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)


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,, 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.


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


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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

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