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Publicatie

Effects of individual traits on diversity-aware music recommender user interfaces

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

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 visualizations, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. 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 Com- Bub 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, for the participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significance is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.
Boek: UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Pagina's: 291 - 299
ISBN:978-1450361668
Jaar van publicatie:2018
BOF-keylabel:ja
IOF-keylabel:ja
Authors from:Higher Education
Toegankelijkheid:Closed