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EvoTunes: Crowdsourcing-Based Music Recommendation

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8326)

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.

Keywords

  • music streaming
  • recommendation
  • crowdsourcing

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  • DOI: 10.1007/978-3-319-04117-9_32
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References

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© 2014 Springer International Publishing Switzerland

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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

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  • 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)