Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

Online music services are increasing in popularity. They enable us to analyze people’s music listening behavior based on play logs. Although it is known that people listen to music based on topic (e.g., rock or jazz), we assume that when a user is addicted to an artist, s/he chooses the artist’s songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model to analyze people’s music listening behavior. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling music listening behavior by taking into account the influence of addiction to artists. Second, by using real-world datasets of play logs, we showed the effectiveness of our proposed model. Third, we carried out qualitative experiments and showed that taking addiction into account enables us to analyze music listening behavior from a new viewpoint in terms of how people listen to music according to the time of day, how an artist’s songs are listened to by people, etc. We also discuss the possibility of applying the analysis results to applications such as artist similarity computation and song recommendation.

References

  1. 1.
    Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)MATHGoogle Scholar
  2. 2.
    Baur, D., Büttgen, J., Butz, A.: Listening factors: a large-scale principal components analysis of long-term music listening histories. In: CHI, pp. 1273–1276 (2012)Google Scholar
  3. 3.
    Berkers, P.: Gendered scrobbling: listening behavior of young adults on last.fm. Interact. Stud. Commun. Culture 2(3), 279–296 (2012)CrossRefGoogle Scholar
  4. 4.
    Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative filtering by using temporal context. In: ICTAI, pp. 783–788 (2013)Google Scholar
  5. 5.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. PNAS 101(Suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  6. 6.
    Herrera, P., Resa, Z., Sordo, M.: Rocking around the clock eight days a week: an exploration of temporal patterns of music listening. In: WOMRAD, pp. 7–10 (2010)Google Scholar
  7. 7.
    Iwata, T., Watanabe, S., Yamada, T., Ueda, N.: Topic tracking model for analyzing consumer purchase behavior. In: IJCAI, pp. 1427–1432 (2009)Google Scholar
  8. 8.
    Kamalzadeh, M., Baur, D., Möller, T.: A survey on music listening and management behaviours. In: ISMIR, pp. 299–305 (2012)Google Scholar
  9. 9.
    Kenmochi, H., Ohshita, H.: Vocaloid - commercial singing synthesizer based on sample concatenation. In: INTERSPEECH, pp. 4009–4010 (2007)Google Scholar
  10. 10.
    Lee, J.H., Kim, Y.-S., Hubbles, C.: A look at the cloud from both sides now: an analysis of cloud music service usage. In: ISMIR, pp. 299–305 (2016)Google Scholar
  11. 11.
    Liu, N.-H., Hsieh, S.-J., Tsai, C.-F.: An intelligent music playlist generator based on the time parameter with artificial neural networks. Expert Syst. Appl. 37(4), 2815–2825 (2010)CrossRefGoogle Scholar
  12. 12.
    Park, C.H., Kahng, M.: Temporal dynamics in music listening behavior: a case study of online music service. In: ACIS-ICIS, pp. 573–578 (2010)Google Scholar
  13. 13.
    Park, S.E., Lee, S., Lee, S.G.: Session-based collaborative filtering for predicting the next song. In: CNSI, pp. 353–358 (2011)Google Scholar
  14. 14.
    Rentfrow, P., Gosling, S.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236–1256 (2003)CrossRefGoogle Scholar
  15. 15.
    Reynolds, G., Barry, D., Burke, T., Coyle, E.: Interacting with large music collections: towards the use of environmental metadata. In: ICME, pp. 989–992 (2008)Google Scholar
  16. 16.
    Schedl, M.: The lfm-1b dataset for music retrieval and recommendation. In: ICMR, pp. 103–110 (2016)Google Scholar
  17. 17.
    Schedl, M., Hauger, D.: Mining microblogs to infer music artist similarity and cultural listening patterns. In: WWW, pp. 877–886 (2012)Google Scholar
  18. 18.
    Zheleva, E., Guiver, J., Mendes Rodrigues, E., Milić-Frayling, N.: Statistical models of music-listening sessions in social media. In: WWW, pp. 1019–1028 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan

Personalised recommendations