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.
Highlights
The value assessment of job skills is important for companies to select and retain the right talent
We introduce a market-oriented definition of skill value, and formulate the task of skill value assessment as the Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the job’s contextual information, and the job salary is assumed to be influenced by the context-aware value of these skills
We propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value from the massive job postings
Summary
The value assessment of job skills is important for companies to select and retain the right talent. We introduce a market-oriented definition of skill value, and formulate the task of skill value assessment as the Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the job’s contextual information, and the job salary is assumed to be influenced by the context-aware value of these skills. Along this line, we propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value from the massive job postings. Extensive experiments on a real-world dataset clearly validate that SSCN can assign meaningful value to job skills in various job contexts, and outperforms state-of-the-art models in terms of job salary prediction
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