  People react differently to inspirations shown to them during brainstorming.
Existing research on large-scale ideation systems has investigated this
phenomenon through aspects of timing, inspiration similarity and inspiration
integration. However, these approaches do not address people's individual
preferences. In the research presented, we aim to address this lack with
regards to inspirations. In a first step, we conducted a co-located
brainstorming study with 15 participants, which allowed us to differentiate two
types of ideators: Inspiration seekers and inspiration avoiders. These insights
informed the study design of the second step, where we propose a user model for
classifying people depending on their ideator types, which was translated into
a rule-based and a random forest-based classifier. We evaluated the validity of
our user model by conducting an online experiment with 380 participants. The
results confirmed our proposed ideator types, showing that, while seekers
benefit from the availability of inspiration, avoiders were influenced
negatively. The random forest classifier enabled us to differentiate people
with a 73 \% accuracy after only three minutes of ideation. These insights show
that the proposed ideator types are a promising user model for large-scale
ideation. In future work, this distinction may help to design more personalized
large-scale ideation systems that recommend inspirations adaptively.
