A Data-Driven Approach to Interdisciplinary Collaborative Discovery and Recommendation

Abstract

The convergence of different fields is crucial for scientific advancement, and interdisciplinary collaborations among scholars are necessary for fostering this development. However, identifying and recommending potential interdisciplinary collaborators systematically is a challenging task. This paper proposes an interdisciplinary collaboration discovery and recommendation model for scholars in different fields. We utilized clustering algorithms, including K-Means, DBSCAN, and Affinity, to generate a scholarly interdisciplinary collaboration discovery graph. To recommend potential collaborators, we proposed an algorithm that considers both the suitability between scholars and the individual comprehensive influence of scholars. The effectiveness of the model was validated using data from 126 scholars at the Beijing University of Posts and Telecommunications.