Interdisciplinary Collaborator Recommendation Based on Research Content Similarity

SUMMARY Most existing methods on research collaborator recom- mendation focus on promoting collaboration within a specific discipline and exploit a network structure derived from co-authorship or co-citation information. To find collaboration opportunities outside researchers’ own fields of expertise and beyond their social network, we present an interdis- ciplinary collaborator recommendation method based on research content similarity. In the proposed method, we calculate textual features that re- flect a researcher’s interests using a research grant database. To find the most relevant researchers who work in other fields, we compare construct- ing a pairwise similarity matrix in a feature space and exploiting existing social networks with content-based similarity. We present a case study at the Graduate University for Advanced Studies in Japan in which actual collaborations across departments are used as ground truth. The re- sults indicate that our content-based approach can accurately predict interdisciplinary collaboration compared with the conventional collaboration network-based approaches.