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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
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
Ferwerda, B., Vall, A., Tkalčič, M., Schedl, M.: Exploring music diversity needs across countries. In: User Modeling, Adaptation and Personalization, Halifax, Canada (2016)
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
Hofstede, G., Hofstede, G.J., Minkov, M.: Cultures and Organizations: Software of the Mind, 3rd edn. McGraw-Hill, New York (2010)
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
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)
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
Schedl, M.: The LFM-1b dataset for music retrieval and recommendation. In: Proceedings of the International Conference on Multimedia Retrieval, USA (2016)
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)
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
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)
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)
Acknowledgments
This research is partially funded by the Austrian Science Fund (FWF) under grant no. P 27530.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Skowron, M., Lemmerich, F., Ferwerda, B., Schedl, M. (2017). Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval. In: Jose, J., 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
Download citation
DOI: https://doi.org/10.1007/978-3-319-56608-5_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56607-8
Online ISBN: 978-3-319-56608-5
eBook Packages: Computer ScienceComputer Science (R0)