How item discovery enabled by diversity leads to increased recommendation list attractiveness

Bruce Ferwerda, Mark P. Graus, Andreu Vall, Marko Tkalcic, Markus Schedl

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

8 Citations (Scopus)


Applying diversity to a recommendation list has been shown to positively influence the user experience. A higher perceived diversity is argued to have a positive effect on the attractiveness of the recommendation list and a negative effect on the difficulty to make a choice. In a user study we presented 100 participants with several personalized lists of recommended music artists varying in levels of diversity. Participants were asked to assess these lists on perceived diversity and attractiveness, the experienced choice difficulty and discovery (i.e., the extent the list enriches their taste). We found that recommendation list attractiveness is influenced by two effects: 1) by diversity mediated through discovery; diverse recommendation lists are perceived to be more attractive if they enrich the user's taste or 2) by the list familiarity; a higher list familiarity contributes to a higher list attractiveness. We additionally revealed how individual differences (i.e., familiarity) moderate the effects found. Our results have implications on the composition of diversified recommendation lists. Specifically recommended items should contribute in extending and/or deepening the user's taste for the diversification to be effective.

Original languageEnglish
Title of host publication32nd Annual ACM Symposium on Applied Computing, SAC 2017
PublisherAssociation for Computing Machinery, Inc
Number of pages4
VolumePart F128005
ISBN (Electronic)9781450344869
Publication statusPublished - 3 Apr 2017
Event32nd ACM Symposium on Applied Computing (SAC 2017) - Cadi Ayyad University (UCA) of Marrakesh, Morocco, Marrakesh, Morocco
Duration: 4 Apr 20176 Apr 2017
Conference number: 32


Conference32nd ACM Symposium on Applied Computing (SAC 2017)
Abbreviated titleSAC 2017
Internet address


  • Diversity
  • Recommender systems
  • User-centric evaluation


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