Intelligent matching and layering method for talent posts

Abstract

The invention discloses an intelligent talent post matching and layering method, and relates to the technical field of artificial intelligence and human resources. Through bidirectional collaborative filtering calculation, when matching scores of candidates to posts and matching scores of posts to candidates are calculated, feature similarity of candidate capabilities and post requirements is considered at the same time; calculating feature similarity based on a cosine similarity algorithm according to a candidate vocational preference matching degree, a candidate vocational preference matching degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree, a candidate vocational preference integrating degree and a candidate vocational proficiency integrating degree, a candidate vocational preference integrating degree and a candidate vocational proficiency integrating degree; according to the mode, the adaptation degree of the two parties can be comprehensively and accurately evaluated, the matching deviation caused by only depending on single dimension is avoided, the accuracy of initial matching of talents and posts is greatly improved, a high-quality candidate person pool is provided for the subsequent recruitment process, the time cost of invalid resume screening of enterprises is reduced, and the recruitment efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence and human resources, and specifically to a talent-post intelligent matching and layering method. BACKGROUND

[0002] In today's highly competitive business environment, the success of an enterprise is highly dependent on the ability to accurately acquire talents that are suitable for the post. Job seekers also aspire to quickly find a post that matches their abilities and career plans. With the expansion of the size of enterprises and the diversification of their businesses, the demand for posts is increasingly complex and diverse, covering emerging technologies, cross-disciplinary comprehensive capabilities, and other dimensions. At the same time, the backgrounds, skills, and potential of job seekers in the talent market are highly differentiated. Under the traditional recruitment model, there is a serious information asymmetry between enterprises and job seekers, resulting in low recruitment efficiency and poor matching accuracy. This not only wastes a lot of time and effort for both parties, but also may hinder the innovation and development of enterprises and the career growth of individuals.

[0003] The existing technology has many shortcomings in talent-post matching. Most traditional matching methods only rely on limited explicit information such as education and work experience for screening, making it difficult to deeply explore the compatibility of the candidate's potential abilities and the deep needs of the post. For example, for some cutting-edge technical posts that require high innovation and learning abilities, relying on past project experience and skill certificates presented on the resume cannot accurately determine whether the candidate has the ability to quickly adapt to new technology iterations and solve complex problems. Moreover, these methods lack a dynamic adjustment mechanism, making it impossible to optimize the matching strategy in real time according to market changes, enterprise strategy adjustments, and candidate feedback during the recruitment process. When facing temporary changes in post requirements or the emergence of new talent types, the matching results are often lagging and inaccurate, resulting in a single matching dimension and making it difficult to comprehensively evaluate the suitability of talents and posts.

[0004] In summary, there is an urgent need in the current talent-post matching field for a method that can break through traditional limitations, fully exploit the value of multi-source data, and achieve accurate, dynamic, and personalized matching to meet the growing needs of efficient matching for both enterprises and job seekers and improve the overall operational efficiency of the talent market. SUMMARY

[0005] The talent post intelligent matching layering method can calculate the matching score of the candidate to the post and the matching score of the post to the candidate through bidirectional collaborative filtering calculation, simultaneously consider the feature similarity of the candidate ability and the post requirement, and the matching degree of the candidate professional preference and the post, the matching degree of the post skill demand and the candidate skill proficiency, calculate the feature similarity based on a cosine similarity algorithm, then combine the professional preference matching degree, the skill proficiency matching degree and other factors, and obtain the comprehensive matching score through weighted calculation, the method can comprehensively and accurately evaluate the adaptation degree of both sides, avoids the matching deviation caused by relying on a single dimension, greatly improves the accuracy of the initial matching of talents and posts, provides a high-quality candidate pool for the subsequent recruitment process, effectively reduces the time cost of the enterprise for invalid resume screening, and improves the recruitment efficiency.

[0006] The talent post intelligent matching layering method provided by the present application comprises the following specific steps:

[0007] S100, data preprocessing and vector construction: candidate data and post data are collected, candidate features and post requirements are extracted through natural language processing technology, a candidate ability feature vector V c and a post demand feature vector V j are constructed respectively, and V c and V j are standardized into numerical vectors of a unified dimension;

[0008] S200, bidirectional collaborative filtering calculation: the collaborative filtering of the candidate to the post and the collaborative filtering of the post to the candidate are executed in parallel, the first matching score S1 of the candidate to the post and the second matching score S2 of the post to the candidate are calculated;

[0009] S300, bidirectional score aggregation and real-time calibration: S1 and S2 are received, S1 weight W1 and S2 weight W2 are determined based on the post characteristics, and the final matching score S final is calculated, meanwhile, behavior feedback signals of HR and the candidate in the recruitment process are collected, W1 and W2 are dynamically adjusted according to the behavior feedback signals;

[0010] S400, bidimensional layering evaluation: the S final is used for instant ability evaluation to screen high-matching-degree candidates, and an instant ability level is generated, meanwhile, a potential value evaluation is performed based on a potential index system to generate a potential value level;

[0011] S500, four-quadrant layering and strategy generation: the instant combat effectiveness evaluation result and the potential value evaluation result are mapped to a four-quadrant layering matrix, and corresponding recruitment strategy suggestions are generated for the candidate groups in each quadrant from a supply-demand docking platform.

[0012] Further, in the S100, the candidate data includes candidate education information, skill certification information, work experience information, project achievement information, and career preference information;

[0013] The post data includes post responsibility description information, skill requirement information, education requirement information, work experience requirement information, and post development path information;

[0014] The natural language processing technology includes word segmentation processing, keyword extraction, and entity recognition. After extracting the candidate features and post features through the natural language processing technology, the candidate features are quantified according to the preset feature dimension, and the candidate ability vector V c and the post requirement vector V j .

[0015] Further, in the S200, the candidate and the post are cooperatively filtered:

[0016] Taking the candidate ability feature vector V c as input, the cosine similarity algorithm is used to calculate the similarity with the post requirement vector in the full post database, to generate a first matching score S1, wherein wherein, represents the ability feature vector of the current candidate, represents the requirement vector of the i-th similar post in the post database, and N represents the number of the first N posts most similar to the current candidate.

[0017] Further, in the S200, the post and the candidate are cooperatively filtered:

[0018] Taking the post demand feature vector as input V j , the cosine similarity algorithm is used to calculate the similarity with the candidate ability vector in the full talent database, to generate a second matching score S2, wherein wherein, represents the demand feature vector of the current post, represents the ability vector of the k-th similar candidate in the talent database, and M represents the number of the first M candidates most similar to the current post.

[0019] Further, in the S300, a post classification system is defined in advance, the post is divided into multiple categories, and an initial weight configuration strategy is preset for each category of post;

[0020] By analyzing the keyword distribution in the post requirement vector V j , one or more categories to which the target post belongs are identified, and the corresponding weight configuration strategy is called to assign initial values to W1 and W2, and the final matching score is calculated by weighting.

[0021] The final matching score S final =W1×S1+W2×S2, where W1 and W2 are the dynamic weight coefficients of the first matching score S1 and the second matching score S2, respectively, and are dynamically adjusted according to the behavioral feedback signal;

[0022] The behavioral feedback signals include the HR's browsing time for candidates, rejection and hiring actions, as well as the candidates' response to job applications and rejection actions.

[0023] When a candidate with a high S2 score is recommended and adopted by HR, the weight of W2 is strengthened; conversely, if a candidate with a high S1 score is recommended, the weight of W1 is strengthened. Through continuous online learning, the weighting strategy is made to continuously approach the optimal configuration for a specific position in the company, achieving personalized self-optimization.

[0024] Furthermore, in S400, regarding real-time capability assessment:

[0025] Based on the final matching score S final Set the matching threshold S th Candidates are divided into real-time capability levels based on the matching threshold, and the real-time capability levels include high real-time capability and medium real-time capability.

[0026] When S final >S th At that time, candidates are classified into the high immediate capability level, indicating that their current capabilities match the job requirements, and they enter the interview and hiring process;

[0027] When S th2 <S final ≤S th Candidates are categorized into the immediate competency level, indicating that their current abilities basically meet the job requirements, but there are skills or experiences that can be optimized. Among these, S... th2 The second threshold is preset and S th2 =0.8×S th ;

[0028] When S final ≤S th2 Candidates will not be included in the immediate interview recommendation process.

[0029] Furthermore, in S400, regarding the assessment of potential value:

[0030] A potential indicator system is constructed using a skills growth curve quantification score Q, a learning ability quantification score X, and a project complexity quantification score F. Based on this potential indicator system, a potential value score P is generated. score =Q+X+F, press P scoreCandidates are divided into two potential value levels: high potential and medium potential.

[0031] The skill growth curve quantification score Q is obtained by extracting the number N of new skills added by the candidate. skill And the average period T of mastering new skills skill Calculation, i.e. Where k1 is a configurable skill growth weight and k1∈(0,1), the default value is 0.4, and it is adjusted according to the job's technical iteration speed;

[0032] The learning ability score X is obtained through the candidate's learning speed score L. test Performance target achievement period T after job change adapt To quantify, that is Where k2 is a configurable learning ability weight and k2∈(0,1), the default value is 0.3, and it can be adjusted according to the job requirements for learning ability;

[0033] The project complexity quantification F is achieved by analyzing the growth rate R of the team size of candidates participating in the project. team and project budget growth rate R budget Quantification, that is Where k3 is the project complexity weight and k3∈(0,1), the default value is 0.3, and it is adjusted according to the job management attribute;

[0034] When P score ≥P th When candidates are classified as high-potential, it indicates that they have strong learning ability and room for growth, and can make up for the gap between their current abilities and the job requirements in a short period of time.

[0035] When P score <P th At that time, candidates were classified as having medium potential, indicating that the candidates had a certain growth potential, but required a longer period of training and development.

[0036] Furthermore, in S500, the four-quadrant hierarchical matrix includes a first quadrant, a second quadrant, a third quadrant, and a fourth quadrant, wherein:

[0037] The first quadrant includes those with high immediate capabilities and high potential;

[0038] The second quadrant includes those with high immediate capabilities and medium potential;

[0039] The third quadrant includes medium-instantaneous capabilities and high potential;

[0040] The fourth quadrant includes medium immediate capabilities and medium potential.

[0041] Compared with existing technologies, this intelligent talent-job matching and stratification method has the following advantages:

[0042] I. This invention employs bidirectional collaborative filtering to calculate both the candidate-job matching score and the job-candidate matching score. It considers the similarity between candidate abilities and job requirements, the fit between candidate career preferences and job requirements, and the fit between job skill requirements and candidate skill proficiency. Based on the cosine similarity algorithm, feature similarity is calculated. This is combined with factors such as career preference matching and skill proficiency matching to arrive at a weighted comprehensive matching score. This approach comprehensively and accurately assesses the suitability of both parties, avoiding matching biases caused by relying on a single dimension. It significantly improves the accuracy of initial talent-job matching, provides a high-quality candidate pool for subsequent recruitment processes, effectively reduces the time cost of ineffective resume screening for companies, and improves recruitment efficiency.

[0043] Second, this invention constructs a two-dimensional hierarchical system of immediate competence assessment and potential value assessment. In the immediate competence assessment, reasonable thresholds are set based on the final matching score to classify candidates into different levels, clearly defining the degree of matching between candidates and positions. In the potential value assessment, a scientific indicator system is constructed through skill growth curves, learning abilities, and project complexity improvement trends to generate potential value scores, and these scores are used to classify candidates. This allows companies to comprehensively understand candidates from two key perspectives: current competence and future development potential. Furthermore, companies can develop differentiated recruitment strategies based on different levels of candidates, achieving efficient allocation and precise training of human resources, and effectively promoting the construction of the company's talent pipeline and long-term development.

[0044] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0046] Figure 1 Flowchart of the intelligent talent-job matching and stratification method;

[0047] Figure 2 A step-by-step framework diagram for a talent-job intelligent matching and stratification method;

[0048] Figure 3 This is the execution logic diagram for the two-dimensional hierarchical evaluation stage in Example 2. Detailed Implementation

[0049] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0050] Example 1

[0051] This embodiment provides a specific implementation process for a talent-position intelligent matching and stratification method, such as... Figure 1 As shown, this method achieves accurate, dynamic, and multi-dimensional matching and stratification between candidates and positions through five steps: data preprocessing and vector construction, bidirectional collaborative filtering calculation, bidirectional score aggregation and real-time calibration, two-dimensional hierarchical evaluation, and four-quadrant matrix mapping and recruitment strategy generation. It provides scientific and efficient support for enterprise recruitment decisions and is applicable to different types of job recruitment scenarios in enterprises of all sizes, meeting the dual needs of enterprises for talent in terms of both immediate combat capability and growth potential.

[0052] First, the data preprocessing and vector construction stage (S100) is entered. In the talent-job intelligent matching hierarchical process, data preprocessing and vector construction are fundamental steps. Their goal is to transform unstructured, scattered candidate and job data into standardized numerical vectors that are computer-recognizable and computable, providing high-quality data support for subsequent matching calculations. Data collection must cover multi-dimensional information from both the candidate and job categories to ensure data comprehensiveness and relevance, laying the foundation for subsequent feature extraction. Specifically, candidate data collection involves collecting information such as educational background, skills certifications, and work experience. Candidate data is the carrier of key information reflecting their abilities, experience, and preferences. The collected data includes educational background, skills certifications, and work experience. Information on work experience, project achievements, and career preferences can be collected through candidates filling out recruitment platform forms, uploading resumes, or by accessing the company's internal talent pool to obtain historical candidate data. Preliminary verification of the collected data is necessary to remove obviously invalid data and ensure its authenticity and completeness. Job data collection: Job data must accurately reflect the company's requirements and development expectations for the position. The collected content includes job descriptions, skill requirements, educational requirements, work experience requirements, and career development paths. Job data is primarily filled in and published by the company's HR department on recruitment platforms or internal recruitment systems. During the collection process, HR should be guided to follow the instructions... Fill out the form according to the standardized template to avoid deviations in subsequent feature extraction due to vague descriptions or incomplete information. From the collected candidate and job data, feature extraction is performed using natural language processing (NLP) techniques, including word segmentation, keyword extraction, and entity recognition. This transforms textual information into structured features. For word segmentation, continuous natural language text is broken down into independent words or phrases according to semantic logic, facilitating subsequent keyword extraction and entity recognition. The job description text is also segmented to ensure that textual information is effectively broken down into the smallest semantic units. For keyword extraction, the purpose is to select words from the segmented text that reflect core features. Or phrases: The principle is to eliminate stop words with no practical meaning based on indicators such as the frequency of occurrence and semantic importance of words in the text. For entity recognition: Entity recognition is based on keyword extraction, further clarifying the entity type to which the keyword belongs, providing a classification basis for subsequent feature quantification. The extracted keywords are labeled with entity types. Through entity recognition, the feature information of candidates and positions is clearly classified, laying the foundation for subsequent quantification according to preset feature dimensions. After completing feature extraction and entity recognition, the candidate features and position features need to be quantified according to preset feature dimensions, converting different types of entity features into numerical forms, and using different quantification methods for different types of features.For quantitative features, direct assignment or standardization is used. For qualitative features (such as skill mastery level, project achievement level, etc.), grade mapping is used. For missing feature values, mean is used for filling. After quantifying all features, all the quantified feature values ​​of the candidate are arranged in the order of the preset feature dimensions to construct the candidate ability feature vector V. c Similarly, by arranging all the quantitative feature values ​​of the job in the same feature dimension order, a job requirement feature vector V is constructed. j Because the quantization ranges of different feature dimensions differ, directly using them for calculation can lead to features with large numerical ranges having an excessive impact on the matching results. Therefore, it is necessary to adjust V... c With V j Standardize the data to map all feature values ​​to the interval [0, 1].

[0053] Then, the bidirectional collaborative filtering calculation stage (S200) begins. This stage calculates the matching degree from both the candidate's and the job's perspectives, overcoming the limitations of traditional single-perspective matching. Based on the cosine similarity algorithm, it calculates the similarity between the candidate and similar jobs, and between the job and similar candidates, thereby generating a bidirectional matching score to comprehensively assess the suitability of both parties. Specifically, the collaborative filtering of the candidate and the job (generating the first matching score S1) uses the candidate's ability feature vector V... c Using this as the core input, the goal is to analyze the similarity between the candidate and positions in the full job database, identify the top N most suitable positions for the candidate, and then calculate the candidate's first matching score S1, reflecting the degree of fit between the candidate and the job requirements. This is achieved by calculating the candidate's ability feature vector V. c Compared with the feature vector of each job requirement in the job database The cosine similarity (where i represents the serial number of a job in the job database) is calculated by taking the cosine of the angle between the two vectors. The smaller the angle, the closer the cosine value is to 1, indicating greater similarity; conversely, the larger the angle, the closer the cosine value is to 0, indicating less similarity. The formula for calculating cosine similarity is: in, Represents vector V c and The dot product (i.e., the sum of the products of corresponding eigenvalues), ||V c || represents vector V c The modulus (i.e., the square root of the sum of squares of the eigenvalues), Representing vectors The modulus length, in the actual calculation process, calls upon all job requirement feature vectors from the full job database. ( (This represents the total number of positions in the job database), and V is calculated sequentially.c With each The cosine similarity is used to obtain the similarity value between each job and the candidate. All jobs are then sorted from highest to lowest similarity value, and the top N jobs with the highest similarity (N is a preset parameter) are selected. These N jobs are the most similar to the current candidate, and their corresponding requirement feature vectors are... Then the first matching score By averaging the similarity between candidates and the N most similar positions, potential feature biases arising from individual positions are eliminated, providing a more comprehensive reflection of the candidate's suitability in the job market. For collaborative filtering of positions and candidates (generating the second matching score S2): collaborative filtering of positions and candidates uses the job requirement feature vector V... j Using the core input, and symmetrical to the collaborative filtering logic of candidates for positions, this approach aims to identify the top M candidates who best match the position by analyzing the similarity between the position and candidates in the entire talent pool. It then calculates the second-to-fourth similarity score S2 for the position and candidates, reflecting the degree of fit between the position requirements and the candidates' abilities. The selection of similar candidates is also based on the cosine similarity algorithm, calculating the position requirement feature vector V. j With the ability feature vector of each candidate in the talent pool The cosine similarity (where k represents the candidate's index in the talent pool) is measured by the cosine of the angle between the vectors, and the calculation formula is: in Represents vector V j and The dot product, ||V j || represents vector V j The length of the mold, Representing vectors The modulus length, in actual calculation, first calls the ability feature vectors of all candidates in the full talent pool. (This represents the total number of candidates in the talent pool), and V is calculated sequentially. j With each The cosine similarity is used to obtain the similarity value between each candidate and the position. All candidates are sorted from highest to lowest similarity value, and the top M candidates with the highest similarity (M is a preset parameter) are selected. These M candidates are the most similar to the current position, and their corresponding ability feature vectors are... Then the second matching score This formula, by averaging the similarity between the job position and the M most similar candidates, more objectively reflects the job's suitability in the talent market.

[0054] Next, the process moves to the bidirectional score aggregation and real-time calibration stage (S300). This stage involves weighted fusion of S1 and S2 obtained from bidirectional collaborative filtering to arrive at the final matching score S, which comprehensively reflects the suitability of the talent for the job. final Simultaneously, weights are dynamically adjusted based on behavioral feedback signals during the recruitment process to achieve personalized self-optimization of the matching strategy. The core of this is balancing the impact of the two-way matching score to ensure the accuracy and timeliness of the final matching score. Since different types of positions have different matching focuses (e.g., technical positions focus more on the match between candidate skills and job skill requirements, while management positions focus more on the match between candidate work experience and job management requirements), a job classification system needs to be pre-built. Initial weights are assigned to different categories of positions to ensure that the initial weights align with the matching requirements of the job type. The construction of this job classification system needs to consider factors such as the company's business domain and the characteristics of job functions, using a hierarchical classification approach to divide positions. Positions are divided into primary categories based on their core functions, such as technical, product, operations, management, marketing, and functional categories. Under each primary category, secondary categories are further divided according to specific job directions. For example, the technical category can be subdivided into backend development, frontend development, testing, algorithm, and data roles. The job categories are categorized into management positions, which can be further subdivided into department management positions, project management positions, and personnel management positions. For some complex secondary categories, tertiary categories can be further divided (e.g., data positions can be subdivided into data development positions, data analysis positions, and data mining positions), forming a clear and well-defined job classification system. During the construction process, specific classification standards and keyword features are defined for each job category. For example, keyword features for technical positions include "programming languages," "database operations," "system development," and "code writing," while keyword features for management positions include "team management," "project coordination," "budget control," and "personnel assessment." These keyword features will serve as the core basis for identifying the category to which the target job belongs. Based on the matching requirements of different job categories, reasonable initial values ​​are set for W1 and W2, and it must satisfy W1 + W2 = 1 (ensuring the total weight sum is 1 to avoid numerical deviations during weighted calculation). When performing matching calculations for the target job, the required feature vector V is extracted. j The keywords in the keyword list are compared with the keyword features of each job category in the job classification system. Through keyword matching degree calculation (e.g., calculating the overlap rate between the target job keywords and category keywords), one or more categories to which the target job belongs are identified. The initial weight configuration corresponding to that category is then called, assigning initial values ​​to W1 and W2, providing a weight basis for the final matching score calculation. The final matching score S... final The comprehensive matching index is obtained by weighting S1 and S2, and its calculation formula is S. final=W1×S1+W2×S2, where W1 and W2 are dynamic weighting coefficients. This formula balances the impact of the matching scores from the candidate's perspective and the job's perspective on the final result based on the job category requirements, ensuring that the final matching score is more in line with the actual recruitment needs of the job. The final matching score of different types of jobs can more accurately reflect their core matching needs. In order to achieve personalized self-optimization of the matching strategy, it is necessary to collect behavioral feedback signals from HR and candidates in the recruitment process in real time. By analyzing the correlation between the feedback signals and S1 and S2, W1 and W2 are dynamically adjusted so that the weighting strategy continuously approaches the optimal configuration of specific positions in the company.

[0055] Next, we move to the two-dimensional stratified assessment stage (S400). This stage stratifies candidates based on two core dimensions: "current competency fit" and "future development potential." It clarifies the candidate's current fit with the position and also identifies their future growth potential, providing a basis for developing differentiated recruitment strategies. This stage is divided into two sub-processes: immediate competency assessment and potential value assessment. For the immediate competency assessment (generating immediate competency levels), based on the final matching score... final By setting reasonable matching thresholds, candidates are divided into different immediate ability levels, which intuitively reflects the degree to which candidates' current abilities match the job requirements. The matching threshold setting includes the main threshold S. th Second threshold S th2 The main threshold S th The candidate's current abilities fully meet the minimum requirements for the position. In this example, the candidate S is set as a historically recruited candidate. final The average value, the second threshold S th2 S represents the minimum standard at which a candidate's current abilities basically meet the job requirements, but there is room for improvement. th2 =0.8×S th This setting ensures a basic fit between candidates at the mid-level of immediate capability and the job requirements, while also clearly distinguishing them from candidates at the high-level of immediate capability, according to S. final The comparison with the threshold divides candidates into three levels, with the highest immediate capability level: when S... final >S th At this stage, candidates are categorized into this level. The core characteristic of candidates at this level is a high degree of alignment between their current abilities and the job requirements. They can quickly become competent in the job without requiring extensive skill development or experience accumulation. Therefore, they directly enter the interview and hiring process and receive priority in receiving corporate recruitment resources. Mid-level immediate ability: When S... th2 <S final ≤S th At this stage, candidates are categorized into this level. While their current abilities generally meet the job requirements, there is room for improvement in several key areas. These areas need to be addressed through short-term training and mentorship after hiring. This level is not recommended at this time.final ≤S th2 At this stage, candidates are categorized into this level. Candidates at this level currently have a significant gap between their current abilities and the job requirements, and even with short-term training, they are unlikely to quickly become competent. Therefore, they are not included in immediate interview recommendations but are instead added to the company's talent pool for reassessment once their abilities improve or job requirements adjust. Regarding potential value assessment: Potential value assessment quantifies a candidate's learning ability, growth rate, and project experience complexity by constructing a potential indicator system, generating a potential value score (P). score Based on this, the potential value levels are divided to assess candidates' future growth potential and job suitability. The potential indicator system revolves around "candidate growth potential" as its core, selecting three key dimensions: skill growth curve, learning ability, and project complexity. Each dimension is scored using quantifiable sub-indicators to ensure the objectivity and operability of the potential assessment. The weights of the three dimensions (k1, k2, k3) are set according to the job's potential requirements and satisfy k1+k2+k3=1. In this embodiment, the default values ​​are k1=0.4 (skill growth weight), k2=0.3 (learning ability weight), and k3=0.3 (project complexity weight). Companies can adjust these values ​​according to the job type (e.g., k1 can be increased for positions with rapid technological iteration, and k3 can be increased for management positions). The quantified score Q of the skill growth curve is obtained by extracting the number of new skills N added by the candidate. skill And the average period T of mastering new skills skill Calculation, i.e. Learning ability score X, compared to candidate learning speed score L. test Performance target achievement period T after job change adapt To quantify, that is Project complexity quantification F is achieved by analyzing the growth rate of the team size R of candidates participating in projects. team and project budget growth rate R budget Quantification, that is When P score ≥P th When candidates are classified as high-potential, it indicates that they possess strong learning abilities and room for growth, and can quickly bridge the gap between their current skills and the job requirements; when P score <P th At that time, candidates were classified as having medium potential, indicating that the candidates had a certain growth potential, but required a longer period of training and development.

[0056] Finally, the process moves to the four-quadrant stratification and strategy generation stage (S500). The four-quadrant stratification matrix cross-combines the immediate ability assessment results and the potential value assessment results to form four distinct candidate quadrants. Each quadrant corresponds to different talent characteristics, thereby generating targeted recruitment strategy suggestions for enterprises to achieve precise allocation and efficient utilization of talent resources. The four-quadrant stratification matrix uses "immediate ability level" as the horizontal axis (divided into high immediate ability and medium immediate ability) and "potential value level" as the vertical axis (divided into high potential and medium potential). The four quadrants are formed through the intersection of the two. The candidate group in each quadrant has unique ability-potential combination characteristics, specifically defined as follows:

[0057] Quadrant 1: High Immediate Capabilities + High Potential: Candidates in this quadrant possess current capabilities that are highly aligned with the job requirements, enabling them to quickly join the company and create value. They also have strong growth potential, allowing them to adapt to future job upgrades or assume more important responsibilities.

[0058] Second Quadrant: High Immediate Ability + Medium Potential: Candidates in this quadrant have the ability to fully meet the job requirements and can quickly perform their work and provide stable output, but their growth potential is relatively limited. In the future, they will find it difficult to undertake complex tasks or higher-level responsibilities beyond the scope of their current position. They are suitable to play a stable role in their current position for a long time.

[0059] Third Quadrant: Medium Immediate Capabilities + High Potential: Candidates in this quadrant currently meet the job requirements (with a few areas for improvement), but possess extremely strong growth potential. Their ability gaps can be quickly bridged through short-term targeted training (such as job skills training and rapid integration into projects), making them suitable for companies as long-term talent reserves.

[0060] Quadrant 4: Medium Immediate Ability + Medium Potential: Candidates in this quadrant have a certain gap between their current ability and the job requirements, and their growth potential is limited. They need long-term systematic training to reach the core requirements of the job. They are only suitable as alternative candidates when the company has a tight talent demand or when the job has low requirements for ability and potential.

[0061] Recruitment strategy recommendations should be based on the core characteristics of candidates in each quadrant, combined with the company's recruitment goals (such as short-term staffing, long-term talent reserves, and cost control) and job attributes (such as core positions, basic positions, and emerging positions) to ensure the strategy is targeted and feasible.

[0062] In summary, this embodiment, through the complete process of the intelligent talent-job matching and stratification method, achieves a breakthrough in talent-job matching from single-dimensional screening to multi-dimensional precise stratification. Technically, it utilizes natural language processing to transform unstructured data into structured data, employs cosine similarity algorithms for bidirectional matching score calculation, and relies on behavioral feedback signals for dynamic weight optimization, ensuring the accuracy and timeliness of the matching. In terms of application, it clearly defines candidates' current abilities and future potential through dual-dimensional stratified evaluation, and combines four-quadrant moment reduction to generate differentiated recruitment strategies. This solves the core problems of information asymmetry, single matching dimensions, and lack of targeted strategies in recruitment, providing candidates with job recommendations that better match their abilities and potential. It also helps companies further optimize their job classification systems, potential indicator weights, and recruitment strategies, enhancing the method's universality and flexibility.

[0063] Example 2

[0064] like Figure 1 As shown in the figure, this embodiment provides a workflow for the intelligent talent-job matching layering method, which includes the following steps:

[0065] (I) Data Preprocessing and Vector Construction Stage

[0066] Collect candidate data, specifically covering educational background, skills certifications, work experience, project achievements, and career preferences.

[0067] Collect job data, including job descriptions, skills requirements, educational requirements, work experience requirements, and career development paths.

[0068] Natural language processing technology was used to segment the collected candidate data and job data into words.

[0069] Based on the word segmentation results, key features from candidate data and key requirements from job data are extracted;

[0070] Entity identification is performed on the extracted candidate characteristics and job requirements to clarify core information;

[0071] The candidate features are quantified according to the preset feature dimensions to construct the candidate ability feature vector;

[0072] Similarly, the job requirements are quantified according to the preset feature dimensions to construct a job requirement feature vector;

[0073] The constructed candidate ability feature vector and job requirement feature vector are standardized to make them numerical vectors of a unified dimension.

[0074] (II) Two-way Collaborative Filtering Calculation Stage

[0075] The standardized candidate capability feature vector is used as the input data.

[0076] In the full job database, compare the candidate's ability feature vector with the requirement feature vectors of each job position.

[0077] Filter out the top N positions that are most similar to the current candidate;

[0078] Calculate the similarity between the current candidate and each of the top N positions;

[0079] Based on the calculated similarity, the candidate's first match score for the position is determined;

[0080] The standardized job requirement feature vector was used as the input data.

[0081] In the full talent pool, compare the feature vector of the job requirements with the feature vectors of each candidate's abilities.

[0082] Select the top M candidates who are most similar to the current position;

[0083] Calculate the similarity between the current position and each of the top M candidates;

[0084] Based on the calculated similarity, a second matching score is derived for the job position and the candidate.

[0085] (III) Two-way fraction aggregation and real-time calibration stage

[0086] Predefine a job classification system to divide all jobs into multiple different categories;

[0087] For each type of job, a pre-defined initial weight configuration strategy is set, and the initial weight references for the first matching score and the second matching score are clearly defined.

[0088] Analyze the keyword distribution in the feature vector of the current target job requirements;

[0089] Based on keyword distribution, identify one or more categories to which the target job belongs;

[0090] Invoke the initial weight configuration strategy corresponding to this job category and assign initial values ​​to the first matching weight and the second matching weight.

[0091] Based on the initial weights, the first matching score and the second matching score are weighted and calculated to obtain the final matching score;

[0092] During the recruitment process, real-time feedback signals from HR regarding candidates' behavior are collected, including browsing time, rejection actions, and hiring actions.

[0093] Simultaneously, collect behavioral feedback signals from candidates regarding the position, including their response to applying for a job, their refusal to apply for a job, and optimize the weighting of online learning.

[0094] (iv) Two-dimensional hierarchical evaluation stage

[0095] like Figure 3 As shown, based on the final matching score S final Set a matching threshold S th And the second threshold;

[0096] Compare the final matching score of each candidate with the threshold. If the final matching score S final Greater than S th The candidate will be classified into the high immediate capability level, indicating that their current ability matches the job requirements, and they can proceed to the interview and hiring process.

[0097] If S th2 <S final ≤S th If the current ability level is between 0 and 1, it is classified as the intermediate immediate ability level, indicating that the current ability basically meets the job requirements, but there are skills or experience items that can be optimized.

[0098] If S final ≤S th2 This candidate will not be included in the immediate interview recommendation at this time.

[0099] Construct a potential indicator system, which includes quantitative scores for skill growth curves, quantitative scores for learning ability, and quantitative scores for project complexity.

[0100] Extract the average period of the number of new skills acquired by the candidates and the degree of mastery of these new skills, and calculate the quantitative score of the skill growth curve accordingly.

[0101] Obtain candidates' learning speed scores and performance achievement cycles after job changes, and calculate quantitative scores of learning ability based on these data.

[0102] Analyze the growth rate of team size and project budget for candidates' projects to calculate a quantitative score for project complexity.

[0103] The potential value score is obtained by adding the quantitative scores of the skill growth curve, learning ability, and project complexity.

[0104] Set a potential value threshold P th If P score ≥P th Classifying them as high-potential level means that the candidate has strong learning ability and room for growth, and can make up for the gap between their current ability and the job requirements in a short period of time.

[0105] If P score <Pth The candidate is classified as having medium potential, indicating that the candidate has a certain growth potential, but requires a longer period of training and development.

[0106] (V) Four-Quadrant Hierarchy and Recruitment Strategy Generation Stage

[0107] Construct a four-quadrant hierarchical matrix and define the four quadrants as follows: Quadrant 1 (high immediate capability and high potential), Quadrant 2 (high immediate capability and medium potential), Quadrant 3 (medium immediate capability and high potential), and Quadrant 4 (medium immediate capability and medium potential).

[0108] The immediate ability assessment results (high immediate ability / medium immediate ability) of each candidate are combined and matched with the potential value assessment results (high potential / medium potential);

[0109] Based on the matching results, the candidates are assigned to their respective quadrants;

[0110] Starting from the supply and demand matching platform, corresponding recruitment strategy suggestions are generated for each quadrant of the candidate group.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A talent-job intelligent matching and stratification method, characterized in that, The specific steps of this method are as follows: S100. Data Preprocessing and Vector Construction: Collect candidate and job data, and extract candidate features and job requirements using natural language processing techniques to construct candidate ability feature vectors V. c and the feature vector V of job requirements j and V c With V j Standardized into numerical vectors of a uniform dimension; S200, Bidirectional Collaborative Filtering Calculation: Perform collaborative filtering of candidates to positions and collaborative filtering of positions to candidates in parallel, and calculate the first matching score S1 of candidates to positions and the second matching score S2 of positions to candidates. S300, Bidirectional Score Aggregation and Real-time Calibration: Receives S1 and S2, determines the S1 weight W1 and S2 weight W2 based on job characteristics, and calculates the final matching score S. final Simultaneously, behavioral feedback signals from HR and candidates during the recruitment process are collected, and W1 and W2 are dynamically adjusted based on these behavioral feedback signals. S400, Two-Dimensional Hierarchical Evaluation: Based on the aforementioned S final Conduct real-time capability assessments to screen highly matched candidates and generate real-time capability levels. At the same time, conduct potential value assessments based on a potential indicator system to generate potential value levels. S500, Four-Quadrant Layering and Strategy Generation: Maps real-time combat capability assessment results and potential value assessment results to a four-quadrant layering matrix, and generates corresponding recruitment strategy suggestions for each quadrant of candidate groups from the supply and demand matching platform.

2. The talent-job intelligent matching and stratification method according to claim 1, characterized in that, In S100, the candidate data includes the candidate's educational background, skills certification information, work experience information, project achievements information, and career preference information; Job data includes job descriptions, skills requirements, educational requirements, work experience requirements, and career development paths. Natural language processing (NLP) techniques include word segmentation, keyword extraction, and entity recognition. After extracting candidate and job features using NLP techniques, the candidate features are quantified according to preset feature dimensions to obtain the candidate capability vector V. c and the job requirement vector V j .

3. The talent-job intelligent matching and stratification method according to claim 1, characterized in that, In S200, collaborative filtering of candidates for positions: Using the candidate's ability feature vector V c As input, the similarity between the vector and the job requirement vector in the full job database is calculated using the cosine similarity algorithm to generate the first matching score S1, where S1 = in, This represents the feature vector of the current candidate's abilities. Let N represent the requirement vector of the i-th similar job in the job database, and let N represent the number of the top N jobs most similar to the current candidate.

4. The intelligent talent-job matching and stratification method according to claim 1, characterized in that, In S200, collaborative filtering of candidates for job positions: Using the job requirement feature vector as input V j The cosine similarity algorithm is used to calculate the similarity between the candidate's ability vector and the candidate's ability vector in the full talent database, generating a second matching score S2. in, This represents the feature vector representing the requirements of the current job position. Let M represent the ability vector of the k-th similar candidate in the talent pool, and M represent the number of the top M candidates most similar to the current position.

5. The intelligent talent-job matching and stratification method according to claim 1, characterized in that, In S300, a job classification system is predefined, dividing jobs into multiple categories, and an initial weight configuration strategy is preset for each category of jobs. By analyzing the job requirement vector V j The keyword distribution in the data identifies one or more categories to which the target job belongs, and calls the corresponding weight configuration strategy to assign initial values ​​to W1 and W2, and calculates the final matching score through weighted calculation. The final matching score S final =W1×S1+W2×S2, where W1 and W2 are the dynamic weight coefficients of the first matching score S1 and the second matching score S2, respectively, and are dynamically adjusted according to the behavioral feedback signal; The behavioral feedback signals include the HR's browsing time of candidates, rejection and hiring actions, as well as candidates' responses to job applications and rejection actions.

6. The intelligent talent-job matching and stratification method according to claim 1, characterized in that, In S400, for real-time capability assessment: Based on the final matching score S final Set the matching threshold S th Candidates are divided into real-time capability levels based on the matching threshold, and the real-time capability levels include high real-time capability and medium real-time capability. When S final >S th At that time, candidates are classified into the high immediate capability level, indicating that their current capabilities match the job requirements, and they enter the interview and hiring process; When S th2 final ≤S th Candidates are categorized into the immediate ability level, S. th2 The second threshold is preset and S th2 =0.8×S th ;​ When S final ≤S th2 Candidates will not be included in the immediate interview recommendation process.

7. The intelligent talent-job matching and stratification method according to claim 1, characterized in that, In S400, regarding the assessment of potential value: A potential indicator system is constructed using a skills growth curve quantification score Q, a learning ability quantification score X, and a project complexity quantification score F. Based on this potential indicator system, a potential value score P is generated. score =Q+X+F, press P score Candidates are divided into two potential value levels: high potential and medium potential. The skill growth curve quantification score Q is obtained by extracting the number N of new skills added by the candidate. skill And the average period T of mastering new skills skill Calculation, i.e. Where k1 is a configurable skill growth weight and k1∈(0,1), the default value is 0.4, and it is adjusted according to the job's technical iteration speed; The learning ability score X is obtained through the candidate's learning speed score L. test Performance target achievement period T after job change adapt To quantify, that is Where k2 is a configurable learning ability weight and k2∈(0,1), the default value is 0.3, and it can be adjusted according to the job requirements for learning ability; The project complexity quantification F is achieved by analyzing the growth rate R of the team size of candidates participating in the project. team and project budget growth rate R budget Quantification, that is Where k3 is the project complexity weight and k3∈(0,1), the default value is 0.3, and it is adjusted according to the job management attribute; When P score ≥P th At that time, candidates were categorized into a high-potential tier; When P score <P th At that time, candidates were divided into the medium potential level.

8. The intelligent talent-job matching and stratification method according to claim 1, characterized in that, In the S500, the four-quadrant hierarchical matrix includes a first quadrant, a second quadrant, a third quadrant, and a fourth quadrant, wherein: The first quadrant includes those with high immediate capabilities and high potential; The second quadrant includes those with high immediate capabilities and medium potential; The third quadrant includes medium-instantaneous capabilities and high potential; The fourth quadrant includes medium immediate capabilities and medium potential.

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