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Conference Paper: Music Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services

TitleMusic Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services
Authors
Issue Date2018
PublisherSpringer 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?
AbstractIn 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 Identifierhttp://hdl.handle.net/10722/262005
ISBN
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; v. 10766

 

DC FieldValueLanguage
dc.contributor.authorHu, X-
dc.contributor.authorTam, IKK-
dc.contributor.authorLiu, M-
dc.contributor.authorDownie, JS-
dc.date.accessioned2018-09-28T04:51:51Z-
dc.date.available2018-09-28T04:51:51Z-
dc.date.issued2018-
dc.identifier.citationProceedings of 13th International Conference on Information, iConference 2018: Transforming Digital Worlds, Sheffield, UK, 25-28 March 2018, p. 378-383-
dc.identifier.isbn9783319781044-
dc.identifier.urihttp://hdl.handle.net/10722/262005-
dc.description.abstractIn 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.languageeng-
dc.publisherSpringer Verlag.-
dc.relation.ispartof13th International Conference, iConference 2018-
dc.relation.ispartofseriesLecture Notes in Computer Science ; v. 10766-
dc.titleMusic Artist Similarity: An Exploratory Study on a Large-Scale Dataset of Online Streaming Services-
dc.typeConference_Paper-
dc.identifier.emailHu, X: xiaoxhu@hku.hk-
dc.identifier.authorityHu, X=rp01711-
dc.identifier.doi10.1007/978-3-319-78105-1_41-
dc.identifier.hkuros292579-
dc.identifier.volume10766-
dc.identifier.spage378-
dc.identifier.epage383-
dc.identifier.isiWOS:000449872000041-
dc.publisher.placeCham-

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