Intelligent recruitment system

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent recruitment system, which comprises a weight determination module used for determining the real-time dynamic weight of each piece of behavior data of a job seeker based on a time decay function and a real-time feedback mechanism; the verification matching module is used for storing the skill certificate of the job seeker and the enterprise qualification of the enterprise terminal on a block chain, verifying the credibility of the job seeker and the enterprise terminal by adopting an intelligent contract, and outputting a verification result; the resume matching module is used for performing matching based on the word vector model, the resume of the job seeker and the post description of the enterprise terminal to obtain a matching result; the simulation interview module is used for collecting facial expressions, voice reply conditions and task operations in an interview scene of the job seeker and determining a comprehensive ability result of the job seeker; and the processing module is used for realizing the matching of the job seekers and the posts, and providing the best matching effect of the job seekers and the posts through layer-by-layer screening and checking.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent recruitment system. Background Technology

[0002] Existing recruitment systems often suffer from information lag when matching companies and job seekers, leading to inaccurate matching results.

[0003] For example, companies receive a large number of resumes that do not meet the job requirements, while job seekers face numerous mismatched job recommendations, resulting in a serious waste of time and resources for both parties.

[0004] Therefore, improving the matching success rate of recruitment systems between companies and job seekers is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] In view of the above problems, the present invention provides an intelligent recruitment system that overcomes or at least partially solves the above problems.

[0006] This invention provides an intelligent recruitment system, comprising:

[0007] The weight determination module is used to determine the real-time dynamic weights of each behavioral data of job seekers based on the time decay function and the real-time feedback mechanism.

[0008] The verification and matching module stores job seekers' skill certificates and company qualifications on the blockchain, uses smart contracts to verify the credibility of job seekers and companies, and outputs the verification results.

[0009] The resume matching module is used to match the job seeker's resume with the target job requirements of the company based on the word vector model, the job seeker's resume and the job description of the company, and obtain the matching result.

[0010] The mock interview module is used to collect job seekers' facial expressions, voice responses, and task operations in interview scenarios to determine the job seekers' overall abilities.

[0011] The processing module is used to match job seekers with positions based on the real-time dynamic weights, the verification results, the matching results, and the comprehensive ability results.

[0012] Preferably, it further includes:

[0013] The privacy protection module is used to protect the privacy of job seekers' behavioral data.

[0014] Preferably, the privacy protection module is used for:

[0015] Feature data is obtained by extracting features from various behavioral data of job seekers.

[0016] The feature data is processed using a dimensionality reduction algorithm to obtain dimensionality-reduced data;

[0017] Based on the reduced-dimensionality data, an encrypted hash value is generated and uploaded to a cloud server for storage.

[0018] Preferably, the weight determination module is used for:

[0019] Obtain the initial weights for each behavioral data point of job seekers;

[0020] The initial dynamic weights of each action data are determined based on the initial weights and the time decay function.

[0021] The initial dynamic weights are adjusted based on a real-time feedback mechanism to obtain the real-time dynamic weights of each behavioral data.

[0022] Preferably, the weight determination module is specifically used for:

[0023] Based on the initial weights and the time decay function, the initial dynamic weights of each row of data are determined according to the following formula:

[0024] W t =W0·e -λt

[0025] Among them, W t λ is the initial dynamic weight, W0 is the decay coefficient, and the decay coefficient is dynamically adjusted based on the job popularity, which is determined based on the number of views and applications within a preset time period.

[0026] Preferably, the weight determination module is specifically used for:

[0027] When the job's popularity exceeds the first threshold, the attenuation coefficient is increased.

[0028] When the job popularity is less than the second threshold, the attenuation coefficient is adjusted to decrease in order to maintain the stability of the initial dynamic weight.

[0029] Preferably, the weight determination module is specifically used for:

[0030] When the click-through rate of job seekers on job postings pushed by companies falls below the third threshold, the initial dynamic weight is adjusted to decrease.

[0031] Preferably, the resume matching module is used for:

[0032] The word vector model is used to extract keyword vectors from job seekers' resumes and job descriptions from companies.

[0033] Calculate the cosine similarity between keyword vectors to obtain the matching results.

[0034] Preferably, the simulated interview module is used for:

[0035] Collect job seekers' facial expressions, voice responses, and task operations during interviews;

[0036] The job seeker's communication skills are determined by performing voice recognition on the recorded responses.

[0037] The emotional state of job seekers can be determined by performing image recognition on their facial expressions.

[0038] By analyzing the logs of the task operations, the logical consistency of the job seeker's actions can be determined.

[0039] Based on the aforementioned communication skills, emotional state, and operational logic, the job seeker's overall ability is determined.

[0040] Preferably, the processing module is used for:

[0041] The job seeker's attention value is determined based on the aforementioned real-time dynamic weights;

[0042] Based on the aforementioned attention values, candidate job seekers and corresponding candidate positions are selected;

[0043] The candidate verification results, candidate matching results, and candidate comprehensive ability results are determined for each candidate job seeker and corresponding candidate position.

[0044] Based on the candidate verification results, candidate matching results, and candidate comprehensive ability results, target job seekers and corresponding target positions are determined, thereby achieving a match between job seekers and positions.

[0045] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0046] This invention provides an intelligent recruitment system, comprising: a weight determination module, used to determine the real-time dynamic weights of various behavioral data of job seekers based on a time decay function and a real-time feedback mechanism; a verification and matching module, used to store job seekers' skill certificates and enterprise qualifications on the blockchain, use smart contracts to verify the credibility of job seekers and enterprises, and output verification results; a resume matching module, used to match job seekers' resumes with the target job requirements of enterprises based on word vector models, job seekers' resumes, and job descriptions from enterprises, to obtain matching results; a simulated interview module, used to collect job seekers' facial expressions, voice responses, and task operations in interview scenarios to determine the job seekers' comprehensive ability results; and a processing module, used to match job seekers with positions based on real-time dynamic weights, verification results, matching results, and comprehensive ability results, providing the best job seeker-position matching effect through layers of screening and checks. Attached Figure Description

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0048] Figure 1 A schematic diagram of the intelligent recruitment system in an embodiment of the present invention is shown;

[0049] Figure 2 A schematic diagram of the processing flow of the weight determination module in an embodiment of the present invention is shown;

[0050] Figure 3 A schematic diagram of the processing flow of the verification matching module in an embodiment of the present invention is shown;

[0051] Figure 4 A schematic diagram of the processing flow of the resume matching module in an embodiment of the present invention is shown;

[0052] Figure 5 A schematic diagram of the processing flow of the simulated interview module in an embodiment of the present invention is shown. Detailed Implementation

[0053] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0054] Example 1:

[0055] Embodiments of the present invention provide an intelligent recruitment system, such as... Figure 1 As shown, it includes:

[0056] The weight determination module 101 is used to determine the real-time dynamic weights of each behavioral data of job seekers based on the time decay function and the real-time feedback mechanism.

[0057] The verification and matching module 102 is used to store job seekers' skill certificates and enterprise qualifications on the blockchain, use smart contracts to verify the credibility of job seekers and enterprises, and output the verification results.

[0058] The resume matching module 103 is used to match the job seeker's resume with the target job requirements of the company based on the word vector model, the job seeker's resume and the job description of the company, and obtain the matching result.

[0059] The mock interview module 104 is used to collect job seekers' facial expressions, voice responses, and task operations in interview scenarios to determine the job seekers' overall abilities.

[0060] The processing module 105 is used to match job seekers with positions based on real-time dynamic weights, verification results, matching results, and comprehensive ability results.

[0061] The intelligent recruitment system can be used by both companies and job seekers. Companies can register, upload company information, and post job openings. Job seekers can browse or click to view companies and positions of interest based on the information provided by the companies. This invention uses an intelligent recruitment system to evaluate information from both sides and matches suitable companies or positions based on job seeker behavioral data. Through in-depth matching, verification, and evaluation, it finds a reasonable match between job seekers and positions, saving time for both parties and increasing the success rate of matching companies and job seekers.

[0062] The following is a detailed description of each module of the intelligent recruitment system:

[0063] like Figure 2 As shown, the weight determination module 101 is specifically used for:

[0064] Obtain the initial weights for each behavioral data point of job seekers;

[0065] The initial dynamic weights of each action data are determined based on the initial weights and the time decay function.

[0066] The initial dynamic weights are adjusted based on a real-time feedback mechanism to obtain the real-time dynamic weights of each behavioral data.

[0067] Specifically, because job seekers' behavior is time-sensitive, recent browsing behavior reflects their current job intentions more accurately than historical behavior; therefore, this behavioral data should be given higher weight. At the same time, market trends also affect the popularity of job postings; for popular positions, the frequency of comments will have a higher weight. For example, in the internet industry, artificial intelligence-related positions are highly sought after. If job seekers frequently comment on these positions, the system will correspondingly increase the weight of this behavior in the calculation of attention value.

[0068] To determine a job seeker's level of interest in a particular position, we need to analyze their various behavioral data.

[0069] The weight determination module 101 is specifically used for:

[0070] Based on the initial weights and the time decay function, the initial dynamic weights of each row of data are determined according to the following formula:

[0071] W t =W0·e -λt

[0072] Among them, W t λ is the initial dynamic weight, W0 is the decay coefficient, and the decay coefficient is dynamically adjusted based on the popularity of the job posting. The popularity of the job posting is determined based on the number of views and applications within a preset time period.

[0073] The popularity of a job posting is determined by real-time statistics of page views and application volume within a preset time period. Then, based on this popularity, a decay coefficient is adjusted to obtain the initial dynamic weight.

[0074] As time changes, the initial dynamic weight will continue to change. Then, the initial dynamic weight will be adjusted according to the real-time feedback mechanism.

[0075] This weight determination module is specifically used for:

[0076] When the job posting popularity exceeds the first threshold, the attenuation coefficient is increased to accelerate the decay of the job seeker's initial dynamic weight, thereby highlighting the impact of recent job seeker behavior on the attention value.

[0077] When the job popularity is less than the second threshold, the attenuation coefficient is adjusted to decrease in order to maintain the stability of the initial dynamic weight.

[0078] Next, the weight determination module 101 is also specifically used for:

[0079] When the click-through rate of job postings pushed by companies falls below the third threshold, the initial dynamic weight is reduced. Therefore, by continuously adjusting the initial dynamic weight as the market and job seeker behavior change, making its weight more relevant to the current situation, it has a profound impact on determining job seeker attention levels.

[0080] By using the real-time dynamic weights determined by the weight determination module 101, the job seeker's level of interest in a position can be accurately determined. This allows for the selection of job seekers with high interest and their corresponding companies.

[0081] Next, as Figure 3 As shown, the verification and matching module 102 specifically uploads key information such as job seekers' skill certificates and company qualifications to the blockchain, leveraging the blockchain's immutability to ensure the authenticity and integrity of the information. Then, a smart contract automatically verifies the company's risk level and the job seeker's creditworthiness to reduce fraud risk. By setting preset rules for the smart contract, data such as the company's business license and recruitment history are analyzed according to these rules to automatically generate a risk rating. During the job seeker matching process, data such as academic certificates and skill certifications stored on the blockchain are automatically retrieved for authenticity verification.

[0082] Then, the resume matching module 103 matches job seekers' resumes with the target job requirements of companies. This module can filter out job seekers who do not match.

[0083] Specifically, such as Figure 4 As shown, the resume matching module 103 is used for:

[0084] The word vector model is used to extract keyword vectors from job seekers' resumes and job descriptions from companies.

[0085] Calculate the cosine similarity between keyword vectors to obtain the matching results.

[0086] The process employs a word vector model to extract core skills, such as "Python programming" and "project management," from job descriptions provided by employers. It also uses the same model to extract keyword vectors describing skills mastered or proficient in from job applicants' resumes. Finally, semantic matching is performed between the two, specifically calculating cosine similarity. The degree of matching is determined by the angle between the two sets of keywords in the judgment space. Since multiple keyword vectors are identified from both the job description and the resume, the cosine similarity is used to assess the overall similarity.

[0087] Using the resume matching module 103, matching results can be obtained, thereby filtering out those with a high degree of matching to enter the mock interview stage.

[0088] The mock interview module 104 can provide interview scenarios based on virtual settings, such as simulating real-world practical scenarios or real-world communication scenarios.

[0089] In these virtual scenarios, such as Figure 5 As shown, the mock interview module 104 is used for:

[0090] Collect job seekers' facial expressions, voice responses, and task operations during interviews;

[0091] By performing voice recognition on the voice responses, the communication skills of job seekers can be verified.

[0092] By performing image recognition on facial expressions, the emotional state of job seekers can be determined;

[0093] By analyzing the logs of task operations, the logical consistency of the job seeker's actions can be determined.

[0094] Based on communication skills, emotional state, and operational logic, the system determines the applicant's overall ability. For example, the simulated interview module 104 provides a virtual meeting scenario where applicants need to answer preset questions and perform tasks. The system collects the applicant's voice responses, facial expressions, and task operation process. These are then analyzed. For instance, voice responses are analyzed using word frequency and tone of voice to determine fluency, responsiveness, and ultimately, communication skills. Facial expressions are analyzed, such as the magnitude and speed of changes between two frames, to determine the applicant's emotional state, such as tension or confusion. Task operations are logged. By setting up a system log in the virtual scenario, the applicant's task operation process is compared with the standard content recorded in the system log to evaluate the logical consistency of the applicant's tasks, resulting in a judgment of strong or weak logic.

[0095] Finally, the scores are weighted and summed to obtain the overall result of the job seeker's abilities.

[0096] Based on overall ability, the job seekers with the highest matching degree are selected.

[0097] In addition, to ensure the security of job seeker data, this intelligent recruitment system also includes a privacy protection module to protect the privacy of job seeker behavior data. Specific protection measures are as follows:

[0098] The privacy protection module is used for:

[0099] Feature data is obtained by extracting features from various behavioral data of job seekers.

[0100] The feature data is processed using a dimensionality reduction algorithm to obtain dimensionality-reduced data;

[0101] Based on the dimensionality reduction data, a cryptographic hash value is generated and uploaded to a cloud server for storage.

[0102] Specifically, feature extraction is performed on the local device of the job seeker. Then, Principal Component Analysis (PCA) is used to process the job seeker's behavioral data to reduce data dimensionality. Finally, the data is encrypted using cryptographic hash values ​​to improve encryption and transmission efficiency, while also meeting GDPR privacy regulations and ensuring that job seeker data does not leave the local device, effectively protecting the job seeker's data security.

[0103] Finally, the processing module 105 is used for:

[0104] The job seeker's attention value is determined based on real-time dynamic weights;

[0105] Based on this attention value, candidate job seekers and corresponding candidate positions are selected;

[0106] The candidate verification results, candidate matching results, and candidate comprehensive ability results are determined separately for the candidate job seekers and their corresponding candidate positions.

[0107] Based on the candidate verification results, candidate matching results, and candidate comprehensive ability results, target job seekers and corresponding target positions are determined, thereby achieving a match between job seekers and positions.

[0108] Specifically, by calculating attention values, the system recommends highly matched candidate applicants to companies and highly matched job positions to job seekers. Simultaneously, through verification, matching, and competency assessment, the results are fed back to both companies and job seekers. Companies can view detailed information and assessment reports of job seekers, while job seekers can understand the specific requirements of the recommended job positions and their own strengths, ultimately achieving a two-way matching result.

[0109] The intelligent recruitment system provided by this invention can effectively improve the accuracy of recruitment matching, data credibility, and user privacy security, providing more efficient and reliable recruitment services for enterprises and job seekers.

[0110] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0111] This invention provides an intelligent recruitment system, comprising: a weight determination module, used to determine the real-time dynamic weights of various behavioral data of job seekers based on a time decay function and a real-time feedback mechanism; a verification and matching module, used to store job seekers' skill certificates and enterprise qualifications on the blockchain, use smart contracts to verify the credibility of job seekers and enterprises, and output verification results; a resume matching module, used to match job seekers' resumes with the target job requirements of enterprises based on word vector models, job seekers' resumes, and job descriptions from enterprises, to obtain matching results; a simulated interview module, used to collect job seekers' facial expressions, voice responses, and task operations in interview scenarios to determine the job seekers' comprehensive ability results; and a processing module, used to match job seekers with positions based on real-time dynamic weights, verification results, matching results, and comprehensive ability results, providing the best job seeker-position matching effect through layers of screening and checks.

[0112] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0113] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent recruitment system, characterized in that, include: The weight determination module is used to determine the real-time dynamic weights of each behavioral data of job seekers based on the time decay function and the real-time feedback mechanism. The verification and matching module stores job seekers' skill certificates and company qualifications on the blockchain, uses smart contracts to verify the credibility of job seekers and companies, and outputs the verification results. The resume matching module is used to match the job seeker's resume with the target job requirements of the company based on the word vector model, the job seeker's resume and the job description of the company, and obtain the matching result. The mock interview module is used to collect job seekers' facial expressions, voice responses, and task operations in interview scenarios to determine the job seekers' overall abilities. The processing module is used to match job seekers with positions based on the real-time dynamic weights, the verification results, the matching results, and the comprehensive ability results.

2. The intelligent recruitment system as described in claim 1, characterized in that, Also includes: The privacy protection module is used to protect the privacy of job seekers' behavioral data.

3. The intelligent recruitment system as described in claim 2, characterized in that, The privacy protection module is used for: Feature data is obtained by extracting features from various behavioral data of job seekers. The feature data is processed using a dimensionality reduction algorithm to obtain dimensionality-reduced data; Based on the reduced-dimensionality data, an encrypted hash value is generated and uploaded to a cloud server for storage.

4. The intelligent recruitment system as described in claim 1, characterized in that, The weight determination module is used for: Obtain the initial weights for each behavioral data point of job seekers; The initial dynamic weights of each action data are determined based on the initial weights and the time decay function. The initial dynamic weights are adjusted based on a real-time feedback mechanism to obtain the real-time dynamic weights of each behavioral data.

5. The intelligent recruitment system as described in claim 4, characterized in that, The weight determination module is specifically used for: Based on the initial weights and the time decay function, the initial dynamic weights of each row of data are determined according to the following formula: W t =W0·e -λt Among them, W t λ is the initial dynamic weight, W0 is the decay coefficient, and the decay coefficient is dynamically adjusted based on the job popularity, which is determined based on the number of views and applications within a preset time period.

6. The intelligent recruitment system as described in claim 5, characterized in that, The weight determination module is specifically used for: When the job's popularity exceeds the first threshold, the attenuation coefficient is increased. When the job popularity is less than the second threshold, the attenuation coefficient is adjusted to decrease in order to maintain the stability of the initial dynamic weight.

7. The intelligent recruitment system as described in claim 4, characterized in that, The weight determination module is specifically used for: When the click-through rate of job seekers on job postings pushed by companies falls below the third threshold, the initial dynamic weight is adjusted to decrease.

8. The intelligent recruitment system as described in claim 1, characterized in that, The resume matching module is used for: The word vector model is used to extract keyword vectors from job seekers' resumes and job descriptions from companies. Calculate the cosine similarity between keyword vectors to obtain the matching results.

9. The intelligent recruitment system as described in claim 1, characterized in that, The simulated interview module is used for: Collect job seekers' facial expressions, voice responses, and task operations during interviews; The job seeker's communication skills are determined by performing voice recognition on the aforementioned voice responses. The emotional state of job seekers can be determined by performing image recognition on their facial expressions. By analyzing the logs of the task operations, the logical consistency of the job seeker's actions can be determined. Based on the aforementioned communication skills, emotional state, and operational logic, the job seeker's overall ability is determined.

10. The intelligent recruitment system as described in claim 1, characterized in that, The processing module is used for: The job seeker's attention value is determined based on the aforementioned real-time dynamic weights; Based on the aforementioned attention values, candidate job seekers and corresponding candidate positions are selected; The candidate verification results, candidate matching results, and candidate comprehensive ability results are determined for each candidate job seeker and corresponding candidate position. Based on the candidate verification results, candidate matching results, and candidate comprehensive ability results, target job seekers and corresponding target positions are determined, thereby achieving a match between job seekers and positions.

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