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Effects of personal characteristics in control-oriented user interfaces for music recommender systems. / Jin, Yucheng; Tintarev, Nava; Htun, Nyi Nyi; Verbert, Katrien.

In: User Modeling and User-Adapted Interaction, 2019.

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Jin, Yucheng ; Tintarev, Nava ; Htun, Nyi Nyi ; Verbert, Katrien. / Effects of personal characteristics in control-oriented user interfaces for music recommender systems. In: User Modeling and User-Adapted Interaction. 2019.

BibTeX

@article{98881e0812ee4d3b9616091573d56f66,
title = "Effects of personal characteristics in control-oriented user interfaces for music recommender systems",
abstract = "Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics, visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.",
keywords = "Acceptance, Cognitive load, Perceived diversity, Personal characteristics, Recommender systems, User control, User experience",
author = "Yucheng Jin and Nava Tintarev and Htun, {Nyi Nyi} and Katrien Verbert",
year = "2019",
doi = "10.1007/s11257-019-09247-2",
language = "English",
journal = "User Modeling and User-Adapted Interaction: the journal of personalization research",
issn = "0924-1868",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Effects of personal characteristics in control-oriented user interfaces for music recommender systems

AU - Jin, Yucheng

AU - Tintarev, Nava

AU - Htun, Nyi Nyi

AU - Verbert, Katrien

PY - 2019

Y1 - 2019

N2 - Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics, visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.

AB - Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics, visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.

KW - Acceptance

KW - Cognitive load

KW - Perceived diversity

KW - Personal characteristics

KW - Recommender systems

KW - User control

KW - User experience

UR - http://www.scopus.com/inward/record.url?scp=85074566495&partnerID=8YFLogxK

U2 - 10.1007/s11257-019-09247-2

DO - 10.1007/s11257-019-09247-2

M3 - Article

JO - User Modeling and User-Adapted Interaction: the journal of personalization research

T2 - User Modeling and User-Adapted Interaction: the journal of personalization research

JF - User Modeling and User-Adapted Interaction: the journal of personalization research

SN - 0924-1868

ER -

ID: 66776426