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- Publisher Website: 10.1007/978-3-319-78105-1_41
- Scopus: eid_2-s2.0-85044423196
- WOS: WOS:000449872000041
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Conference Paper: Music Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services
Title | Music Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services |
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Authors | |
Keywords | Artist popularity Genre Large-scale dataset Music artist similarity Online music services |
Issue Date | 2018 |
Publisher | Springer Verlag. |
Citation | Proceedings of 13th International Conference on Information, iConference 2018: Transforming Digital Worlds, Sheffield, UK, 25-28 March 2018, p. 378-383 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/262005 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID | |
Series/Report no. | Lecture Notes in Computer Science ; v. 10766 |
DC Field | Value | Language |
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dc.contributor.author | Hu, X | - |
dc.contributor.author | Tam, IKK | - |
dc.contributor.author | Liu, M | - |
dc.contributor.author | Downie, JS | - |
dc.date.accessioned | 2018-09-28T04:51:51Z | - |
dc.date.available | 2018-09-28T04:51:51Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of 13th International Conference on Information, iConference 2018: Transforming Digital Worlds, Sheffield, UK, 25-28 March 2018, p. 378-383 | - |
dc.identifier.isbn | 9783319781044 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262005 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Springer Verlag. | - |
dc.relation.ispartof | 13th International Conference, iConference 2018 | - |
dc.relation.ispartofseries | Lecture Notes in Computer Science ; v. 10766 | - |
dc.subject | Artist popularity | - |
dc.subject | Genre | - |
dc.subject | Large-scale dataset | - |
dc.subject | Music artist similarity | - |
dc.subject | Online music services | - |
dc.title | Music Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Hu, X: xiaoxhu@hku.hk | - |
dc.identifier.authority | Hu, X=rp01711 | - |
dc.identifier.doi | 10.1007/978-3-319-78105-1_41 | - |
dc.identifier.scopus | eid_2-s2.0-85044423196 | - |
dc.identifier.hkuros | 292579 | - |
dc.identifier.volume | 10766 | - |
dc.identifier.spage | 378 | - |
dc.identifier.epage | 383 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000449872000041 | - |
dc.publisher.place | Cham | - |
dc.identifier.issnl | 0302-9743 | - |