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| Potential Scientific Collaborator Recommendation Model Utilizing Dynamic Research Interest and Social Trust |
| Zhong Yuansheng1,2,3, Gao Chengzhen1,2, Zhu Wenqiang3 |
1.School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013
2.Key Laboratory of Data Science in Finance and Economics, Jiangxi University of Finance and Economics, Nanchang 330013
3.School of Software & Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013 |
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Abstract Discovering potential collaborators automatically from massive data is a hot topic in scientific collaboration prediction. Considering that research interests change over time and people with social relationships are more likely to collaborate, a potential scientific collaborator recommendation model “SimTrustRec” is proposed, which integrates dynamic research interest and academic social trust. First, the Latent Dirichlet Allocation model is used to learn the topic distribution of published papers, and dynamic research interests of scholars are mined to calculate the similarity of research interests between two scholars. Second, an academic social network is constructed based on the co-occurrence relationship of scholars and units in papers. Direct academic social trust values are calculated and indirect academic social trust values are then calculated based on the transitivity of social trust. Finally, the possibility of potential collaboration between two scholars is calculated by combining research interest similarity and academic social trust value, and a list of potential collaborators is generated. Experimental results using ArnetMiner datasets demonstrate that the proposed method achieves better performance in terms of recall, hit rate, and mean reciprocal rank compared to existing methods.
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Received: 14 October 2022
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