A graph neural network-based teammate recommendation model for knowledge-intensive crowdsourcing

Current research on teammate recommendation decision in various fields is difficult to apply to knowledgeintensive crowdsourcing, which needs to make a trade-off between solver willingness and team performance. To weigh the bilateral benefits between the solver and the seeker, this paper proposes a teammate recommendation model based on multi-criteria decision-making of solver willingness and team performance. The prediction of willingness to team up is driven by explicit competitiveness, teammate familiarity, and social capital. By adapting the graph-based deep learning model, we transform the solver performance prediction into a bipartite network link prediction problem. We jointly compute the solver willingness and team performance to make the final recommendation decisions. The experimental results based on Kaggle data prove the effectiveness of our method. The findings provide managerial implications and a practical reference for the design and operation of teammate recommender systems.