Advertisement

Music Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services

  • Xiao HuEmail author
  • Ira Keung Kit Tam
  • Meijun Liu
  • J. Stephen Downie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)

Abstract

In supporting music search, online music streaming services often suggest artists who are deemed as similar to those listened to or liked by users. However, there has been an ongoing debate on what constitutes artist similarity. Approaching this problem from an empirical perspective, this study collected a large-scale dataset of similar artists recommended in four well-known online music steaming services, namely Spotify, Last.fm, the Echo Nest, and KKBOX, on which an exploratory quantitative analysis was conducted. Preliminary results reveal that similar artists in these services were related to the genre and popularity of the artists. The findings shed light on how the concept of artist similarity is manifested in widely adopted real-world applications, which will in turn help enhance our understanding of music similarity and recommendation.

Keywords

Music Artist Similarity Online music services Large-scale dataset Genre Artist popularity 

References

  1. Bonnin, G., Jannach, D.: Automated generation of music playlists: Survey and experiments. ACM Comput. Surv. (CSUR) 47(2), 26 (2015)Google Scholar
  2. Celma, Ò.: Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13287-2 CrossRefGoogle Scholar
  3. Celma, Ò., Cano, P.: From hits to niches?: or how popular artists can bias music recommendation and discovery. In: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pp. 5. ACM (2008)Google Scholar
  4. Cillessen, A.H.N., Rose, A.J.: Understanding popularity in the peer system. Curr. Dir. Psychol. Sci. 14(2), 102–105 (2005)CrossRefGoogle Scholar
  5. Echo Nest: our company (2016). http://the.echonest.com/company/. Accessed 09 Aug 2017
  6. Ellis, D.P., Whitman, B., Berenzweig, A., Lawrence, S.: The quest for ground truth in musical artist similarity. In: Proceedings of International Society for Music Information Retrieval, Paris, France (2002)Google Scholar
  7. Jacobson, K.: Connections in music. Ph.D. thesis, Queen Marry University of London, London, U.K. (2011)Google Scholar
  8. Koch, N.M., Soto, I.M.: Let the music be your master: power laws and music listening habits. Musicae Scientiae 20, 193–206 (2016). European Society for the Cognitive Sciences of MusicCrossRefGoogle Scholar
  9. Koenigstein, N., Dror, G., Koren, Y.: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: Proceedings of the 5th ACM conference on Recommender systems, pp. 165–172 (2011)Google Scholar
  10. Lee, J.H., Waterman, N.M.: Understanding user requirements for music information services. In: Proceedings of International Society for Music Information Retrieval, Porto, Portugal, pp. 253–258 (2012)Google Scholar
  11. Lee, S.H., Kim, P.-J., Jeong, H.: Statistical properties of sampled networks. Phys. Rev. E. Stat. Nonlinear, Soft Matter Phys. 73(1), 016102 (2006)CrossRefGoogle Scholar
  12. Mauch, M., Maccallum, R.M., Levy, M., Leroi, A.M.: The evolution of popular music: USA 1960–2010. R. Soc. Open Sci. 2(5), 150081 (2015)CrossRefGoogle Scholar
  13. Oramas, S., Sordo, M., Anke, L.E., Serra, X.: A semantic-based approach for artist similarity. In: Proceedings of International Society for Music Information Retrieval, Malaga, Spain, pp. 100–106 (2015)Google Scholar
  14. Oxford Music Online: genre. http://www.oxfordmusiconline.com/subscriber/article/grove/music/40599. Accessed 23 Aug 2017
  15. Pálovics, R., Benczúr, A.A.: Temporal influence over the Last.fm social network. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 486–493 (2013)Google Scholar
  16. Pucihar, A., Borštnar, M.K., Kittl, C., Ravesteijn, P., Clarke, R., Bons, R.: Music recommender systems challenges and opportunities for non-superstar artists. In: 30th Bled eConference, Slovania (2017)Google Scholar
  17. Zax, D.: The Echo Nest makes pandora look like a transistor radio (2011). http://www.fastcocreate.com/1679062/the-echo-nest-makes-pandora-look-like-a-transistor-radio. Accessed 23 Aug 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Hong KongHong KongHong Kong S.A.R.
  2. 2.University of IllinoisChampaignUSA

Personalised recommendations