CrowdMuse

Abstract

Online crowds, with their large numbers and diversity, show great potential for creativity, particularly during large-scale brainstorming sessions. Research has explored different ways of augmenting this creativity, such as showing ideators some form of inspiration to get them to explore more categories or generate more ideas. The mechanisms used to select which inspirations are shown to ideators thus far have been focused on characteristics of the inspirations rather than on ideators. This can hinder their effect, as creativity research has shown that ideators have unique cognitive structures and may therefore be better inspired by some ideas rather than others. We introduce CrowdMuse, an adaptive system for supporting large scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanisms accordingly. An evaluation of this system could inform how to better individually support ideators.