基于多特征融合的金融领域科研合作推荐研究

Collaboration Recommendation of Finance Research Based on Multi-feature Fusion

【目的】科研合作关系是一种重要的社会网络。为了促进科研合作,提高科研生产率,对金融领域的科研合作推荐模型进行研究。【方法】建立金融领域个人、机构和区域三个层面的科研合作网络,提出一种新的融合基于邻居节点和基于路径的网络特征的科研合作推荐模型,并从个人、机构和区域三个层面进行实证检验。【结果】通过对2000年到2014年刊载的68 905篇金融领域的文章进行分析并构建科研合作网络,在个人、机构和区域三个层面上,基于特征融合的链接预测方法的AUC值分别为84.25%、87.34%和91.84%,均高于基于邻居节点的算法和基于路径的算法的AUC值。【局限】在进行训练集和测试集选取的时候只按时间进行切分,有待使用更多的切分方式对实验结果进行优化。【结论】本文有助于金融科研领域的个人、机构和区域寻求合作对象,为进行科研网络的研究以及科研合作推荐的学者提供新的研究方法和思路。

[Objective] Research collaboration builds an important social network system. This paper proposes a new recommendation model for research collaboration in finance, aiming to promote the scientific collaboration and improve research productivity. [Methods] First, we established the scientific collaboration networks at individuals, institutions and regions levels. Then, we established a recommendation model based on network neighbors and paths. Finally, we conducted empirical study to examine the model at three levels. [Results] A total of 68 905 articles published from 2000 to 2014 on finance were analyzed to construct their research collaboration networks. The AUC values of the proposed model at individual, institutional and regional levels were 84.25%, 87.34%, and 91.84%, respectively, which were higher than those of the traditional algorithms. [Limitations] The training and testing sets were only classified by time. More segmentation methods were needed to optimize the new model. [Conclusions] This study helps researchers find collaboration opportunities, and provides new directions for studies on scientific collaboration networks.