@inproceedings{6052b274db2343c9a6f853b42d9283a2,
title = "Music artist similarity: An exploratory study on a large-scale dataset of online streaming services",
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 = "Artist popularity, Genre, Large-scale dataset, Music artist similarity, Online music services",
author = "Xiao Hu and Tam, {Ira Keung Kit} and Meijun Liu and Downie, {J. Stephen}",
year = "2018",
doi = "10.1007/978-3-319-78105-1_41",
language = "English (US)",
isbn = "9783319781044",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "378--383",
editor = "Gobinda Chowdhury and Julie McLeod and Val Gillet and Peter Willett",
booktitle = "Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings",
address = "Germany",
note = "13th International Conference on Transforming Digital Worlds, iConference 2018 ; Conference date: 25-03-2018 Through 28-03-2018",
}