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.

翻译：跨学科合作已成为科学突破的驱动力，评估学者在跨学科研究中的表现对促进此类合作至关重要。然而，传统基于个人成就的学者评估方法未考虑跨学科合作，这为跨学科学者评估与推荐带来了挑战。为解决此问题，我们提出了一种学者嵌入模型，该模型基于全局语义信息与社会影响力对学者进行量化表征，并实现学者研究趋势的实时追踪。模型整合了语义信息与社会影响力以评估跨学科学者，为未来跨学科合作发现与推荐项目奠定基础。我们以北京邮电大学学者样本验证了模型的有效性。