Dynamic scholarly collaborator recommendation via competitive multi-agent reinforcement learning

Abstract

© 2017 ACM. In an interdisciplinary environment, scientific collaboration is becoming increasingly important. Helping scholars make a right choice of potential collaborators is essential in achieving scientific success. Intuitively, the generation of collaboration relationship is a dynamic process. For instance, one scholar may first choose to work with Scholar A, and then work with Scholar B after accumulating additional academic credits. To address this property, we propose a novel dynamic collaboration recommendation method by adapting the multi-agent reinforcement learning technique to the coauthor network analysis. The collaborator selection is optimized from several different scholar similarity measurements. Unlike prior studies, the proposed method characterizes scholarly competition, a.k.a. different scholars will compete for potential collaborator at each iteration. An evaluation with the ACM data shows that multi-agent reinforcement learning plus scholarly competition modeling can be significant for collaboration recommendation.