Identifying contributory domain experts in online innovation communities

Conventional approaches for identifying domain experts focus only on their level of expertise and fail to consider their innovation potential. Thus, we propose a more comprehensive method by considering the types of innovation tasks and their corresponding knowledge domains. With a set of novel and effective metrics, the proposed method is able to assess the knowledge quality and innovation potential of each participating user. We evaluate our method with a real-world dataset collected from a popular online innovation community. The results indicate that the proposed method is efficient and scalable for contributory domain expert identification with different innovation tasks and different knowledge domains. This work expands expert identification research by providing both a new theoretical angle and new technical solution for quantifying the value of users.