Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

TL;DR

This paper introduces a data-driven, multi-criteria approach using a novel skill popularity model to analyze large-scale recruitment data, effectively ranking job skills and revealing insights about skill popularity and salary correlations.

Contribution

It proposes the Skill Popularity based Topic Model (SPTM), integrating multiple job criteria and skill connections to measure skill popularity from large recruitment datasets.

Findings

Effective ranking of job skills based on multi-criteria analysis

Identification of popular skills linked to high-paying jobs

Validation of the model's effectiveness through real-world data

Abstract

To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid employment.