Seeker or Avoider? User Modeling for Inspiration Deployment in Large-Scale Ideation
This research addresses the need for personalized inspiration deployment in large-scale ideation systems, offering an incremental improvement by introducing user modeling based on ideator types.
The study tackled the problem of individual differences in how people react to inspirations during brainstorming by identifying two ideator types (seekers and avoiders) and developing a user model to classify them. The random forest classifier achieved 73% accuracy in differentiating these types after three minutes of ideation, showing that seekers benefit from inspirations while avoiders are negatively affected.
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.