Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
Consumers 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.
Music Genres Reconsidered: Challenging Established Genres with a Data-driven Approach
Online
Consumers 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.
https://aisel.aisnet.org/hicss-55/dsm/streaming_media/5