Skip to main navigation Skip to search Skip to main content

User Insights on Diversity in Music Recommendation Lists

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

While many researchers have proposed various ways of quantifying recommendation list diversity, these approaches have had little input from users on their own perceptions and preferences in seeking diversity. Through an exploratory user study, we provide a better understanding of how users view the concept of diversity in music recommendations, and how they might optimise levels of intralist diversity themselves. In our study, 17 participants interacted with and rated the suggestions from two different recommendation systems. One provided static top-7 collaborative filtering recommendations, and the other provided an interactive slider to re-rank these recommendations based on a continuous diversity scale. We also asked participants a series of free-form questions on music discovery and diversity in semi-structured interviews. Userpreferred levels of diversity varied widely both within and between subjects. Although most users agreed that diversity is beneficial in music discovery, they also noted a risk of dissatisfaction from too much diversity. A key finding is that preference for diversification was often linked to user mood. Participants also expressed a clear distinction between diversity within existing preferences, and outside of existing preferences. These ideas of inner and outer diversity are not well defined within the bounds of current diversity metrics, and we discuss their implications.
Original languageEnglish
Title of host publicationProceedings of the 21st International Society for Music Information Retrieval Conference, ISMIR 2020
EditorsJulie Cumming, Jin Ha Lee, Brian McFee, Markus Schedl, Johanna Devaney, Johanna Devaney, Cory McKay, Eva Zangerle, Timothy de Reuse
Pages446-453
Number of pages8
ISBN (Electronic)9780981353708
Publication statusPublished - Oct 2020

Fields of science

  • 202002 Audiovisual media
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102015 Information systems

JKU Focus areas

  • Digital Transformation

Cite this