.gu.se/data/datadownloads/qogbasicdata.

References

  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 2014

    Google Scholar 

  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 2016

    Google Scholar 

  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. 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 2013

    Google Scholar 

  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. 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 2012

    Google Scholar 

  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. 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 2011

    Google Scholar 

  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. 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. 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 2014

    Google Scholar 

  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. 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 

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Acknowledgments

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

Author information

Authors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Vienna, Austria

    Marcin Skowron

  2. GESIS - Leibniz Institute for the Social Sciences, University of Koblenz-Landau, Mainz, Germany

    Florian Lemmerich

  3. Johannes Kepler University, Linz, Austria

    Bruce Ferwerda & Markus Schedl

Authors
  1. Marcin Skowron
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  2. Florian Lemmerich
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  3. Bruce Ferwerda
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  4. Markus Schedl
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Corresponding author

Correspondence to Marcin Skowron .

Editor information

Editors and Affiliations

  1. University of Glasgow , Glasgow, United Kingdom

    Joemon M Jose

  2. TU Delft - EWI/ST/WIS , Delft, The Netherlands

    Claudia Hauff

  3. Middle East Technical University , Ankara, Turkey

    Ismail Sengor Altıngovde

  4. Open University , Milton Keynes, United Kingdom

    Dawei Song

  5. Signal Media , London, United Kingdom

    Dyaa Albakour

  6. Toronto, Canada

    Stuart Watt

  7. JohnTait.net Ltd. and BCS IRSG , Sunderland, United Kingdom

    John Tait

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Skowron, M., Lemmerich, F., Ferwerda, B., Schedl, M. (2017). Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval. In: , et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_49

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  • DOI: https://doi.org/10.1007/978-3-319-56608-5_49

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