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When selecting ideas or trying to find inspiration, designers often must siftthrough hundreds or thousands of ideas. This paper provides an algorithm torank design ideas such that the ranked list simultaneously maximizes thequality and diversity of recommended designs. To do so, we first define andcompare two diversity measures using Determinantal Point Processes (DPP) andadditive sub-modular functions. We show that DPPs are more suitable for itemsexpressed as text and that a greedy algorithm diversifies rankings with boththeoretical guarantees and empirical performance on what is otherwise anNP-Hard problem. To produce such rankings, this paper contributes a novel wayto extend quality and diversity metrics from sets to permutations of rankedlists.  These rank metrics open up the use of multi-objective optimization todescribe trade-offs between diversity and quality in ranked lists. We use suchtrade-off fronts to help designers select rankings using indifference curves.However, we also show that rankings on trade-off front share a number oftop-ranked items; this means reviewing items (for a given depth like the top10) from across the entire diversity-to-quality front incurs only a marginalincrease in the number of designs considered. While the proposed techniques aregeneral purpose enough to be used across domains, we demonstrate concreteperformance on selecting items in an online design community (OpenIDEO), whereour approach reduces the time required to review diverse, high-quality ideasfrom around 25 hours to 90 minutes. This makes evaluation of crowd-generatedideas tractable for a single designer. Our code is publicly accessible forfurther research.

Ranking ideas for diversity and quality