Recruitment and application matching method

Abstract

The invention discloses a recruitment and application matching method. Compared with the existing character and word group matching method in the industry, the recruitment and application matching method can convert word groups into background big data precisely, and provides an automatic matching function with excellent precision for positions and resumes through adopting a data-data matching method, greatly shortens the time for filtering resumes of a recruiter; meanwhile, the recruitment and application matching method can further help a job seeker to create a resume by using professional language through providing detailed content tags, or automatically generates a resume according to the tags selected by the job seeker, thereby greatly saving time cost of the job seeker, and improving the success rate of application.

Description

Technical field [0001] The invention relates to the field of management systems, specifically a recruitment and application matching method. Background technique [0002] Currently, on existing recruitment platforms (websites and the Internet), data and content are used to match positions and resumes. Both positions and resumes are divided into two parts: data and content: [0003] The position is divided into two parts: [0004] 1-Data part: such as: work location, industry category, job category, work highlights, education, years of experience, etc. The proportion of this part of data in positions: 20-30%. [0005] 2-Content section: Job requirements and job description. The proportion of this part of data in positions: 70-80%. [0006] The resume is also divided into two parts: [0007] 1-Data part: such as: work location, industry category, job category, work highlights, education, years of experience, etc. This part of the data generally accounts for 20-30% of th...

AI Technical Summary

Problems solved by technology

Therefore, the content does not match, which leads to the low accuracy of the existing matching system, and the matching accuracy is only about 20-30%

Therefore, for recruiters, it is extremely time-consuming to manually check and filter each resume to see if they are suitable for their requirements.

At the same time, in terms of job seekers, although some job seekers have strong professional abilities, they lack expressive skills, so they cannot reasonably express their professional abilities through their resumes, which will undoubtedly reduce their chances of getting a job.

[0010] In order to solve the above two problems, that is, the low content matching between job titles and resumes, and the low success rate of job hunting due to insufficient resume writing ability of talents, we created big data to replace the parts of job titles and resume content