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How item discovery enabled by diversity leads to increased recommendation list attractiveness
- Source :
- SAC, 32nd Annual ACM Symposium on Applied Computing, SAC 2017, Part F128005, 1693-1696
- Publication Year :
- 2017
-
Abstract
- 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.
- Subjects :
- Attractiveness
Computer science
media_common.quotation_subject
02 engineering and technology
Recommender system
Diversification (marketing strategy)
computer.software_genre
User experience design
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Recommender systems
Composition (language)
media_common
Diversity
Multimedia
business.industry
Taste (sociology)
05 social sciences
050301 education
Advertising
respiratory system
business
0503 education
computer
human activities
User-centric evaluation
Diversity (politics)
Subjects
Details
- Language :
- English
- ISSN :
- 16931696
- Database :
- OpenAIRE
- Journal :
- SAC, 32nd Annual ACM Symposium on Applied Computing, SAC 2017, Part F128005, 1693-1696
- Accession number :
- edsair.doi.dedup.....5998bc024018349309c6df88815baa0c