research-article

Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces

Authors:

Katrien VerbertAuthors Info & Claims

UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Pages 291 - 299

https://doi.org/10.1145/3209219.3209225

Published: 03 July 2018 Publication History

Abstract

When recommendations become increasingly personalized, users are often presented with a narrower range of content. To mitigate this issue, diversity-enhanced user interfaces for recommender systems have in the past found to be effective in increasing overall user satisfaction with recommendations. However, users may have different requirements for diversity, and consequently different visualization requirements. In this paper, we evaluate two visual user interfaces, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. SimBub is a baseline bubble chart that shows music genres and popularity by color and size, respectively. In addition, ComBub visualizes selected audio features along the X and Y axis in a more advanced and complex visualization. Our goal is to investigate how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots. We hypothesize that music experts, or people with better visual memory, will perceive higher diversity in ComBub than SimBub. A within-subjects user study (N=83) is conducted to compare these two visualizations. Results of our study show that participants with high MS and VM tend to perceive significantly higher diversity from ComBub compared to SimBub. In contrast, participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significant result is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
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Ferraro AFerreira GDiaz FBorn G(2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3643138
Wang JHuang CYan SXie WHe D(2024)When Young Scholars Cooperate with LLMs in Academic Tasks: The Influence of Individual Differences and Task ComplexitiesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2352919(1-16)Online publication date: 20-May-2024
https://doi.org/10.1080/10447318.2024.2352919
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Index Terms

Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
  2. Visualization
    1. Visualization design and evaluation methods
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

July 2018

393 pages

ISBN:9781450355896

DOI:10.1145/3209219

General Chairs:
Tanja Mitrovic
University of Canterbury, New Zealand
,
Jie Zhang
Nanyang Technological University, Singapore
,
Program Chairs:
Li Chen
Hong Kong Baptist University, China
,
David Chin
University of Hawaii, USA

Copyright © 2018 ACM.

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Published: 03 July 2018

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UMAP '18: 26th Conference on User Modeling, Adaptation and Personalization

July 8 - 11, 2018

Singapore, Singapore

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UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Ferraro AFerreira GDiaz FBorn G(2024)Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural CitizenshipACM Transactions on Recommender Systems10.1145/36431382:1(1-32)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3643138
Wang JHuang CYan SXie WHe D(2024)When Young Scholars Cooperate with LLMs in Academic Tasks: The Influence of Individual Differences and Task ComplexitiesInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2352919(1-16)Online publication date: 20-May-2024
https://doi.org/10.1080/10447318.2024.2352919
Shimizu TFutami KTerada TTsukamoto M(2023)Selection Interface for Promoting User Selection Diversity by Presenting Positive/Negative Review Text and Video to Evoke Product Impression and User EmotionElectronics10.3390/electronics1212261112:12(2611)Online publication date: 9-Jun-2023
https://doi.org/10.3390/electronics12122611
Tran TFelfernig ALe VChau TMai T(2023)User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal CharacteristicsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592950(54-65)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592950
Bouraffa AFuhrmann GMaalej WGrundy JPollock LPenta M(2023)Developers' Visuo-Spatial Mental Model and Program ComprehensionProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00163(1920-1932)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE48619.2023.00163
Schelenz L(2022)Diversity Concepts in Computer Science and Technology Development: A CritiqueScience, Technology, & Human Values10.1177/0162243922112254948:5(1054-1079)Online publication date: 13-Sep-2022
https://doi.org/10.1177/01622439221122549
Ferraro AFerreira GDiaz FBorn G(2022)Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural CitizenshipProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551476(567-572)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551476
Cai WJin YChen L(2022)Impacts of Personal Characteristics on User Trust in Conversational Recommender SystemsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517471(1-14)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517471
Cai WJin YChen L(2022)Task-Oriented User Evaluation on Critiquing-Based Recommendation ChatbotsIEEE Transactions on Human-Machine Systems10.1109/THMS.2021.313167452:3(354-366)Online publication date: Jun-2022
https://doi.org/10.1109/THMS.2021.3131674
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