Intelligent skill assessment and position matching system

Abstract

The invention discloses an intelligent skill evaluation and position matching system, and aims to improve the position recommendation accuracy and efficiency through a multi-dimensional skill analysis and dynamic optimization algorithm. The system comprises a user information acquisition module, a skill evaluation module, a position demand analysis module, an intelligent matching engine and a feedback optimization module. The user information acquisition module acquires resumes, test scores and skill labels of job seekers; the skill evaluation module comprehensively evaluates the hard skill and the soft skill of the job seeker to generate a comprehensive skill portrait; the position requirement analysis module extracts position requirements; the intelligent matching engine calculates the matching degree between the job seeker and the position by using a similarity algorithm, and provides recommendation; and the feedback optimization module dynamically adjusts a matching algorithm according to interview feedback and work performance, and continuously optimizes a position recommendation effect. The system can provide accurate position matching according to skill portraits and position demand portraits of job seekers, reduces manual intervention, and improves recruitment efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of talent recruitment and job matching systems, and in particular to an intelligent skill assessment and job matching system. Background Art

[0002] With the rapid development of information technology, especially the widespread application of artificial intelligence, machine learning and big data technology, the recruitment industry has ushered in unprecedented changes. Traditional recruitment methods mainly rely on manual screening of resumes and interviews, which is not only inefficient, but also easily affected by the personal preferences, experience and subjective judgment of recruiters, resulting in unsatisfactory recruitment results and high time and labor costs in the recruitment process. In addition, there is often information asymmetry between job seekers and recruiters, and the match between job seekers' skills and job requirements is not easy to quantify, resulting in low accuracy of job recommendations.

[0003] At present, although there are some automated recruitment systems based on resume screening and keyword matching on the market, most of these systems have the following problems:

[0004] Insufficient matching accuracy: Traditional keyword-based matching methods can only simply compare whether the job applicant's resume contains the keywords in the job description, ignoring the multi-dimensional relationship between skills, the depth of skills and actual application scenarios. For example, a job applicant may have mastered a certain skill but did not describe it in detail in his resume. This simplified matching method may miss some excellent talents.

[0005] Insufficient skills assessment: Currently, many recruitment platforms rely only on the job seekers’ self-reported skills or work experience, lacking an objective assessment of the degree of skill mastery. This approach is not only susceptible to the limitations of the job seekers’ personal statements, but also fails to effectively measure the depth of skills and actual operational capabilities.

[0006] Singleness of job matching: Existing job recommendation systems mainly match jobs based on job seekers’ historical work experience or skill keywords, but these methods ignore factors such as job seekers’ career development potential and soft skills. In addition, job requirements often include a combination of hard skills and soft skills, while existing systems rarely consider the impact of soft skills, resulting in insufficient diversity and accuracy of job recommendations.

[0007] Lack of feedback mechanism: Many recruitment systems lack an effective feedback mechanism and cannot optimize the job matching algorithm based on dynamic data such as interview results and post-employment performance. This results in the recruitment system only being able to provide matching suggestions in the initial stage, and the matching effect has not been continuously improved over time.

[0008] In order to overcome the above problems, the industry has proposed some intelligent recruitment solutions based on artificial intelligence. These solutions try to optimize the job matching process and improve the accuracy and efficiency of matching through technologies such as big data analysis, natural language processing (NLP), and deep learning. However, the existing solutions still have the following shortcomings:

[0009] Insufficient in-depth skill analysis: Although many intelligent recruitment systems can match jobs based on resume data, they lack in-depth assessment and multi-dimensional analysis of job seekers’ skills, and are unable to accurately grasp the job seekers’ actual ability level and development potential.

[0010] Lack of soft skills assessment: The soft skills of job seekers (such as communication skills, teamwork skills, etc.) have an important impact on job matching, but existing systems often ignore the evaluation and quantification of soft skills, resulting in incomplete job recommendations.

[0011] Insufficient dynamic optimization: Most existing recruitment systems are based on matching based on static data (such as resumes and job descriptions), and lack dynamic optimization of feedback data (such as interview evaluations, employee performance, etc.) to improve future matching results.

[0012] To this end, we urgently need to design an intelligent skills assessment and job matching system to solve the above problems. Summary of the invention

[0013] The purpose of the present invention is to provide an intelligent skill assessment and job matching system to address the deficiencies of the prior art and to solve the problems raised in the background technology.

[0014] To achieve the above object, the present invention provides the following technical solutions:

[0015] An intelligent skills assessment and job matching system, comprising:

[0016] User information collection module: used to collect job seekers’ basic information, resumes, skill tags, work experience, etc.;

[0017] Skill assessment module: used to automatically assess the job seeker's skill level based on the information provided by the job seeker and generate a skill profile of the job seeker;

[0018] Job requirement analysis module: used to analyze job descriptions, extract the skill requirements for the job, and generate skill profiles for the job;

[0019] Intelligent matching engine: used to match job seekers’ skill profiles with job requirements profiles and recommend the most suitable positions;

[0020] Feedback optimization module: used to optimize the matching algorithm of the intelligent matching engine based on feedback information.

[0021] It should be further explained that the skill assessment module assesses the skills of the job applicant through the following steps:

[0022] Extract the resume text, project experience, online assessment results and other data provided by job seekers, perform semantic analysis, and obtain the skill score matrix S u ;

[0023] The skill score matrix S u Comprehensively evaluate the job seeker's historical performance data to generate a skill profile V u , where V u =[v u1 ,v u2 ,...,v un ] represents the scores of job applicants on n skill dimensions.

[0024] It should be further explained that the job demand analysis module extracts job demand by performing natural language processing on the job description, extracting the skill keywords and experience requirements required for the job, and generating a job demand vector D p , where D p =[d p1 ,d p2 ,...,d pn ] represents the demand intensity of the position in n skill dimensions.

[0025] It should be further explained that the intelligent matching engine calculates the four compatibility factors between job seekers and positions using the following formula:

[0026]

[0027] Among them, M u p is the matching degree between job seeker u and position p, V u To create a skill profile for job seekers, p is the skill requirement of the position, ||V u || and ||D p || is the modulus of the skill vector.

[0028] It should be further explained that the intelligent matching engine further calculates the matching matrix M of all job seekers and all positions through the following algorithm:

[0029]

[0030] Among them, M is an N×M matching matrix, N is the number of job seekers, M is the number of positions, and M up The matching value between each job seeker and the position.

[0031] It should be further explained that the intelligent matching engine uses a sorting algorithm based on the matching matrix M to determine the most suitable job seeker and position pairing, and the sorting algorithm is a sorting algorithm based on matching:

[0032] P u = sort(M u,: )

[0033] Among them, P u is the list of positions that best matches job seeker u, M u,: is the matching degree vector between job seeker u and all positions, and sort means sorting the matching degree vector in descending order.

[0034] It should be further explained that the feedback optimization module optimizes the matching algorithm according to the recruitment feedback through the following steps:

[0035] Collect interview feedback data u,p , where F u,p Score the suitability of job applicant u for position p during the interview process;

[0036] Update the matching matrix M based on the feedback data up , weighted adjustment is performed using the following formula:

[0037]

[0038] Among them, α is the weighted coefficient of matching degree and feedback data, is the updated matching degree.

[0039] It should be further explained that the feedback optimization module further adjusts the matching degree according to the job seeker's performance at work and the position suitability, and generates a long-term optimized matching degree formula:

[0040]

[0041] Among them, G u,p is the actual performance score of job applicant u in position p, and β is the weighting coefficient of the performance data.

[0042] It should be further explained that the skill assessment module is trained by a machine learning model based on historical recruitment data and job seeker feedback data to optimize the assessment model so that the skill assessment result is more accurate. The training method includes an optimization algorithm based on the gradient descent method:

[0043]

[0044] Among them, θ is the model parameter, η is the learning rate, J(θ) is the loss function, is the gradient of the loss function with respect to the model parameters.

[0045] It should be further explained that the intelligent matching engine predicts the long-term career development of job seekers through a deep learning model and provides personalized career development suggestions for job seekers. The prediction model generates career development trends through time series analysis and regression algorithms:

[0046] Y u,t =f(Y u,t-1 ,X u ,θ)

[0047] Among them, Y u,t is the career development status of job seeker u at time t, X u is the historical data of job seekers, θ is the model parameter, and f(·) is the mapping function of the deep learning model.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention generates a comprehensive skill profile by comprehensively evaluating the hard skills, soft skills and career potential of job seekers, thus overcoming the limitation of traditional recruitment systems that rely only on resume keyword matching. The system can accurately evaluate the actual ability of job seekers and deeply match them with the job requirement profile, thereby greatly improving the accuracy of job recommendations. At the same time, the automated matching process greatly saves the time and energy of recruiters and improves recruitment efficiency.

[0050] The present invention introduces a dynamic feedback mechanism to continuously optimize the job matching algorithm through real-time data such as interview feedback and employee performance. As the system accumulates in practical applications, the matching algorithm will be continuously adjusted and optimized, thereby continuously improving the accuracy of recommendations. This dynamic optimization mechanism makes the recruitment process not only rely on static resumes and job descriptions, but also can adjust the recommendation strategy in real time according to the actual performance of job seekers, thereby improving long-term matching results.

[0051] Different from the traditional skill matching method, the present invention not only considers hard skills (such as programming language, professional knowledge, etc.), but also conducts a comprehensive ability assessment of job seekers through soft skills assessment (such as communication skills, teamwork, etc.). Job requirements also include comprehensive requirements of hard skills and soft skills. This multi-dimensional matching method is more in line with the needs of actual work, can effectively improve the quality of recruitment, and avoids the limitation of ignoring soft skills. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a system block diagram of an intelligent skill assessment and job matching system proposed by the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The specific implementation of the present invention is described in detail below in conjunction with the figures and embodiments.

[0055] 1. User information collection module;

[0056] The user information collection module is mainly responsible for collecting job seekers' personal information, resume data, skill tags, online assessment scores, etc. These data provide raw data for subsequent skill assessment and job matching.

[0057] Suppose job seeker Xiao Zhang uploaded his resume and took an online skills test.

[0058] The resume should include the following information:

[0059] Hard skills: Python, SQL, data analysis, machine learning;

[0060] Soft skills: communication, teamwork, leadership;

[0061] Online test scores: Python: 85%, SQL: 80%, Data Analysis: 78%, Machine Learning: 70%;

[0062] The system extracts skill tags from resumes through natural language processing (NLP) technology and generates skill scores based on online assessment results.

[0063] Finally, Xiao Zhang's skill information is as follows:

[0064] Hard skills tags: Python, SQL, data analysis, machine learning;

[0065] Soft skills tags: communication, teamwork, leadership;

[0066] Online test scores (hard skills): Python = 0.85, SQL = 0.80, data analysis = 0.78, machine learning = 0.70

[0067] This information will provide basic data for subsequent skills assessment modules.

[0068] 2. Skills Assessment Module

[0069] The skill assessment module evaluates the hard and soft skills of job seekers based on their resume data and online test scores, and generates a comprehensive skill profile. This process includes hard skill assessment, soft skill assessment, and comprehensive score calculation.

[0070] Hard skills assessment: Hard skills assessment generates a skill profile based on the job seeker's online assessment results. Suppose Xiao Zhang's hard skills assessment results are as follows:

[0071] V hard =[0.85,0.80,0.78,0.70]

[0072] Among them: 0.85 represents the skill score of Python; 0.80 represents the skill score of SQL; 0.78 represents the skill score of data analysis; 0.70 represents the skill score of machine learning.

[0073] Soft skills assessment: Soft skills assessment generates soft skills scores by analyzing data such as job seekers' project experience, interview performance, and self-assessment questionnaires. Suppose Xiao Zhang's soft skills assessment score is as follows:

[0074] V soft =[0.82,0.76,0.78]

[0075] Among them: 0.82 represents the score of communication ability; 0.76 represents the score of teamwork ability; 0.78 represents the score of leadership.

[0076] Comprehensive score: The comprehensive score is the weighted average of hard skills and soft skills. The specific calculation formula is:

[0077] V u =α·V hard +(1-α)·V soft

[0078] Among them, α is the weight of hard skills. Assuming α = 0.7, Xiao Zhang’s comprehensive skill profile is: V u =0.7 · [0.85, 0.80, 0.78, 0.70] + 0.3 · [0.82, 0.76, 0.78] = [0.845, 0.784, 0.785, 0.70]

[0079] 3. Job requirement analysis module: The job requirement analysis module extracts the required skills based on the job description and generates a skill requirement profile for the job.

[0080] Job requirements include hard skill requirements and soft skill requirements.

[0081] Hard skill requirements: Suppose a job description is: "Requires proficiency in Python, SQL, and the ability to perform data analysis and machine learning." Extract the hard skill requirements from the job description:

[0082] D p,hard =[0.9,0.85,0.8,0.75]

[0083] Among them: 0.9 represents the demand intensity for Python skills 0.85 represents the demand intensity for SQL skills 0.8 represents the demand intensity for data analysis skills 0.75 represents the demand intensity for machine learning skills

[0084] Soft skills requirements: Assuming that the position also requires strong communication and teamwork skills, the soft skills requirements are as follows:

[0085] D p,soft =[0.85,0.80]

[0086] Among them: 0.85 represents the demand intensity for communication skills; 0.80 represents the demand intensity for teamwork skills; finally, the skill requirement profile of the position for:

[0087] D p =[0.9,0.85,0.8,0.75,0.85,0.80]

[0088] 4. Intelligent matching engine: The intelligent matching engine recommends the most suitable position through similarity calculation based on the skill profile of the job seeker and the skill requirement profile of the position. The commonly used similarity calculation method is cosine similarity, and its calculation formula is:

[0089]

[0090] Where: V u Is the job seeker's skill profile D p Is the skill requirement profile of the position||V u || and ||D p || They are the models of job seeker skill profile and job skill requirement profile.

[0091] Assume that Xiao Zhang’s comprehensive skill profile is:

[0092] V u =[0.845,0.784,0.785,0.70,0.82,0.76]

[0093] The skill profile of the position is:

[0094] D p =[0.9,0.85,0.8,0.75,0.85,0.80]

[0095] Compute dot product: V u *D p=0.845×0.9+0.784×0.85+0.785×0.8+0.70×0.75+0.82×0.85+0.76×0.80=0.7605+0.6664+0.628+0.525+0.697+0.608=3.8849

[0096] Calculation module:

[0097]

[0098] Calculate the matching degree:

[0099]

[0100] Matching degree M up ≈0.9996, indicating that the job seeker Xiao Zhang is highly matched with the position, and the system will recommend the position to Xiao Zhang.

[0101] 5. Feedback Optimization Module: The feedback optimization module optimizes the matching algorithm based on dynamic data such as interview feedback and work performance.

[0102] Assume that Xiao Zhang participated in the interview and the interview score was F u,p =0.88, the feedback optimization module adjusts the matching degree according to the following formula:

[0103]

[0104] Among them, α is the weight of the historical matching degree. Assuming α = 0.7, the updated matching degree is:

[0105]

[0106] Updated matching The system believes that Xiao Zhang’s match with the position has increased, and the recommended position is more accurate.

[0107] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent skills assessment and job matching system, characterized in that: include: User information collection module: used to collect job seekers’ basic information, resumes, skill tags, and work experience; Skill assessment module: used to automatically assess the job seeker's skill level based on the information provided by the job seeker and generate a skill profile of the job seeker; Job requirement analysis module: used to analyze job descriptions, extract the skill requirements for the job, and generate skill profiles for the job; Intelligent matching engine: used to match job seekers’ skill profiles with job requirements profiles and recommend the most suitable positions; Feedback optimization module: used to optimize the matching algorithm of the intelligent matching engine based on feedback information.

2. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The skills assessment module assesses the skills of job applicants through the following steps: Extract the resume text, project experience, and online assessment results data provided by job seekers, perform semantic analysis, and obtain the skill score matrix S u ; The skill score matrix S u Comprehensively evaluate the job seeker's historical performance data to generate a skill profile V u , where V u =[v u1 ,v u2 ,...,v un ] represents the scores of job applicants on n skill dimensions.

3. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The job demand analysis module extracts job demand by performing natural language processing on the job description, extracting the skill keywords and experience requirements required for the job, and generating a job demand vector D p , where D p =[d p1 ,d p2 ,...,d pn ] represents the demand intensity of the position in n skill dimensions.

4. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The intelligent matching engine calculates the four compatibility factors between job seekers and positions using the following formula: Among them, M is the matching degree between job seeker u and position p, V u To create a skill profile for job seekers, p is the skill requirement of the position, ∥V u ∥ and ∥D p ∥ is the modulus of the skill vector.

5. The intelligent skill assessment and job matching system according to claim 4, characterized in that: The intelligent matching engine further calculates the matching matrix M of all job seekers and all positions through the following algorithm: Among them, M is an N×M matching matrix, N is the number of job seekers, M is the number of positions, and M up The matching value between each job seeker and the position.

6. The intelligent skill assessment and job matching system according to claim 5, characterized in that: The intelligent matching engine uses a sorting algorithm based on the matching matrix M to determine the most suitable job seeker and position pairing. The sorting algorithm is a sorting algorithm based on matching: P u =sort(M u,: ) Among them, P u is the list of positions that best matches job seeker u, M u,: is the matching degree vector between job seeker u and all positions, and sort means sorting the matching degree vector in descending order.

7. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The feedback optimization module optimizes the matching algorithm according to the recruitment feedback through the following steps: Collect interview feedback data u,p , where F u,p Score the suitability of job applicant u for position p during the interview process; Update the matching matrix M based on the feedback data up , weighted adjustment is performed using the following formula: Among them, α is the weighted coefficient of matching degree and feedback data, is the updated matching degree.

8. The intelligent skill assessment and job matching system according to claim 7, characterized in that: The feedback optimization module further adjusts the matching degree according to the job seeker's performance at work and the position suitability, and generates a long-term optimized matching degree formula: Among them, G u,p is the actual performance score of job applicant u in position p, and β is the weighting coefficient of the performance data.

9. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The skill assessment module is trained by a machine learning model based on historical recruitment data and job seeker feedback data to optimize the assessment model so that the skill assessment result is more accurate. The training method includes an optimization algorithm based on the gradient descent method: Among them, θ is the model parameter, η is the learning rate, J(θ) is the loss function, is the gradient of the loss function with respect to the model parameters.

10. The intelligent skill assessment and job matching system according to claim 1, characterized in that: The intelligent matching engine predicts the long-term career development of job seekers through a deep learning model and provides personalized career development suggestions for job seekers. The prediction model generates career development trends through time series analysis and regression algorithms: Y u,t =f(Y u,t-1 ,X u ,θ) Among them, Y u,t is the career development status of job seeker u at time t, X u is the historical data of job seekers, θ is the model parameter, and f(·) is the mapping function of the deep learning model.

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