Ranking ideas for diversity and quality

[+] Author and Article Information
Faez Ahmed

Dept. of Mechanical Engineering University of Maryland College Park, Maryland 20742

Mark Fuge

Dept. of Mechanical Engineering University of Maryland College Park, Maryland 20742

1Corresponding author.

ASME doi:10.1115/1.4038070 History: Received December 15, 2016; Revised September 21, 2017


When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using Determinantal Point Processes (DPP) and sub-modular functions. We show that DPPs have better average performance and that a greedy algorithm diversifies rankings with both theoretical guarantees and empirical performance on what is otherwise an NP-Complete problem. To produce such rankings, this paper contributes a novel way to extend quality and diversity metrics from sets to permutations of ranked lists. These rank metrics open up the use of multi-objective optimization to describe trade-offs between diversity and quality in ranked lists. We use such trade-off fronts to help designers select rankings using indifference curves. However, we also show that rankings on trade-off front share a number of top-ranked items; this means reviewing items (say top 10) from across the entire diversity-to-quality front incurs only a marginal increase in the number of designs considered. While the proposed techniques are general purpose enough to be used across domains, we demonstrate concrete performance on selecting items in an online design community (OpenIDEO), where our approach reduces the time required to review diverse, high-quality ideas from around 25 hours to 90 minutes. This makes evaluation of crowd-generated ideas tractable for a designer.

Copyright (c) 2017 by ASME
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