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A bipartite fitness model for online music streaming services

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  • Pongnumkul, Suchit
  • Motohashi, Kazuyuki

Abstract

This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

Suggested Citation

  • Pongnumkul, Suchit & Motohashi, Kazuyuki, 2018. "A bipartite fitness model for online music streaming services," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1125-1137.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1125-1137
    DOI: 10.1016/j.physa.2017.08.108
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    References listed on IDEAS

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    1. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    2. Zhang, Chu-Xu & Zhang, Zi-Ke & Liu, Chuang, 2013. "An evolving model of online bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6100-6106.
    3. Hu, Hai-Bo & Han, Ding-Yi, 2008. "Empirical analysis of individual popularity and activity on an online music service system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(23), pages 5916-5921.
    4. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    5. Yanbo Zhou & An Zeng & Wei-Hong Wang, 2015. "Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-10, March.
    6. Thomes, Tim Paul, 2013. "An economic analysis of online streaming music services," Information Economics and Policy, Elsevier, vol. 25(2), pages 81-91.
    7. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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    Cited by:

    1. Chandra, Anita & Garg, Himanshu & Maiti, Abyayananda, 2019. "A general growth model for online emerging user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 370-384.
    2. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    3. Wang, Pengyu & Fang, Debin & Cao, GangCheng, 2022. "How social learning affects customer behavior under the implementation of TOU in the electricity retailing market," Energy Economics, Elsevier, vol. 106(C).

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