Recommending Scientific Collaboration from ResearchGate

Scientific collaboration improves researchers productivity by providing a way to share new ideas, learn new techniques, and find new research applications, increasing the chance to access funding. Beyond ethics and reciprocity, there are other important aspects on achieving scientific collaborations, such as research interests and expected productivity gain, that are paramount to a successful partnership. However, achieve effective collaborations is a hard work and can drain researchers time. In this work, we propose a recommendation approach that uses different strategies to suggest scientific collaboration for researchers based on their research interest. In particular, our approach exploits ResearchGate, a well known research social network from where research interests and researchers production are used to model similarity between them. Experimental results show that the content-based strategy outperforms neighborhood-based collaborative filtering strategies to recommend scientific collaboration with gains of up 16.60% in precision, 37.19% in recall, and 21.16% in F1 for the top-20 recommendation lists.