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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.

People like variety and often prefer to choose from large item sets. However, large sets can cause a phenomenon called “choice overload”: they are more difficult to choose from, and as a result decision makers are less satisfied with their choices. It has been argued that choice overload occurs because large sets contain more similar items. To overcome this effect, the present paper proposes that increasing the diversity of item sets might make them more attractive and satisfactory, without making them much more difficult to choose from. To this purpose, by using structural equation model methodology, we study diversification based on the latent features of a matrix factorization recommender model. Study 1 diversifies a set of recommended items while controlling for the overall quality of the set, and tests it in two online user experiments with a movie recommender system. Study 1a tests the effectiveness of the latent feature diversification, and shows that diversification increases the perceived diversity and attractiveness of the item set, while at the same time reducing the perceived difficulty of choosing from the set. Study 1b subsequently shows that diversification can increase users’ satisfaction with the chosen option, especially when they are choosing from small, diverse item sets. Study 2 extends these results by testing our diversification algorithm against traditional Top-N recommendations, and finds that diverse, small item sets are just as satisfying and less effortful to choose from than Top-N recommendations. Our results suggest that, at least for the movie domain, diverse small sets may be the best thing one could offer a user of a recommender system.

Even though people are attracted by large, high quality recommendation sets, psychological research on choice overload shows that choosing an item from recommendation sets containing many attractive items can be a very difficult task. A web-based user experiment using a matrix factorization algorithm applied to the MovieLens dataset was used to investigate the effect of recommendation set size (5 or 20 items) and set quality (low or high) on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item. The results show that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets, as the increased recommendation set attractiveness is counteracted by the increased difficulty of choosing from these sets. These findings were supported by behavioral measurements revealing intensified information search and increased acquisition times for these large attractive sets. Important implications of these findings for the design of recommender system user interfaces will be discussed.