A Novel Scholar Embedding Model for Interdisciplinary Collaboration
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Interdisciplinary collaboration has become a driving force for scientific breakthroughs, and evaluating scholars' performance in interdisciplinary researches is essential for promoting such collaborations. However, traditional scholar evaluation methods based solely on individual achievements do not consider interdisciplinary cooperation, creating a challenge for interdisciplinary scholar evaluation and recommendation. To address this issue, we propose a scholar embedding model that quantifies and represents scholars based on global semantic information and social influence, enabling real-time tracking of scholars' research trends. Our model incorporates semantic information and social influence for interdisciplinary scholar evaluation, laying the foundation for future interdisciplinary collaboration discovery and recommendation projects. We demonstrate the effectiveness of our model on a sample of scholars from the Beijing University of Posts and Telecommunications.
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