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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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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.

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Acknowledgments

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

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Correspondence to Marcin Skowron .

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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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  • Publisher Name: Springer, Cham

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