A method for intelligent matching and screening of human positions

Abstract

The present application provides a kind of intelligent matching screening method of human post, comprising the following steps: collecting the personal characteristic data of personnel to be matched and the post demand data of post to be screened, the two kinds of original data collected are filtered and normalized quantization processing etc., the present application adds dimension weight constraint mechanism and post differentiation weight configuration strategy, limit single dimension weight in fixed interval and weight total sum is normalized, while the corresponding core dimension weight is adjusted for technical class and management class post respectively, it is adhered to the core appeal of different post actual recruitment, further strengthen matching adaptability.Matching threshold supports dynamic adjustment according to the recruitment shortage degree, reduce threshold and expand screening range in shortage post, improve screening precision in high-quality scarce post, the present application can complete the intelligent matching screening of mass personnel and post in batch, and automatically generate personnel priority order list, replace inefficient manual screening, greatly improve the efficiency of human resource post matching processing.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to a method for intelligent matching and screening of human resources positions. Background Technology

[0002] In current human resource recruitment and job matching scenarios, traditional manual resume screening and job matching methods dominate. These methods rely solely on recruiters' subjective experience to manually compare and verify applicants' skills, education, years of work experience, and field of expertise, lacking standardized and quantifiable matching criteria. On the one hand, manual matching is inefficient, struggling to achieve rapid batch matching with massive amounts of applicant and job requirements. On the other hand, subjective human judgment is prone to inconsistencies in standards, significant matching biases, and the loss of high-quality talent. Furthermore, it fails to differentiate core recruitment dimensions for different job types, nor can it flexibly adjust screening criteria based on job sufficiency. The arbitrary weighting of dimensions lacks standardized constraints, making it difficult to accurately quantify the fit between applicants and positions, severely hindering the intelligent, standardized, and precise development of human resource recruitment and matching. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the technical background, the present invention proposes an intelligent matching and screening method for human resource positions. Specifically, it includes the following: A human resource job intelligent matching and screening method includes the following steps: S1. Collect personal characteristic data of the candidates to be matched and job requirement data of the positions to be screened. Personal characteristic data includes four core dimensions: skill tags, years of work experience, education level, and industry field. Job requirement data includes four matching dimensions: job skill requirements, years of experience requirements, education requirements, and job field. S2. Clean, filter, normalize, and quantize the two types of raw data collected, remove missing data in the core dimensions, and convert the text and numerical raw data into standardized quantized values ​​in the 0-1 range. S3. Calculate the independent matching scores for each dimension based on the single-dimensional difference matching formula. The single-dimensional matching formula is: ; Let i be the matching score for the i-th dimension. Quantify the corresponding dimensions for personnel. This formula quantifies the dimensions corresponding to job positions. By calculating the difference between the dimensions of personnel characteristics and job requirements, it intuitively reflects the degree of matching and fit in a single dimension. S4. Introduce dimensional weights and calculate the overall matching score using a weighted summation formula. The overall matching formula is: ; In the formula, M represents the overall matching score between personnel and positions. Here, n is the preset weight for the i-th dimension, n is the total number of matching dimensions, and the sum of the weights of all dimensions is 1. S5. Standardize the dimension weight values ​​using a weight constraint formula, where 0.1 ≤ ≤0.4、 Limit the weight range to a single dimension to complete weight calibration; S6. Set a fixed matching threshold and filter out candidates whose comprehensive matching score is not lower than the matching threshold to complete the initial screening of intelligent job matching.

[0004] Furthermore, the normalization and quantization processing in S2 adopts differentiated quantization rules. For numerical dimensions such as education level and years of work experience, a linear normalization quantization method is used, while for textual dimensions such as skill tags and professional fields, a precise matching quantization method is used. A complete match is assigned a value of 1, a partial match is assigned a value of 0.5, and a non-match is assigned a value of 0.

[0005] Furthermore, the weights for each dimension are preset differently based on the job type. For technical positions, the weight of the skill tag dimension is increased, and for management positions, the weight of the years of work experience and industry field is increased to adapt to the core recruitment requirements of different positions.

[0006] Furthermore, the matching threshold in S6 supports dynamic adjustment, adjusting the threshold parameter according to the degree of job shortage. The matching threshold is lowered for job shortages and raised for high-quality and scarce jobs.

[0007] Furthermore, after completing the initial screening, the selected personnel are sorted in descending order based on the comprehensive matching score M, generating a standardized priority ranking list of personnel matched for specific positions.

[0008] Furthermore, the data cleaning and filtering rules of S2 are to automatically identify and remove personnel data and job requirement data that have two or more missing core dimension data or abnormal data values, so as to ensure the effectiveness of the matching data.

[0009] The above technical solution has the following beneficial effects: This invention sets differentiated normalization and quantification rules, using linear normalization for numerical dimensions such as education level and years of work experience, and precise hierarchical assignment for textual dimensions such as skill tags and professional fields, adapting to the quantification characteristics of different types of data and ensuring the rationality and accuracy of data standardization processing; at the same time, it formulates strict data cleaning rules to remove multi-dimensional missing and abnormal data, ensuring the effectiveness of matching basic data from the source.

[0010] The system adds a dimension weight constraint mechanism and a job-differentiated weight configuration strategy, which limits the weight of a single dimension to a fixed range and normalizes the total weight. At the same time, it increases the weight of the corresponding core dimensions for technical and management positions, which aligns with the actual recruitment needs of different positions and further enhances the matching and adaptability.

[0011] The matching threshold can be dynamically adjusted based on the scarcity of job openings. Lowering the threshold for in-demand positions expands the screening range, while raising the threshold for high-quality, scarce positions improves screening accuracy, adapting to the actual needs of various recruitment scenarios. Finally, this invention can intelligently match and screen massive numbers of personnel with jobs in batches, automatically generating a priority list of personnel, replacing inefficient manual screening, significantly improving the efficiency of human resources job matching, and facilitating the intelligent, standardized, and regulated upgrading of the human resources recruitment process. It has extremely strong practical application value. Attached Figure Description

[0012] Figure 1 This is a flowchart of a human resource job intelligent matching and screening method according to the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example 1, see Figure 1 The method for intelligent matching and screening of human resource positions, as shown, includes the following steps: S1. Collect personal characteristic data of the candidates to be matched and job requirement data of the positions to be screened. Personal characteristic data includes four core dimensions: skill tags, years of work experience, education level, and industry field. Job requirement data includes four matching dimensions: job skill requirements, years of experience requirements, education requirements, and job field. S2. Clean, filter, normalize, and quantize the two types of raw data collected, remove missing data in the core dimensions, and convert the text and numerical raw data into standardized quantized values ​​in the 0-1 range. S3. Calculate the independent matching scores for each dimension based on the single-dimensional difference matching formula. The single-dimensional matching formula is: ; Let i be the matching score for the i-th dimension. Quantify the corresponding dimensions for personnel. This formula quantifies the dimensions corresponding to job positions. By calculating the difference between the dimensions of personnel characteristics and job requirements, it intuitively reflects the degree of matching and fit in a single dimension. S4. Introduce dimensional weights and calculate the overall matching score using a weighted summation formula. The overall matching formula is: ; In the formula, M represents the overall matching score between personnel and positions. Here, n is the preset weight for the i-th dimension, n is the total number of matching dimensions, and the sum of the weights of all dimensions is 1. S5. Standardize the dimension weight values ​​using a weight constraint formula, where 0.1 ≤ ≤0.4、 Limit the weight range to a single dimension to complete weight calibration; S6. Set a fixed matching threshold and filter out candidates whose comprehensive matching score is not lower than the matching threshold to complete the initial screening of intelligent job matching.

[0015] Example 2, based on Example 1, in this example, the normalization and quantification processing in S2 adopts differentiated quantification rules. Linear normalization is used for numerical dimensions such as education level and years of work experience, while precise matching is used for textual dimensions such as skill tags and professional fields. A perfect match is assigned a value of 1, a partial match a value of 0.5, and a non-match a value of 0. The weights of each dimension are preset differently according to the job type. For technical positions, the weight of the skill tag dimension is increased; for management positions, the weight of the years of work experience and professional field dimensions is increased, adapting to the core recruitment requirements of different positions. The matching threshold in S6 supports dynamic adjustment, adjusting the threshold parameter according to the degree of job shortage. The matching threshold is lowered for positions with high recruitment shortages and raised for high-quality, scarce positions. After initial screening, the selected personnel are sorted in descending order based on the comprehensive matching score M, generating a standardized priority ranking list of personnel for job matching. The data cleaning and filtering rules in S2 automatically identify and remove personnel data and job requirement data with two or more missing core dimensions or abnormal data values, ensuring the effectiveness of the matching data.

[0016] In the actual implementation and operation of this invention, multi-dimensional data collection is first carried out, focusing on the four core dimensions of human resource matching. Skill tags, years of work experience, education level, and personal characteristics of the candidates are collected in a unified manner. At the same time, job skill requirements, years of experience requirements, education requirements, and field requirements are collected simultaneously for the job positions. This ensures a one-to-one correspondence between individual characteristics and job requirements, building a standardized data foundation for subsequent accurate matching and avoiding the problems of messy information collection and inconsistent benchmarking dimensions in traditional matching. After data collection, the data enters the data cleaning, filtering, normalization, and quantification stages. The data cleaning stage follows fixed rules, automatically identifying missing core dimensions in the data related to testing personnel and job requirements. Records with two or more missing core dimensions or significantly abnormal data values ​​are directly removed, eliminating invalid and incomplete data at the source to prevent abnormal data from interfering with the matching results. The normalization and quantification process employs differentiated adaptation rules. For quantifiable numerical dimensions such as education level and years of work experience, linear normalization is used to convert the original values ​​to a uniform quantification value in the 0-1 range. For textual descriptive dimensions such as skill tags and professional fields, a precise matching quantification method is used, setting a grading standard of 1 for complete match, 0.5 for partial match, and 0 for no match. This achieves the digital standardization of textual information, allowing both types of data with different attributes to be included in a unified calculation formula for comparison. After data standardization, the independent matching score for each dimension is calculated using a single-dimensional difference matching formula. ; Using the absolute difference between the quantitative values ​​of the personnel's corresponding dimension and the quantitative values ​​of the job's corresponding dimension as the benchmark, the smaller the difference, the higher the matching score; the larger the difference, the lower the matching score. This method can intuitively and accurately reflect the degree of fit between a person's single-dimensional trait and the job's corresponding requirements, transforming the abstract dimensional adaptation relationship into a concrete quantitative score, providing single-dimensional score support for comprehensive matching calculations. After obtaining the independent matching scores for each dimension, a weighted summation is performed using dimension weights to obtain the comprehensive matching score. The weighted summation formula is as follows. ; It integrates matching scores and the importance of four dimensions, with dimension weights preset differently based on job type. For technical positions, the weight of the skill tag dimension is increased to match the recruitment characteristics of technical positions that emphasize professional skills. For management positions, the weight of the years of work experience and industry field dimensions is increased to match the recruitment requirements of management positions that emphasize work experience and industry background. At the same time, the weight constraint formula standardizes all weight values, limiting the weight of a single dimension to the range of 0.1 to 0.4, and ensuring that the sum of all dimension weights equals 1. This avoids a single dimension weight being too high and dominating the matching results, and also achieves standardization and normalization of the weight system, making the weight allocation scientific and reasonable. After the comprehensive matching score is calculated, intelligent preliminary screening is carried out based on the preset matching threshold. The matching threshold is not fixed and has dynamic adjustment capabilities. It can be flexibly adjusted according to the degree of job shortage. For urgently needed positions with large labor gaps and urgent recruitment, the matching threshold is appropriately lowered to expand the scope of candidates to ensure recruitment progress. For high-end, high-quality positions with scarce talent, the matching threshold is raised to strictly screen highly suitable candidates, adapting to the actual staffing needs of different recruitment scenarios. After the preliminary screening is completed, the qualified candidates are sorted in descending order according to the comprehensive matching score, and a standardized priority ranking list of candidates is automatically generated. This allows recruiters to directly conduct subsequent interviews and hiring based on priority. The entire process of data collection, processing, score calculation, weight calibration, intelligent screening, and priority ranking is fully automated without human intervention. It balances matching efficiency, accuracy, and scenario adaptability, and is suitable for various human resource matching scenarios such as enterprise bulk recruitment, intelligent matching on human resource platforms, and talent pool job recommendations.

[0017] Taking the recruitment of Java developers in a technical field as an example, this invention's method is applied to conduct a matching practice along four dimensions: skill tags, years of work experience, education level, and industry field. First, S1 data collection is performed. The job requirements are a bachelor's degree or above, more than 3 years of development experience, proficiency in Java frameworks, and an experience in the internet industry. Information on three job seekers is then filtered. After S2 data cleaning, no data is missing in two or more dimensions, so no data needs to be removed. Next, normalization and quantification are performed. Education level and years of work experience are linearly normalized, while skills and industry field are assigned values ​​according to rules: 1 for a perfect match, 0.5 for a partial match, and 0 for no match. This yields the four-dimensional quantified values ​​for the job position: Q = [1.0, 0.8, 1.0, 1.0], and the quantified values ​​for job seeker A: P = [1.0, 0.75, 1.0, 1.0], job seeker B: P = [0.5, 0.5, 0.8, 0.5], and job seeker C: P = [1.0, 0.8, 1.0, 0.5]. Then, a single-dimensional matching formula is applied. Calculate the scores for each dimension, and then set weights differently according to the rules for technical positions, following the principle of 0.1 ≤ Given the constraint that the sum of weights is ≤0.4 and the total weight is 1, the weights for skill tags are set to 0.4, years of work experience to 0.2, education level to 0.2, and industry field to 0.2. Then, a comprehensive matching formula is used. Calculate the total score, and find that A's comprehensive matching score is 0.99, B's is 0.60, and C's is 0.90.

[0018] The job posting is set as a regular recruitment posting, with a preset matching threshold of 0.7. Applicants A and C with scores above the threshold are selected, while applicant B with a score below the threshold is removed. Finally, the positions are sorted in descending order of their overall matching scores, generating a recruitment priority list with A first, followed by C. This application example fully utilizes the invention's quantitative rules, matching formulas, weight constraints, and dynamic filtering logic to avoid biases from subjective human judgment, achieving standardized quantitative matching of resumes. It can quickly complete the initial screening of batches of talent, adapting to the intelligent recruitment needs of various enterprise positions.

[0019] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent matching and screening of human resource positions, characterized in that, Includes the following steps: S1. Collect personal characteristic data of the candidates to be matched and job requirement data of the positions to be screened. Personal characteristic data includes four core dimensions: skill tags, years of work experience, education level, and industry field. Job requirement data includes four matching dimensions: job skill requirements, years of experience requirements, education requirements, and job field. S2. Clean, filter, normalize, and quantize the two types of raw data collected, remove missing data in the core dimensions, and convert the text and numerical raw data into standardized quantized values ​​in the 0-1 range. S3. Calculate the independent matching scores for each dimension based on the single-dimensional difference matching formula. The single-dimensional matching formula is: ； Let i be the matching score for the i-th dimension. Quantify the corresponding dimensions for personnel. This formula quantifies the dimensions corresponding to job positions. By calculating the difference between the dimensions of personnel characteristics and job requirements, it intuitively reflects the degree of matching and fit in a single dimension. S4. Introduce dimensional weights and calculate the comprehensive matching score using a weighted summation formula. The comprehensive matching formula is as follows: ； In the formula, M represents the overall matching score between personnel and positions. Here, n is the preset weight for the i-th dimension, n is the total number of matching dimensions, and the sum of the weights of all dimensions is 1. S5. Standardize the dimension weight values ​​using a weight constraint formula, where the weight constraint formula is 0.1 ≤ ≤0.4、 Limit the weight range to a single dimension to complete weight calibration; S6. Set a fixed matching threshold and filter out candidates whose comprehensive matching score is not lower than the matching threshold to complete the initial screening of intelligent job matching.

2. The intelligent matching and screening method for human resource positions according to claim 1, characterized in that, The normalization and quantization process in S2 adopts differentiated quantization rules. For numerical dimensions such as education level and years of work experience, a linear normalization quantization method is used, while for textual dimensions such as skill tags and professional fields, a precise matching quantization method is used. A complete match is assigned a value of 1, a partial match is assigned a value of 0.5, and a non-match is assigned a value of 0.

3. The intelligent matching and screening method for human resource positions according to claim 1, characterized in that, The weights for each dimension are preset differently based on the job type. For technical positions, the weight of the skill tag dimension is increased, and for management positions, the weight of the years of work experience and professional field is increased to adapt to the core recruitment requirements of different positions.

4. The intelligent matching and screening method for human resource positions according to claim 1, characterized in that, The matching threshold in S6 supports dynamic adjustment. The threshold parameter is adjusted according to the degree of job shortage. The matching threshold is lowered for job shortages and raised for high-quality and scarce jobs.

5. The intelligent matching and screening method for human resource positions according to claim 1, characterized in that, After the initial screening is completed, the selected personnel are sorted in descending order based on the comprehensive matching score M, and a standardized priority ranking list of personnel for job matching is generated.

6. The intelligent matching and screening method for human resource positions according to claim 1, characterized in that, The data cleaning and filtering rules of S2 are to automatically identify and remove personnel data and job requirement data that have two or more missing core dimension data or abnormal data values, so as to ensure the effectiveness of the matching data.

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