An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions

Guoliang Si, Hengyi Lv, Hangfei Yuan, Dan Xie, Ce Peng and Gengxin Sun

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-13

Abstract: With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. They have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:2215280

DOI: 10.1155/2021/2215280

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