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Leveraging Microblogs for Spatiotemporal Music Information Retrieval

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Advances in Information Retrieval (ECIR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7814))

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Abstract

We present results of text data mining experiments for music retrieval, analyzing microblogs gathered from November 2011 to September 2012 to infer music listening patterns all around the world. We assess relationships between particular music preferences and spatial properties, such as month, weekday, and country, and the temporal stability of listening activities. The findings of our study will help improve music retrieval and recommendation systems in that it will allow to incorporate geospatial and cultural information into models for music retrieval, which has not been looked into before.

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References

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Author information

Authors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Linz, Austria

    Markus Schedl

Authors
  1. Markus Schedl
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Editor information

Editors and Affiliations

  1. Yandex, Leo Tolstoy, 16, 119021, Moscow, Russia

    Pavel Serdyukov  & Ilya Segalovich  & 

  2. Kontur Labs and Ural Federal University, Fonvizina 3-27, 620078, Yekaterinburg, Russia

    Pavel Braslavski

  3. National Research University Higher School of Economics (HSE), Pokrovskii bd 11, 109028, Moscow, Russia

    Sergei O. Kuznetsov

  4. University of Amsterdam, Turfdraagsterpad 9, 1012 XT, Amsterdam, The Netherlands

    Jaap Kamps

  5. Knowledge Media Institute, The Open University, Walton Hall, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  6. Mathematics & Computer Science Department, Emory University, 400 dowman Drive, 30329, Atlanta, GA, USA

    Eugene Agichtein

  7. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    Emine Yilmaz

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© 2013 Springer-Verlag Berlin Heidelberg

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Schedl, M. (2013). Leveraging Microblogs for Spatiotemporal Music Information Retrieval. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_87

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  • DOI: https://doi.org/10.1007/978-3-642-36973-5_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36972-8

  • Online ISBN: 978-3-642-36973-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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