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：交叉学科合作已成为科学突破的驱动力，评估学者在交叉学科研究中的表现对于促进此类合作至关重要。然而，传统仅基于个人成就的学者评价方法未考虑跨学科合作，这为交叉学科领域学者的评估与推荐带来了挑战。为解决这一问题，我们提出了一种学者嵌入模型，该模型通过全局语义信息与社会影响力对学者进行量化表征，能够实时追踪学者的研究趋势。我们的模型融合了语义信息与社会影响力，用于交叉学科领域的学者评估，为未来交叉学科合作发现与推荐项目奠定了基础。我们以北京邮电大学的部分学者为样本，验证了该模型的有效性。