Market-oriented job skill valuation with cooperative composition neural network

Ying Sun, Fuzhen Zhuang (), Hengshu Zhu (), Qi Zhang, Qing He and Hui Xiong ()
Additional contact information
Ying Sun: Institute of Computing Technology, CAS
Fuzhen Zhuang: Institute of Computing Technology, CAS
Hengshu Zhu: Baidu Inc.
Qi Zhang: Baidu Inc.
Qing He: Institute of Computing Technology, CAS
Hui Xiong: Rutgers, the State University of New Jersey

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract The value assessment of job skills is important for companies to select and retain the right talent. However, there are few quantitative ways available for this assessment. Therefore, we propose a data-driven solution to assess skill value from a market-oriented perspective. Specifically, we formulate the task of job skill value assessment as a Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the contextual information of jobs, and the job salary is assumed to be jointly influenced by the context-aware value of these skills. Then, we propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value based on the massive job postings. Experiments show that SSCN can not only assign meaningful value to job skills, but also outperforms benchmark models for job salary prediction.

Date: 2021
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DOI: 10.1038/s41467-021-22215-y

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