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.

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Bibtex


@inproceedings{ahmed:idetc_2016_idea,
    address = {Charlotte, USA},
    author = {Faez Ahmed, Mark Fuge, and Lev D. Gorbunov},
    booktitle = {ASME International Design Engineering Technical Conferences},
    title = {Discovering diverse, high quality design ideas from a large corpus},
    year = {2016},
    month = aug,
    publisher = {ASME}
}