Method for talented person ability assessment and post matching aiming at college students

Abstract

The invention discloses a method for talent ability evaluation and job matching for college students. The specific steps include the creation of a student-side personal quality model, the creation of an enterprise-side job ability model, a matching and scoring algorithm, an adaptive algorithm, and engineering implementation. The student-side personal quality model adopts the teaching-simulation-practice model, and the job ability model includes two basic ability models and a specific ability model. Starting from the enterprise-side job ability model, the relationship between variables is obtained through linear regression; the overall structure of engineering realization Consists of data acquisition, data preparation and data application. The present invention uses a big data matching algorithm to calculate the matching degree of the personal quality model and the job ability model, and uses this data to obtain each person's competitiveness and application ranking for a specific position, has self-learning ability, improves accuracy, and saves Reduce time cost, improve efficiency, and achieve better results.

Description

technical field [0001] The invention relates to the technical field of recruitment, in particular to a method for evaluating talent ability and job matching for college students. Background technique [0002] There are many human resources recruitment-related technologies on the market. In China, the matching is mainly based on the evaluation results, and some companies use resume analysis algorithms for matching. However, the problem with the existing human resources recruitment methods is that for students, the resumes tend to be homogeneous. Its algorithm efficiency is very poor for the student population, text mining technology is easy to be cracked, and the data is relatively more subjective; the content learned in the existing human resources recruitment method is a means, which cannot be used as a standard for talent recommendation, and the cycle is very long, and the actual There is also a disconnect; in addition, the existing human resource recruitment systems all h...

AI Technical Summary

Problems solved by technology

[0002] There are many human resources recruitment-related technologies on the market. In China, the matching is mainly based on the evaluation results, and some companies use resume analysis algorithms for matching. However, the problem with the existing human resources recruitment methods is that for students, the resumes tend to be homogeneous. Its algorithm efficiency is very poor for the student population, text mining technology is easy to be cracked, and the data is relatively more subjective; the content learned in the existing human resources recruitment method is a means, which cannot be used as a standard for talent recommendation, and the cycle is very long, and the actual There is also a disconnect; in addition, the existing human resource recruitment systems all have the disadvantage of long information update cycles, and users' resume updates and learning progress are low-frequency operations

[0003] However, the assessment cannot really reflect a person's problem-solving ability through simple questions and answers, and the assessment cannot be reused on a person

Traditional resumes provide limited useful information

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