Abstract
In recent days, there have been many attempts to automatically recommend music clips that are expected to be liked by a listener. In this paper, we present a novel music recommendation system that automatically gathers listeners’ direct responses about the satisfaction of playing specific two songs one after the other and evolves accordingly for enhanced music recommendation. Our music streaming web service, called “EvoTunes,” is described in detail. Experimental results using the service demonstrate that the success rate of recommendation increases over time through the proposed evolution process.
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
Preview
Unable to display preview. Download preview PDF.
References
Cano, P., Koppenberger, M., Wack, N.: An industrial-strength content-based music recommendation system. In: Proceedings of the 28th ACM SIGIR International Conference on Research and Development in Information Retrieval, p. 673. ACM (2005)
Eck, D., Lamere, P., Bertin-Mahieux, T., Green, S.: Automatic generation of social tags for music recommendation. In: Advances in Neural Information Processing Systems, pp. 385–392 (2007)
Lee, J.H.: Crowdsourcing music similarity judgments using Mechanical Turk. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, pp. 183–188 (2010)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Barrington, L., Oda, R., Lanckriet, G.R.: Smarter than genius? Human evaluation of music recommender systems. In: Proceedings of the International Society for Music Information Retrieval, vol. 9, pp. 357–362 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Choi, JH., Lee, JS. (2014). EvoTunes: Crowdsourcing-Based Music Recommendation. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-04117-9_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04116-2
Online ISBN: 978-3-319-04117-9
eBook Packages: Computer ScienceComputer Science (R0)