This paper describes how to select diverse, high quality, representative ideas when the number of ideas grow beyond what a person can easily organize. When designers have a large number of ideas, it becomes prohibitively difficult for them to explore the scope of those ideas and find inspiration. We propose a computational method to recommend a diverse set of representative and high quality design ideas and demonstrate the results for design challenges on OpenIDEO — a web-based online design community. Diversity of these ideas is defined using topic modeling to identify latent concepts within the text while the quality is measured from user feedback. Multi-objective optimization then trades off quality and diversity of ideas. The results show that our approach attains a diverse set of high quality ideas and that the proposed method is applicable to multiple domains.

This content is only available via PDF.
You do not currently have access to this content.