Abstract
Applying diversity to a recommendation list has been shown to positively inuence 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 inuenced 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 diversiffed recommendation lists. Specifically recommended items should contribute in extending and/or deepening the user's taste for the diversification to be effective.
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CITATION STYLE
Ferwerda, B., Graus, M. P., Vall, A., Tkalcic, M., & Schedl, M. (2017). How item discovery enabled by diversity leads to increased recommendation list attractiveness. In Proceedings of the ACM Symposium on Applied Computing (Vol. Part F128005, pp. 1693–1696). Association for Computing Machinery. https://doi.org/10.1145/3019612.3019899
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