Collaboration Recommendation of Finance Research Based on Multi-feature Fusion
Yu Chuanming1, Gong Yutian1, Zhao Xiaoli1, An Lu2()
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2School of Information Management, Wuhan University, Wuhan 430072, China
[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.
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