CrowdMuse: Supporting crowd idea generation through user modeling and adaptation

Victor Girotto, Erin Walker, Winslow Burleson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Online crowds, with their large numbers and diversity, show great potential for creativity. Research has explored different ways of augmenting their creative performance, particularly during large-scale brainstorming sessions. Traditionally, this comes in the form of showing ideators some form of inspiration to get them to explore more categories or generate more and better ideas. The mechanisms used to select which inspirations are shown to ideators thus far have not taken into consideration ideators' individualities, which could hinder the effectiveness of support. In this paper, we introduce and evaluate CrowdMuse, a novel adaptive system for supporting large-scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanism accordingly. We evaluate CrowdMuse over two iterative large online studies and discuss the implication of our findings for designing adaptive creativity support systems.

Original languageEnglish (US)
Title of host publicationC and C 2019 - Proceedings of the 2019 Creativity and Cognition
PublisherAssociation for Computing Machinery, Inc
Pages95-106
Number of pages12
ISBN (Electronic)9781450359177
DOIs
StatePublished - Jun 13 2019
Externally publishedYes
Event12th ACM Creativity and Cognition Conference, C and C 2019 - San Diego, United States
Duration: Jun 23 2019Jun 26 2019

Publication series

NameC and C 2019 - Proceedings of the 2019 Creativity and Cognition

Conference

Conference12th ACM Creativity and Cognition Conference, C and C 2019
Country/TerritoryUnited States
CitySan Diego
Period6/23/196/26/19

Keywords

  • Adaptive systems
  • Brainstorming
  • Creativity
  • Crowd

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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