Automatic credit evaluation method and system based on job seeker behavior and job application data

Abstract

The invention relates to the technical field of human resource management, in particular to an automatic credit evaluation method and system based on job seeker behaviors and job application data, and the method comprises the following steps: data collection, data processing and analysis, intelligent recommendation and dynamic monitoring. The method has the beneficial effects that based on big data analysis, human prejudice is reduced, and evaluation accuracy is improved; a large amount of job application data is automatically processed, and recruitment efficiency is remarkably improved; the credit of the job seeker is evaluated in multiple dimensions, including behavioral habits, professional ability, communication attitudes and the like; the credit state of the job seeker is updated in real time, and changes in the recruitment process are adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and particularly to an automatic credit evaluation method and system based on job seeker behavior and job application data. Background Art

[0002] In the current recruitment market, enterprises are faced with the resume screening and evaluation of a large number of job seekers. The traditional manual review method is not only inefficient, but also difficult to comprehensively and objectively evaluate the true ability and integrity of job seekers. With the development of big data and artificial intelligence technologies, it has become possible to use the online behavior data of job seekers for credit evaluation, which helps enterprises to more accurately match suitable talents and reduce recruitment risks.

[0003] Therefore, there is a need for an automatic credit evaluation method and system based on job seeker behavior and job application data, which can automatically, objectively and efficiently evaluate the credit level by comprehensively analyzing multi-dimensional data of job seekers on job hunting websites, and provide decision-making support for employers. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic credit evaluation method and system based on job seeker behavior and job application data to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An automatic credit evaluation method based on job seeker behavior and job application data, comprising the following steps:

[0006] Data collection step: Collect job application data of job seekers in multiple aspects, specifically including:

[0007] Record the frequency of job seekers submitting resumes, the matching degree of target positions, and the distribution of submission times;

[0008] Evaluate the detail level of resume information, and verify the authenticity of educational background and work experience through third-party data comparison;

[0009] Record the interview participation degree, such as whether to attend the interview on time and the evaluation of interview performance, and at the same time count the interview passing rate;

[0010] Analyze the frequency, quality, response speed of communication between job seekers and employers, as well as the politeness and professionalism of communication content.

[0011] Preferably, it further includes a data processing and analysis step, specifically:

[0012] Clean the collected data to remove outliers and duplicate data to ensure data quality;

[0013] Extract key features from the cleaned data, such as resume update frequency, adequacy of interview preparation, and communication efficiency;

[0014] Adopt machine learning algorithms, such as logistic regression, random forest, and neural network, to build a credit evaluation model, and use historical data and expert scoring results to train the model;

[0015] According to the trained model, calculate a credit score for each job seeker. The higher the score, the higher the credit level.

[0016] Preferably, it also includes the credit report generation operation in the result output and application step, specifically:

[0017] Generate a detailed credit report for the recruiter based on the calculated credit score, other key behavior indicators, and potential risk warnings. The report content covers the credit score, analysis of key behavior indicators, and potential risk warnings.

[0018] Preferably, it also includes the intelligent recommendation operation in the result output and application step, specifically:

[0019] Based on the credit evaluation results, use intelligent algorithms to screen and rank job seekers who meet the requirements of the recruiter, and recommend these job seekers to the recruiter to improve the recruitment efficiency and quality.

[0020] Preferably, it also includes the dynamic monitoring operation in the result output and application step, specifically:

[0021] Continuously track the credit changes of job seekers. When the credit score of a job seeker drops below a preset threshold, automatically send a warning message to the recruiter. The warning message includes the specific situation of the credit decline of the job seeker, so that the recruiter can adjust the recruitment strategy in a timely manner.

[0022] A system for an automatic credit evaluation method based on job seeker behavior and job application data, including a data collection module, which is used for:

[0023] Record the resume submission situation of job seekers, covering the frequency of resume submission, the matching degree of target positions, and the submission time distribution;

[0024] Evaluate the resume integrity situation, specifically involving the detail level of resume information, and verify the authenticity of educational background and work experience through third-party data comparison;

[0025] Record the interview situation, including interview participation, such as whether to attend the interview on time and the interview performance evaluation, and at the same time count the interview passing rate;

[0026] Analyze the online communication situation, including the frequency, quality, response speed of communication between job seekers and employers, as well as the politeness and professionalism of communication content.

[0027] Preferably, it further includes a data processing and analysis module, which is used for:

[0028] Clean the collected data to remove outliers and duplicate data to ensure data quality;

[0029] Extract key features from the cleaned data, such as resume update frequency, interview preparation sufficiency, communication efficiency;

[0030] Adopt machine learning algorithms, such as logistic regression, random forest, neural network, to build a credit evaluation model, and use historical data and expert scoring results to train the model;

[0031] According to the trained model, calculate a credit score for each job seeker. The higher the score, the higher the credit level.

[0032] Preferably, it further includes a credit report generation unit in the result output and application module, which is used for:

[0033] Generate a detailed credit report of the job seeker for the recruiter according to the calculated credit score and other key behavior indicators and potential risk warnings. The report content includes credit score, key behavior indicator analysis, and potential risk warnings.

[0034] Preferably, it further includes an intelligent recommendation unit in the result output and application module, which is used for:

[0035] Based on the credit evaluation results, use intelligent algorithms to screen and rank job seekers who meet the requirements of the recruiter, and recommend these job seekers to the recruiter to improve the recruitment efficiency and quality.

[0036] Preferably, it further includes a dynamic monitoring unit in the result output and application module, which is used for:

[0037] Continuously track the credit change situation of the job seeker. When the credit score of the job seeker drops below the preset threshold, automatically send a warning message to the recruiter. The warning message includes the specific situation of the credit decline of the job seeker, so that the recruiter can adjust the recruitment strategy in time.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] The automatic credit evaluation method and system based on job seeker behavior and job seeking data proposed by the present invention, based on big data analysis, reduces human bias and improves evaluation accuracy; automatically processes a large amount of job seeking data, significantly improves recruitment efficiency; evaluates the credit of job seekers from multiple dimensions, including behavior habits, professional abilities, communication attitudes, etc.; and updates the credit status of job seekers in real time to adapt to changes in the recruitment process. Brief Description of the Drawings

[0040] Figure 1 This is the flowchart of the method of the present invention. Detailed implementation manners

[0041] In order to clearly and completely describe the objectives, technical solutions of the present invention, and make the advantages more clearly understood, the following further details the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, not to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0042] Example 1, please refer to Figure 1 , the present invention provides a technical solution: an automatic credit evaluation method based on job seeker behavior and job hunting data, including the following steps: Data collection step: Collect job hunting data from multiple aspects of job seekers, specifically including: recording the frequency of job seekers submitting resumes, the matching degree of target positions, and the distribution of submission times; evaluating the detail level of resume information, and verifying the authenticity of educational background and work experience through third-party data comparison; recording interview participation, such as whether to attend the interview on time and the evaluation of interview performance, and at the same time counting the interview passing rate; analyzing the frequency, quality, response speed of communication between job seekers and employers, as well as the courtesy and professionalism of communication content.

[0043] It also includes a data processing and analysis step, specifically: cleaning the collected data to remove outliers and duplicate data to ensure data quality; extracting key features from the cleaned data, such as resume update frequency, interview preparation sufficiency, communication efficiency; using machine learning algorithms, such as logistic regression, random forest, neural network, to build a credit evaluation model, and training the model using historical data and expert scoring results; calculating a credit score for each job seeker according to the trained model, and the higher the score, the higher the credit level.

[0044] It also includes the credit report generation operation in the result output and application step, specifically: generating a detailed job seeker credit report for the recruiter according to the calculated credit score and other key behavior indicators and potential risk warnings, and the report content covers credit score, key behavior indicator analysis, and potential risk warnings.

[0045] It also includes the intelligent recommendation operation in the result output and application step, specifically: based on the credit evaluation results, using intelligent algorithms to screen and rank job seekers who meet the requirements of the recruiter, and recommending these job seekers to the recruiter to improve the recruitment efficiency and quality.

[0046] It also includes the dynamic monitoring operation in the result output and application steps, specifically: continuously tracking the credit changes of job seekers, and when the credit score of a job seeker drops below the preset threshold, automatically sending a warning message to the recruiter. The warning message includes the specific situation of the job seeker's credit decline, so that the recruiter can adjust the recruitment strategy in a timely manner.

[0047] Embodiment 2, based on Embodiment 1, proposes a system for an automatic credit evaluation method based on job seeker behavior and job application data, including the following steps:

[0048] 1. Data collection module

[0049] (1) Resume submission situation: Record the frequency of resume submission by job seekers, the matching degree of target positions, the distribution of submission time, etc.

[0050] (2) Resume integrity situation: Evaluate the detail level of resume information, educational background, and verification of the authenticity of work experience (such as

[0051] through third-party data comparison).

[0052] (3) Interview situation: Record interview participation (such as whether attending on time, interview performance evaluation), interview pass rate, etc.

[0053] (4) Online communication situation: Analyze the frequency, quality, response speed of communication between job seekers and employers, and the

[0054] politeness and professionalism of communication content.

[0055] 2. Data processing and analysis module

[0056] (1) Data cleaning: Remove outliers and duplicate data to ensure data quality.

[0057] (2) Feature extraction: Extract key features from the above data, such as resume update frequency, interview preparation sufficiency, communication

[0058] efficiency, etc.

[0059] (3) Model construction: Use machine learning algorithms (such as logistic regression, random forest, neural network, etc.) to construct a credit evaluation

[0060] model, and train the model according to historical data and expert scoring results.

[0061] (4) Credit score calculation: Calculate a credit score for each job seeker according to the model output. The higher the score, the higher the

[0062] credit level.

[0063] 3. Result output and application module

[0064] (1) Credit report generation: Provide detailed credit reports of job seekers for employers, including credit scores, analysis of key behavior indicators, potential risk warnings, etc.

[0065]

[0066] (2) Intelligent recommendation: Based on the credit evaluation results, intelligently recommend job seekers meeting the requirements to employers to improve the recruitment efficiency and quality.

[0067]

[0068] (3) Dynamic monitoring: Continuously track the credit changes of job seekers, give early warnings for credit decline situations, and help employers adjust strategies in a timely manner.

[0069]

[0070] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic credit evaluation method based on job seeker behavior and job hunting data, characterized in that: It includes the following steps: Data collection step: Collect job hunting data of job seekers from multiple aspects, specifically including: Record the frequency of resume submission by job seekers, the matching degree of target positions, and the distribution of submission time; Evaluate the detail level of resume information, and verify the authenticity of educational background and work experience through third-party data comparison; Record interview participation, such as whether to attend the interview on time and the evaluation of interview performance, and at the same time count the interview passing rate; Analyze the frequency, quality, response speed of communication between job seekers and employers, as well as the politeness and professionalism of communication content.

2. The automatic credit evaluation method based on job seeker behavior and job seeking data according to claim 1, characterized in that: It also includes a data processing and analysis step, specifically: Clean the collected data to remove outliers and duplicate data to ensure data quality; Extract key features from the cleaned data, such as resume update frequency, interview preparation sufficiency, communication efficiency; Adopt machine learning algorithms, such as logistic regression, random forest, neural network, to build a credit evaluation model, and use historical data and expert scoring results to train the model; According to the trained model, calculate a credit score for each job seeker, and the higher the score, the higher the credit level.

3. The automatic credit evaluation method based on job seeker behavior and job seeking data according to claim 2, characterized in that: It also includes the operation of generating a credit report in the result output and application step, specifically: Generate a detailed credit report of job seekers for the recruitment party according to the calculated credit score, other key behavior indicators, and potential risk warnings. The report content covers credit score, key behavior indicator analysis, and potential risk warnings.

4. An automatic credit evaluation method based on job seeker behavior and job seeking data according to claim 3, characterized in that: It also includes the intelligent recommendation operation in the result output and application step, specifically: Based on the credit evaluation results, use intelligent algorithms to screen and rank job seekers who meet the requirements of the recruitment party, and recommend these job seekers to the recruitment party to improve the recruitment efficiency and quality.

5. The automatic credit evaluation method based on job seeker behavior and job hunting data according to claim 4, characterized in that: It also includes the dynamic monitoring operation in the result output and application step, specifically: Continuously track the credit change situation of job seekers. When the credit score of a job seeker drops below the preset threshold, automatically send a warning message to the recruitment party. The warning message includes the specific situation of the credit drop of the job seeker, so that the recruitment party can adjust the recruitment strategy in time.

6. A system for the automatic credit evaluation method based on job seeker behavior and job seeking data according to claim 5, characterized in that: It includes a data collection module, which is used for: Record the resume submission situation of job seekers, covering the frequency of resume submission, the matching degree of target positions, and the distribution of submission time; Evaluate the resume integrity situation, specifically involving the detail level of resume information, and verify the authenticity of educational background and work experience through third-party data comparison; Record the interview situation, including interview participation, such as whether to attend the interview on time and the evaluation of interview performance, and at the same time count the interview passing rate; Analyze the online communication situation, including the frequency, quality, response speed of communication between job seekers and employers, as well as the politeness and professionalism of communication content.

7. A system according to claim 6, characterized in that: It also includes a data processing and analysis module, which is used for: Clean the collected data to remove outliers and duplicate data to ensure data quality; Extract key features from the cleaned data, such as resume update frequency, interview preparation sufficiency, communication efficiency; Adopt machine learning algorithms, such as logistic regression, random forest, neural network, to build a credit evaluation model, and use historical data and expert scoring results to train the model; Based on the trained model, a credit score is calculated for each job seeker, and the higher the score, the higher the credit level.

8. A system according to claim 7, characterized in that: It also includes a credit report generation unit in the result output and application module, which is used for: Generating a detailed credit report of the job seeker for the recruiter according to the calculated credit score, other key behavioral indicators and potential risk warnings. The report content includes the credit score, analysis of key behavioral indicators and potential risk warnings.

9. A system according to claim 8, wherein: It also includes an intelligent recommendation unit in the result output and application module, which is used for: Based on the credit evaluation results, using intelligent algorithms to screen and rank job seekers who meet the requirements of the recruiter, and recommending these job seekers to the recruiter to improve the recruitment efficiency and quality.

10. A system according to claim 9, characterized in that: It also includes a dynamic monitoring unit in the result output and application module, which is used for: Continuously tracking the credit changes of job seekers. When the credit score of a job seeker drops below the preset threshold, an early warning message will be automatically sent to the recruiter. The early warning message includes the specific situation of the credit decline of the job seeker, so that the recruiter can adjust the recruitment strategy in a timely manner.

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