MACRec: A Meta-learning Enhanced Model for Academic Collaborator Recommendation

Identifying potential academic collaborators has become increasingly crucial to promote scientific development. Existing methods of recommending collaborators primarily focus on analyzing publication content or integrating both content and academic network information. However, these methods often suffer from the cold-start problem due to sparse interaction data among researchers. To this end, we propose a novel meta-learning enhanced model on heterogeneous academic networks for academic collaborator recommendation, named MACRec. This model considers the academic collaborator recommendation in both data and model levels to address the cold-start problem. We introduce a multi-view task constructor that uses academic data and meta-paths to capture semantic contexts within an academic network at the data level, and we also include an academic meta-learner that performs semantic-wise and task-wise adaptations, allowing for fast adaptation to new researcher tasks with few data at the model level. Extensive experiments on two real-world datasets show that MACRec performs significantly better than state-of-the-art baseline methods.