Beyond Explicit Reports

Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference

Kim, Jaehun (TU Delft Multimedia Computing)
Manolios, S. (TU Delft Multimedia Computing)
Demetriou, A.M. (TU Delft Multimedia Computing)
Liem, C.C.S. (TU Delft Multimedia Computing)

2019

Prior research from the field of music psychology has suggested that there are factors common to music preference beyond individual genres. Specifically, research has shown that self-reported ratings of preference for individual musical genres can be reduced to 4 or 5 dimensions, which in turn have been shown to correlate to relevant psychological constructs, such as personality. However, the number of dimensions emerging from multiple studies has varied despite the care taken in conducting such research. Data-driven approaches offer opportunities to further this line of research with actual listening data, at a scale and scope surpassing that of traditional psychological studies. Although listening data can be considered more direct and comprehensive evidence of listening preference, transforming this data into meaningful measurements is non-trivial. In the current paper, we report on investigations seeking to find interpretable underlying dimensions of music taste, using implicit large-scale listening data. Offering a critical reflection on potential researchers' degrees of freedom, we adopt an explicit systematic approach, investigating the impact of varying different parameters, analysis, and normalization techniques. More precisely, we consider various ways to extract listening preference information from two large, openly available datasets of music listening behavior, making use of principal component analysis and variational autoencoders to extract potential underlying dimensions. Results and implications are discussed in light of prior psychological theory, and the potential of user listening data to further research on music preference.

Latent factor models
Listening behavior
Multidisciplinary approaches
Music preferences
OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:371a3832-6ec4-4fb5-985d-6bc80f447039

https://doi.org/10.1145/3320435.3320462

Association for Computing Machinery (ACM), New York, NY

978-1-4503-6021-0

ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

UMAP 2019, 2019-06-09 → 2019-06-12, Larnaca, Cyprus

Institutional Repository

conference paper

© 2019 Jaehun Kim, S. Manolios, A.M. Demetriou, C.C.S. Liem