Ranking ideas for diversity and quality
Faez Ahmed, Mark Fuge

TL;DR
This paper introduces a ranking algorithm that balances diversity and quality of ideas, using DPPs and sub-modular functions, with practical application in design idea selection reducing review time significantly.
Contribution
It develops a novel extension of quality and diversity metrics to ranked lists and demonstrates effective trade-off analysis for idea ranking.
Findings
DPPs outperform additive sub-modular functions for text-based items.
The greedy algorithm provides theoretical guarantees and good empirical results.
Application reduces idea review time from 25 hours to 90 minutes.
Abstract
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 additive sub-modular functions. We show that DPPs are more suitable for items expressed as text and that a greedy algorithm diversifies rankings with both theoretical guarantees and empirical performance on what is otherwise an NP-Hard 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…
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