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Discovering Diverse, High Quality Design Ideas From a Large Corpus

[+] Author Affiliations
Faez Ahmed, Mark Fuge, Lev D. Gorbunov

University of Maryland, College Park, MD

Paper No. DETC2016-59926, pp. V007T06A008; 11 pages
doi:10.1115/DETC2016-59926
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 28th International Conference on Design Theory and Methodology
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5019-0
  • Copyright © 2016 by ASME

abstract

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.

Copyright © 2016 by ASME
Topics: Design

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