• p285-kim-1

    Final published version, 4.37 MB, PDF document


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.
Original languageEnglish
Title of host publicationACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450360210
ISBN (Print)978-1-4503-6021-0
Publication statusPublished - 2019
EventUMAP 2019: 27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Cyprus
Duration: 9 Jun 201912 Jun 2019
Conference number: 27


ConferenceUMAP 2019

    Research areas

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

ID: 55308717