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

Yucheng Jin, Nava Tintarev, Katrien Verbert

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

22 Citations (Scopus)
133 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationUMAP'18
Subtitle of host publicationProceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Print)978-1-4503-5589-6
Publication statusPublished - 2018
EventUMAP 2018 : The 26th Conference on User Modeling, Adaptation and Personalization - Singapore, Singapore
Duration: 8 Jul 201811 Jul 2018
Conference number: 26


ConferenceUMAP 2018
Abbreviated titleUMAP '18

Bibliographical note

Accepted author manuscript


  • Individual traits
  • diversity
  • recommender user interfaces
  • visual memory
  • musical sophistication

Cite this