Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach

dc.contributor.authorHotz-Behofsits, Christian
dc.contributor.authorWinkler, Daniel
dc.contributor.authorWlömert, Nils
dc.date.accessioned2021-12-24T17:48:51Z
dc.date.available2021-12-24T17:48:51Z
dc.date.issued2022-01-04
dc.description.abstractConsumers widely use music genres (e.g., pop, rock) for finding the right products. However, they are commonly arbitrary, not-standardized, disputed, and closely related genres often overlap. In this paper, we challenge established music genres (e.g., pop, rock) by comparing them to an entirely data-driven approach. To this end, we use a unique data set of revealed user preferences to carry out a context-based artist similarity. This measure is used in turn to find high-density artist clusters. The contribution of this paper is twofold. First, we investigate the differences between established music genres and data-driven clustering. Second, we provide implications for researchers and practitioners.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2022.412
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79746
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectStreaming Media in Entertainment
dc.subjectclustering
dc.subjectconsumer preferences
dc.subjectmusic genres
dc.subjectproduct discovery
dc.subjectproduct similarity
dc.titleMusic Genres Reconsidered: Challenging Established Genres with a Data-driven Approach
dc.type.dcmitext

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