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Social Knowledge-Driven Music Hit Prediction

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Advanced Data Mining and Applications (ADMA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

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Abstract

What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users’ music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches.

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Author information

Authors and Affiliations

  1. L3S Research Center, Leibniz Universität Hannover, Appelstrasse 9a, 30167, Hannover, Germany

    Kerstin Bischoff, Claudiu S. Firan, Mihai Georgescu, Wolfgang Nejdl & Raluca Paiu

Authors
  1. Kerstin Bischoff
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  2. Claudiu S. Firan
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  3. Mihai Georgescu
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  4. Wolfgang Nejdl
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  5. Raluca Paiu
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Editor information

Editors and Affiliations

  1. Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Xinjiekouwai Ave. 19, 100875, Beijing, China

    Ronghuai Huang

  2. The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong, Hong Kong

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  4. Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João Gama

  5. School of Information, Zhongguancum, Renmin University, 100872, Beijing, China

    Xiaofeng Meng

  6. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, St. Lucia, Queensland, Australia

    Xue Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Bischoff, K., Firan, C.S., Georgescu, M., Nejdl, W., Paiu, R. (2009). Social Knowledge-Driven Music Hit Prediction. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-03348-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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