一种面向跨学科合作的新型学者嵌入模型
A Novel Scholar Embedding Model for Interdisciplinary Collaboration
AI总结：
针对传统学者评估方法忽视跨学科合作的问题，提出融合全局语义信息与社交影响力的学者嵌入模型，实现研究趋势实时追踪，并在北京邮电大学样本上验证有效性，为跨学科合作发现与推荐奠定基础。
摘要：
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，仅基于个人成就的传统学者评估方法未考虑跨学科合作，给跨学科评估与推荐带来挑战。为解决此问题，我们提出一种学者嵌入模型，基于全局语义信息和社交影响力量化并表征学者，实现学者研究趋势的实时追踪。该模型融合语义信息与社交影响力用于跨学科评估，为未来跨学科合作发现与推荐项目奠定基础。我们在北京邮电大学的学者样本上验证了模型的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
