Explainable Cross-Domain Collaborator Recommendation

Collaborating with researchers from different academic domains has become more accessible with the emergence of academic social networking platforms. However, finding the right collaborator remains a challenge. Current cross-domain collaborator recommendation methods fail to provide explanations for their suggestions, thus requiring additional effort from users. In this paper, we propose the Explainable Cross-Domain Collaborator Recommendation (ECDCR) problem, which aims to provide users with collaborator recommendations and corresponding explanations. Our approach, named ECRec, constructs an academic network by using external information and extracting users’ meta-path connections to provide comprehensive explanations for their collaborative behavior. To ensure consistency between recommendation and explanation, we employ an attention mechanism that integrates multiple meta-paths to predict collaboration and selects the most important meta-path connection as an explanation. We conduct extensive experiments on three real-world datasets and demonstrate that our method has excellent explainability and recommendation performance.