Music artist similarity: An exploratory study on a large-scale dataset of online streaming services

Xiao Hu, Ira Keung Kit Tam, Meijun Liu, J. Stephen Downie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationTransforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
EditorsGobinda Chowdhury, Julie McLeod, Val Gillet, Peter Willett
PublisherSpringer-Verlag Berlin Heidelberg
Pages378-383
Number of pages6
ISBN (Print)9783319781044
DOIs
StatePublished - Jan 1 2018
Event13th International Conference on Transforming Digital Worlds, iConference 2018 - Sheffield, United Kingdom
Duration: Mar 25 2018Mar 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Transforming Digital Worlds, iConference 2018
CountryUnited Kingdom
CitySheffield
Period3/25/183/28/18

Keywords

  • Artist popularity
  • Genre
  • Large-scale dataset
  • Music artist similarity
  • Online music services

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Music artist similarity: An exploratory study on a large-scale dataset of online streaming services'. Together they form a unique fingerprint.

  • Cite this

    Hu, X., Tam, I. K. K., Liu, M., & Downie, J. S. (2018). Music artist similarity: An exploratory study on a large-scale dataset of online streaming services. In G. Chowdhury, J. McLeod, V. Gillet, & P. Willett (Eds.), Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings (pp. 378-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10766 LNCS). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-78105-1_41