MDJOSC: matching digital talents and job titles in open source communities

Abstract

Open source communities have a wealth of digital talents, who are urgently needed by various industries under the digitalization process of the entire society. However, barriers exist between digital talents in open source communities and employers. On one hand, open source contributors wonder whether their expertise matches the requirements of specific jobs; On the other hand, developers working on small open source projects are less likely to get recognition from employers, compared with those contributing to well-known projects. To bridge this gap, we propose a new task, matching digital talents and job titles in open source communities, which measures the matching degrees between digital talents with open source experience and job titles requiring digital skills. To solve the task, we construct a heterogeneous information network connecting open source communities and job markets, and propose a semi-supervised network alignment model to augment the connectivity of the network. Then we employ a graph neural network to learn the representations of the digital talents and the job titles from the augmented network, based on which we measure the matching degrees between them. Experimental results demonstrate that our method achieves improvements of at least 5.34, 3.52, 2.37, 2.93, and 8.21 in accuracy, precision, recall, F1, and AUC compared to other possible solutions.

Graphical abstract

Keywords

open source community / matching digital talents and job titles / heterogeneous information network / network alignment

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