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Beyond Explicit Reports : Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. / Kim, Jaehun; Manolios, Sandy; Demetriou, Andrew; Liem, Cynthia.

ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. New York, NY : Association for Computing Machinery (ACM), 2019. p. 285-293.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Kim, J, Manolios, S, Demetriou, A & Liem, C 2019, Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. in ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), New York, NY, pp. 285-293, UMAP 2019, Larnaca, Cyprus, 9/06/19. https://doi.org/10.1145/3320435.3320462

APA

Kim, J., Manolios, S., Demetriou, A., & Liem, C. (2019). Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. In ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (pp. 285-293). New York, NY: Association for Computing Machinery (ACM). https://doi.org/10.1145/3320435.3320462

Vancouver

Kim J, Manolios S, Demetriou A, Liem C. Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. In ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. New York, NY: Association for Computing Machinery (ACM). 2019. p. 285-293 https://doi.org/10.1145/3320435.3320462

Author

Kim, Jaehun ; Manolios, Sandy ; Demetriou, Andrew ; Liem, Cynthia. / Beyond Explicit Reports : Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference. ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. New York, NY : Association for Computing Machinery (ACM), 2019. pp. 285-293

BibTeX

@inproceedings{371a38326ec44fb5985d6bc80f447039,
title = "Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference",
abstract = "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.",
keywords = "Latent factor models, Listening behavior, Multidisciplinary approaches, Music preferences, OA-Fund TU Delft",
author = "Jaehun Kim and Sandy Manolios and Andrew Demetriou and Cynthia Liem",
year = "2019",
doi = "10.1145/3320435.3320462",
language = "English",
isbn = "978-1-4503-6021-0",
pages = "285--293",
booktitle = "ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Beyond Explicit Reports

T2 - Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference

AU - Kim, Jaehun

AU - Manolios, Sandy

AU - Demetriou, Andrew

AU - Liem, Cynthia

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Latent factor models

KW - Listening behavior

KW - Multidisciplinary approaches

KW - Music preferences

KW - OA-Fund TU Delft

UR - http://www.scopus.com/inward/record.url?scp=85068052004&partnerID=8YFLogxK

U2 - 10.1145/3320435.3320462

DO - 10.1145/3320435.3320462

M3 - Conference contribution

SN - 978-1-4503-6021-0

SP - 285

EP - 293

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

PB - Association for Computing Machinery (ACM)

CY - New York, NY

ER -

ID: 55308717