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Interactive Music Genre Exploration with Visualization and Mood Control

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Martijn C. WillemsenAuthors Info & Claims

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Pages 175 - 185

https://doi.org/10.1145/3397481.3450700

Published: 14 April 2021 Publication History

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Abstract

Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users’ understandability and acceptance of the novel items as well as support users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a music genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood dimensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users’ subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and understandable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualization. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization.

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Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Loepp BZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Exploring the Potential of Generative AI for Augmenting Choice-Based Preference Elicitation in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664873(114-119)Online publication date: 27-Jun-2024
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Wang HSourin A(2024)Visual signatures for music mood and timbreThe Visual Computer10.1007/s00371-024-03417-zOnline publication date: 31-May-2024
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Index Terms

Interactive Music Genre Exploration with Visualization and Mood Control

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

April 2021

618 pages

ISBN:9781450380171

DOI:10.1145/3397481

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.

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Published: 14 April 2021

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April 14 - 17, 2021

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Cited By

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https://dl.acm.org/doi/10.1145/3652891
Loepp BZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Exploring the Potential of Generative AI for Augmenting Choice-Based Preference Elicitation in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664873(114-119)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664873
Wang HSourin A(2024)Visual signatures for music mood and timbreThe Visual Computer10.1007/s00371-024-03417-zOnline publication date: 31-May-2024
https://doi.org/10.1007/s00371-024-03417-z
Melchiorre APenz DGanhör CLesota OFragoso VFritzl FParada-Cabaleiro ESchubert FSchedl M(2023)Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendationInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00275-812:1Online publication date: 2-Jun-2023
https://doi.org/10.1007/s13735-023-00275-8
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042
Liang YWillemsen M(2022)Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferencesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546772(3-13)Online publication date: 12-Sep-2022
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Kleemann TLoepp BZiegler JBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Towards Multi-Method Support for Product Search and RecommendingAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536408(74-79)Online publication date: 4-Jul-2022
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Liang YWillemsen M(2022)Promoting Music Exploration through Personalized Nudging in a Genre Exploration RecommenderInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210806039:7(1495-1518)Online publication date: 21-Aug-2022
https://doi.org/10.1080/10447318.2022.2108060
Liang YWillemsen M(2021)The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommenderProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474253(230-240)Online publication date: 13-Sep-2021
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