TY - GEN
T1 - Music artist similarity
T2 - 13th International Conference on Transforming Digital Worlds, iConference 2018
AU - Hu, Xiao
AU - Tam, Ira Keung Kit
AU - Liu, Meijun
AU - Downie, J. Stephen
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Artist popularity
KW - Genre
KW - Large-scale dataset
KW - Music artist similarity
KW - Online music services
UR - http://www.scopus.com/inward/record.url?scp=85044423196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044423196&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78105-1_41
DO - 10.1007/978-3-319-78105-1_41
M3 - Conference contribution
AN - SCOPUS:85044423196
SN - 9783319781044
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 378
EP - 383
BT - Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
A2 - Chowdhury, Gobinda
A2 - McLeod, Julie
A2 - Gillet, Val
A2 - Willett, Peter
PB - Springer
Y2 - 25 March 2018 through 28 March 2018
ER -