CrowdMuse: Supporting Crowd Idea Generation through User Modeling and Adaptation

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.