Multi-channel data fusion talent pushing system and method

Abstract

The invention discloses a talent pushing system and method based on multi-channel data fusion, and relates to the technical field of pushing systems.A big data analysis module extracts key features and generates a high-quality matching model by processing and analyzing mass data from different sources; therefore, the recommendation accuracy and the recruitment efficiency of the enterprise are remarkably improved. Based on the analysis results, the intelligent recommendation module can flexibly cope with personalized requirements of different enterprises and accurately match candidates and posts, so that the recommendation success rate is improved, and meanwhile, the time and energy of the enterprises for screening improper candidates are reduced. Besides, through a real-time monitoring and feedback mechanism, the system can continuously optimize a recommendation algorithm and sensitively respond to market changes, the recommendation accuracy and effectiveness are further improved, and enterprises are helped to take the earlier opportunity in the talent market with intense competition.

Description

Technical Field

[0001] The present invention belongs to the field of talent push systems, and in particular, relates to a talent push system and method for multi-channel data fusion. Background Art

[0002] In the current complex and competitive recruitment market, companies are faced with the huge challenge of selecting the most suitable talents from a large number of job seekers. With the continuous expansion of the talent market, the surge in the number of job seekers and the diversification of job requirements, traditional recruitment methods are gradually unable to meet the needs of companies. Traditional recruitment methods usually rely on a single data source, such as a resume library or an online recruitment platform. This single source of information often lacks comprehensiveness and depth, and cannot fully capture the multi-dimensional characteristics and potential capabilities of job seekers. In addition, the manual screening process of traditional methods is inefficient, which can easily lead to a longer recruitment cycle and even miss excellent candidates.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a talent push system and method with multi-channel data fusion, which solves the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0006] A talent push system with multi-channel data fusion, comprising: a data collection module, the data collection module is used to collect data from multiple channels and fuse the obtained data;

[0007] Data preprocessing module: The data preprocessing module is used to clean, convert and standardize the collected multi-channel data;

[0008] Data storage module: the data storage module uses a combination of structured and unstructured databases to store preprocessed data and can store and retrieve large-scale data;

[0009] Big data analysis module: The big data analysis module is used to perform semantic analysis and sentiment analysis on job descriptions and job seekers' resumes to generate job requirement models and candidate portraits;

[0010] SSM framework management module: The SSM framework management module is used to manage and maintain user information, recruitment information, resume information, and job information in the system;

[0011] Machine learning module, which is used to train models and optimize them through iterative techniques;

[0012] Intelligent recommendation module: The intelligent recommendation module uses the results of big data analysis and machine learning to recommend the most suitable candidates to enterprises, and can provide personalized recommendations and dynamic adjustments;

[0013] Real-time monitoring and feedback module, the real-time monitoring and feedback module is used to monitor corporate recruitment needs and market changes, and adjust recommendation models and strategies based on corporate feedback.

[0014] Optionally, when generating the job requirement model and candidate portrait, the big data analysis module first obtains the job description data and the job seeker resume data from the data storage module and performs semantic analysis on the text content of the job description and the job seeker resume to extract the corresponding key information. After extracting the key information, the formula S = σ(W s ·T+b s ) performs sentiment classification on the text. Then, based on the semantic analysis and sentiment analysis results of the job description, a job requirement model is constructed. Finally, based on the semantic analysis and sentiment analysis results of the job seeker's resume, a candidate portrait is generated, where S is the sentiment score, ranging from 0 to 1, σ is the Sigmoid activation function, and W s is the weight matrix of the sentiment analysis model, T is the text vector after semantic analysis, and b s is the bias term of the sentiment analysis model.

[0015] Optionally, in the process of managing and maintaining user information, recruitment information, resume information, and position information in the system, first, analyze the relationship between user information, recruitment information, resume information, and position information, and then design a corresponding database table structure for each entity. The database table structure includes a user information table, a recruitment information table, a resume information table, and a position information table.

[0016] Optionally, when training and optimizing the talent matching algorithm, the machine learning module first extracts a data set for training the model from the data storage module, the data set including the characteristics of the recruitment position and the characteristics of the job seeker, then extracts features from the job description and the job seeker resume, and divides the data set into a training set and a validation set, establishes and trains a logistic regression model, and initializes the weight parameters of the logistic regression model.

[0017] Optionally, when building and training a logistic regression model, first initialize the weight parameters of the logistic regression model, input the processed data into the logistic regression model and adjust the weight parameters through gradient descent. Through multiple rounds of iterative training, the logistic regression model continuously adjusts the weights and bias terms to minimize the loss function, and uses the validation set data to evaluate the accuracy, precision, recall, and F1 score of the logistic regression model. Finally, deploy the trained logistic regression model to the usage environment. The model will output the matching probability in real time based on the input job description and job seeker information.

[0018] When the intelligent recommendation module recommends the most suitable candidate to the enterprise, it first calculates the matching score between the candidate portrait established by the big data analysis module and the job demand model, and then calculates the matching score based on the enterprise's historical preference data and current job demand in the data storage module through the formula M′=α·M+β·H e The matching score is adjusted individually, and the most suitable candidate is recommended to the enterprise based on the matching score after the individual adjustment, where M′ is the matching score after the individual adjustment, M is the initial matching score, and H e is the historical preference score of the enterprise, which is calculated based on the enterprise's past selection history of candidates. α and β are the weights that adjust the impact of the matching score and the historical preference score, and α+β=1.

[0019] Optionally, after recommending a candidate, the company will provide feedback on the recommendation result through the formula Dynamically adjust the parameters of the recommendation model to improve the accuracy of subsequent recommendations. After the model parameters are adjusted, recalculate the matching score and generate a new candidate recommendation list, where Δθ is the adjustment value of the model parameter θ, η is the learning rate, which controls the step size of the parameter adjustment, and L is the loss function, which is defined according to enterprise feedback and represents the difference between the recommendation results and the actual needs of the enterprise.

[0020] Optionally, continuously monitor the progress of the company's recruitment needs and the overall changes in the recruitment market. Whenever the company releases new recruitment information or modifies existing information, the system automatically records these changes, regularly obtains data from multiple recruitment platforms, industry reports and market analysis tools, and updates the market status. When the company receives the recommended candidates, it will provide feedback through the system. Whenever the company provides feedback, the system updates the loss function based on the feedback and adjusts the model parameters through the gradient descent method. After receiving the feedback, the system uses the formula S new =S old +ΔS(M(t),F e ) recalculates the recommended strategy to ensure that the new strategy reflects the current market conditions and enterprise needs. new represents the adjusted recommended strategy, S old represents the current recommended strategy, ΔS(M(t), F e ) is based on market changes M(t) and enterprise feedback F e The resulting strategy adjustment amount.

[0021] Optionally, after the model and strategy are adjusted, first, by formula Verify whether the adjusted strategy can effectively improve the recommendation accuracy. Then, use the formula Verify whether the adjusted model can effectively improve the recommendation accuracy, where V represents the new strategy S newThe validation accuracy of i is the i-th test sample;

[0022] After completing the adjustment and verification of the model and strategy, the new model and strategy are deployed to actual applications and based on the new recommendation strategy, the candidate recommendation list is recalculated and generated and pushed to the company for the next round of recruitment.

[0023] A talent push method based on multi-channel data integration includes the following steps:

[0024] Collect data from multiple channels and fuse the obtained data;

[0025] Clean, convert and standardize the collected multi-channel data;

[0026] It uses a combination of structured and unstructured databases to store preprocessed data and can store and retrieve large-scale data;

[0027] Perform semantic and sentiment analysis on job descriptions and job seekers’ resumes to generate job requirement models and candidate portraits;

[0028] Manage and maintain user information, recruitment information, resume information, and job information in the system;

[0029] Train the model and optimize it through iterative techniques;

[0030] Using the results of big data analysis and machine learning, we recommend the most suitable candidates to companies, and can make personalized recommendations and dynamic adjustments;

[0031] Monitor corporate recruitment needs and market changes, and adjust recommendation models and strategies based on corporate feedback.

[0032] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0033] The big data analysis module of the present invention processes and analyzes massive data from different sources, extracts key features and generates high-quality matching models, thereby significantly improving the accuracy of recommendations and the recruitment efficiency of enterprises. Based on these analysis results, the intelligent recommendation module can flexibly respond to the personalized needs of different enterprises, accurately match candidates with positions, thereby improving the success rate of recommendations, and reducing the time and energy of enterprises in screening unsuitable candidates. In addition, through real-time monitoring and feedback mechanisms, the system can continuously optimize the recommendation algorithm, respond sensitively to market changes, further improve the accuracy and effectiveness of recommendations, and help enterprises gain an advantage in the fiercely competitive talent market.

[0034] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0036] Figure 1 A block diagram of a talent push system with multi-channel data fusion according to an embodiment of the present invention;

[0037] Figure 2 The present invention is a flowchart of a talent push method based on multi-channel data fusion according to an embodiment of the present invention.

[0038] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0039] The present invention will now be described in further detail with reference to the accompanying drawings.

[0040] See also Figure 1 As shown, in this embodiment, a talent push system with multi-channel data fusion is provided, including a data collection module, which is used to collect data from multiple channels such as recruitment market data, job seeker data, social network data, historical recruitment data, etc., and fuse the obtained data;

[0041] Data preprocessing module: The data preprocessing module is used to clean, convert and standardize the collected multi-channel data to ensure data consistency;

[0042] Data storage module: the data storage module uses a combination of structured and unstructured databases to store preprocessed data and can store and retrieve large-scale data;

[0043] Big data analysis module: The big data analysis module is used to perform semantic analysis and sentiment analysis on job descriptions and job seekers' resumes to generate job requirement models and candidate portraits;

[0044] Through semantic and sentiment analysis of job descriptions and job seekers' resumes, the big data analysis module can deeply understand the semantic and sentimental connotations of text data. This in-depth analysis enables the system to build accurate job demand models and candidate portraits, thereby improving the accuracy of matching. The ability to analyze and process large-scale data also enables the system to discover patterns and trends hidden in large amounts of data, thereby providing high-quality recommendation results in complex recruitment scenarios.

[0045] SSM framework management module: The SSM framework management module is used to manage and maintain user information, recruitment information, resume information, and job information in the system;

[0046] Machine learning module, which is used to train models and optimize them through iterative techniques;

[0047] Intelligent recommendation module: The intelligent recommendation module uses the results of big data analysis and machine learning to recommend the most suitable candidates to enterprises, and can make personalized recommendations and dynamic adjustments; the intelligent recommendation module uses the results of big data analysis and machine learning models to make personalized candidate recommendations based on the specific needs of enterprises. It not only considers the recruitment preferences of enterprises when making initial recommendations, but also makes dynamic adjustments based on real-time changing enterprise needs and market conditions, ensuring that the recommendation results are always highly consistent with current recruitment needs.

[0048] Real-time monitoring and feedback module, which is used to monitor the recruitment needs of enterprises and market changes, and adjust the recommendation model and strategy based on the enterprise feedback. The real-time monitoring and feedback module continuously tracks the changes in the recruitment needs of enterprises and market dynamics, and collects the feedback information of enterprises on the recommendation results. This enables the system to adjust the recommendation model and strategy in a timely manner to adapt to the changing external environment and the specific requirements of enterprises, and ensure that the recommended candidates always meet the latest recruitment standards.

[0049] In this embodiment, when the big data analysis module generates the job requirement model and the candidate portrait, it first obtains the job description data and the job seeker resume data from the data storage module and performs semantic analysis on the text content of the job description and the job seeker resume to extract the corresponding key information. After the key information is extracted, the formula S=σ(W s ·T+b s ) performs sentiment classification on the text. Then, based on the semantic analysis and sentiment analysis results of the job description, a job requirement model is constructed. Finally, based on the semantic analysis and sentiment analysis results of the job seeker's resume, a candidate portrait is generated, where S is the sentiment score, ranging from 0 to 1, σ is the Sigmoid activation function, and W s is the weight matrix of the sentiment analysis model, T is the text vector after semantic analysis, and b sis the bias term of the sentiment analysis model. Key information includes key skills, work experience, and educational requirements. The big data analysis module provides enterprises with an accurate talent recommendation system by processing and analyzing massive data from different sources. First, by performing semantic analysis and sentiment analysis on job descriptions and job seekers' resumes, the system can deeply understand the key information in the text, such as key skills, work experience, and educational requirements. This in-depth text analysis ensures that the system can accurately build job demand models and candidate portraits, thereby improving the accuracy of recommendations. Unlike traditional simple algorithms based on keyword matching, the big data analysis module can capture more complex semantic relationships and ensure a high degree of match between job descriptions and candidate characteristics. In addition, by taking information from multiple data sources, the system can comprehensively consider multi-dimensional candidate characteristics and avoid information bias caused by a single data source. This data fusion method enables the system to provide more comprehensive and diverse recommendation results, ensuring that enterprises can find the most suitable candidates in a variety of recruitment scenarios.

[0050] Specifically, the big data analysis module not only improves the accuracy of recommendations, but also enhances the adaptability and flexibility of the system in handling complex recruitment tasks by integrating multi-channel data, bringing significant efficiency improvements to the company's recruitment process.

[0051] In the process of managing and maintaining user information, recruitment information, resume information, and job information in the system of this embodiment, first, the relationship between user information, recruitment information, resume information, and job information is analyzed. For example, a user may have multiple resumes, one recruitment information corresponds to multiple jobs, and one job may be associated with the resumes of multiple users, etc. Then, a corresponding database table structure is designed for each entity. The database table structure includes a user information table, a recruitment information table, a resume information table, and a job information table. The user information table is used to store basic information of the user, such as user name, password, contact information, role, etc. The recruitment information table is used to store detailed information of the recruitment position, such as job title, company name, work location, salary range, etc. The resume information table is used to store resume information uploaded by the user, such as educational background, work experience, skills, etc. The job information table is used to store specific information of the job, such as job title, job description, required skills, etc. The SSM framework management module ensures efficient data processing and stable operation of the system by effectively managing and maintaining user information, recruitment information, resume information, and job information in the system. First, in the process of managing these information, the module can analyze the complex relationship between different entities such as users, jobs, and resumes. For example, a user may have multiple resumes, a recruitment information may correspond to multiple positions, and a position may be associated with the resumes of multiple job seekers. By deeply analyzing these relationships, the SSM framework management module can design optimized data table structures, such as user information tables, recruitment information tables, resume information tables, and position information tables. This structured data management method not only ensures the relevance and consistency between information, but also reduces data redundancy and errors, improves data reliability and overall system performance. The application of the SSM framework also provides a guarantee for the scalability of the system, allowing the system to maintain efficient responsiveness when the amount of data increases or demand changes.

[0052] In this embodiment, when training and optimizing the talent matching algorithm, the machine learning module first extracts a data set for training the model from the data storage module, the data set includes the characteristics of the job position (such as job description, skill requirements) and the characteristics of the job seeker (such as resume content, work experience, skills, etc.), then extracts features from the job description and the job seeker resume, and divides the data set into a training set and a validation set, establishes and trains a logistic regression model, and initializes the weight parameters of the logistic regression model, and the features include skill matching, years of work experience, education level, keyword matching, etc. When establishing and training the logistic regression model, first, initialize the weight parameters of the logistic regression model, input the processed data into the logistic regression model, adjust the weight parameters through gradient descent, and train through multiple rounds of iterative training, the logistic regression model continuously adjusts the weights and bias terms to minimize the loss function, and uses the validation set data to evaluate the accuracy, precision, recall rate, and F1 score of the logistic regression model, and finally, deploys the trained logistic regression model to the use environment. The model will output the matching probability in real time according to the input job description and job seeker information. The machine learning module achieves an accurate match between the job position and the job seeker characteristics through the training and optimization of the logistic regression model. By continuously adjusting the model parameters, the system can iteratively optimize the matching algorithm and improve the accuracy and reliability of subsequent recommendations. Secondly, the introduction of the machine learning module enables the system to automatically learn from the data and optimize the matching algorithm, greatly improving the accuracy of the recommendation results and the success rate of corporate recruitment.

[0053] When the intelligent recommendation module recommends the most suitable candidate to the enterprise, it first calculates the matching score between the candidate portrait established by the big data analysis module and the job demand model, and then calculates the matching score based on the enterprise's historical preference data and current job demand in the data storage module through the formula M′=α·M+β·H e The matching score is adjusted individually, and the most suitable candidate is recommended to the enterprise based on the matching score after the individual adjustment, where M′ is the matching score after the individual adjustment, M is the initial matching score, and H eis the historical preference score of the enterprise, which is calculated based on the enterprise's past selection history of candidates. α and β are the weights for adjusting the matching score and the historical preference score, and α+β=1. The intelligent recommendation module is the key bridge connecting data analysis and practical application in the system. It realizes a highly personalized and dynamically adjusted candidate recommendation function by combining the big data analysis results and the output of the machine learning model. First, when making recommendations, the intelligent recommendation module calculates the matching score between the candidate portrait generated by the big data analysis module and the job demand model. This matching score is based on complex feature extraction and semantic analysis, and can accurately reflect the degree of fit between job requirements and candidate capabilities. In addition, the intelligent recommendation module can also make personalized adjustments based on the company's historical preference data. This adjustment is based on the company's past recruitment records, analyzes the characteristics of candidates that have been successfully matched in history, and incorporates them into the current recommendation calculation to ensure that the recommendation results are more in line with the company's specific preferences and needs. This personalized recommendation greatly improves the success rate of candidate matching, while reducing the time and energy spent by the company on screening unsuitable candidates. More importantly, the intelligent recommendation module has the ability to dynamically adjust, and can update the recommendation strategy in a timely manner according to the real-time changing corporate needs and market dynamics, ensuring that the recommendation results always maintain the best accuracy and timeliness.

[0054] In this embodiment, after recommending candidates, the enterprise will provide feedback on the recommendation results through the formula Dynamically adjust the parameters of the recommendation model to improve the accuracy of subsequent recommendations. After the model parameters are adjusted, recalculate the matching score and generate a new candidate recommendation list, where Δθ is the adjustment value of the model parameter θ, η is the learning rate, which controls the step size of the parameter adjustment, and L is the loss function, which is defined according to enterprise feedback and represents the difference between the recommendation results and the actual needs of the enterprise.

[0055] In this embodiment, the dynamic adjustment step is as follows: collecting feedback information from enterprises on recommended candidates (such as recruitment success or failure, enterprise satisfaction, etc.), calculating the loss function L based on the feedback information, using the gradient descent method to update the model parameters θ to reduce the loss L, and after iterative optimization, updating the recommendation model to improve the accuracy of the next round of recommendations.

[0056] In this embodiment, the recruitment demand process of the enterprise and the overall changes of the recruitment market are continuously monitored. The overall changes of the recruitment market include dynamic changes in the supply and demand relationship, industry trends, talent mobility, etc. Whenever an enterprise releases new recruitment information or modifies existing information, the system automatically records these changes, regularly obtains data from multiple recruitment platforms, industry reports and market analysis tools, and updates the market status. When an enterprise receives a recommended candidate, it will provide feedback through the system. Whenever an enterprise provides feedback, the system updates the loss function according to the feedback and adjusts the model parameters through the gradient descent method. After receiving the feedback, the system uses the formula S new =S old +ΔS(M(t),F e ) recalculates the recommended strategy to ensure that the new strategy reflects the current market conditions and enterprise needs. new represents the adjusted recommended strategy, S old represents the current recommended strategy, ΔS(M(t), F e ) is based on market changes M(t) and enterprise feedback F e By real-time monitoring of corporate recruitment needs and market changes, and continuously updating data and strategies, we ensure that the recommendation model can respond to market dynamics in a timely manner and improve the accuracy and effectiveness of recommendations.

[0057] In this embodiment, after the model and strategy are adjusted, first, through the formula Verify whether the adjusted strategy can effectively improve the recommendation accuracy. Then, use the formula Verify whether the adjusted model can effectively improve the recommendation accuracy, where V represents the new strategy S new The validation accuracy of i is the i-th test sample;

[0058] After completing the adjustment and verification of the model and strategy, the new model and strategy are deployed to the actual application and based on the new recommendation strategy, the candidate recommendation list is recalculated and generated and pushed to the enterprise for the next round of recruitment. The real-time monitoring and feedback module plays a key role in regulation and optimization in the system. It ensures that the system can always provide recommendation results that meet current needs by continuously tracking changes in the recruitment needs of enterprises and market dynamics. First, the real-time monitoring module can monitor new positions posted by enterprises and updates to existing positions, record these changes and incorporate them into the recommendation algorithm of the system. At the same time, the module will regularly obtain data from multiple recruitment platforms, industry reports and market analysis tools to update market status information, including dynamic changes in supply and demand, industry trends, talent mobility, etc. Through the dynamic update of these data, the system can adjust the parameters of the recommendation model in time to ensure that the model's predictive ability is consistent with the actual market situation. Secondly, after the enterprise receives the recommended candidates, the feedback module collects and analyzes the feedback information of the enterprise to provide optimization basis for the model. The system will recalculate the loss function based on this feedback information, and adjust the model parameters through the gradient descent method to ensure the continuous optimization of the recommendation algorithm. When the system receives feedback, the real-time monitoring and feedback module will recalculate the recommendation strategy, verify whether the adjusted strategy and model can effectively improve the recommendation accuracy, and deploy the new strategy into actual applications.

[0059] Figure 2 Flow chart of a talent push method based on multi-channel data fusion according to an embodiment of the present invention. Method 200 includes:

[0060] Step 201, collecting data from multiple channels and fusing the obtained data;

[0061] Step 202, cleaning, converting and standardizing the collected multi-channel data;

[0062] Step 203, using a combination of structured and unstructured databases to store the preprocessed data, and capable of storing and retrieving large-scale data;

[0063] Step 204, performing semantic analysis and sentiment analysis on the job description and the job applicant's resume to generate a job requirement model and a candidate portrait;

[0064] Step 205, managing and maintaining user information, recruitment information, resume information, and job information in the system;

[0065] Step 206, training the model and optimizing the model through iterative techniques;

[0066] Step 207, using the results of big data analysis and machine learning to recommend the most suitable candidates to the enterprise, and can provide personalized recommendations and dynamic adjustments;

[0067] Step 208, monitor the recruitment needs of enterprises and market changes, and adjust the recommendation model and strategy based on enterprise feedback.

[0068] The big data analysis module of the present invention processes and analyzes massive data from different sources, extracts key features and generates high-quality matching models, thereby significantly improving the accuracy of recommendations and the recruitment efficiency of enterprises. Based on these analysis results, the intelligent recommendation module can flexibly respond to the personalized needs of different enterprises, accurately match candidates with positions, thereby improving the success rate of recommendations, and reducing the time and energy of enterprises in screening unsuitable candidates. In addition, through real-time monitoring and feedback mechanisms, the system can continuously optimize the recommendation algorithm, respond sensitively to market changes, further improve the accuracy and effectiveness of recommendations, and help enterprises gain an advantage in the fiercely competitive talent market.

[0069] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A talent push system integrating multi-channel data, characterized in that: include: Data collection module, which is used to collect data from multiple channels and fuse the obtained data; Data preprocessing module: The data preprocessing module is used to clean, convert and standardize the collected multi-channel data; Data storage module: the data storage module uses a combination of structured and unstructured databases to store preprocessed data and can store and retrieve large-scale data; Big data analysis module: The big data analysis module is used to perform semantic analysis and sentiment analysis on job descriptions and job seekers' resumes to generate job requirement models and candidate portraits; SSM framework management module: The SSM framework management module is used to manage and maintain user information, recruitment information, resume information, and job information in the system; Machine learning module, which is used to train models and optimize them through iterative techniques; Intelligent recommendation module: The intelligent recommendation module uses the results of big data analysis and machine learning to recommend the most suitable candidates to enterprises, and can provide personalized recommendations and dynamic adjustments; as well as Real-time monitoring and feedback module, the real-time monitoring and feedback module is used to monitor corporate recruitment needs and market changes, and adjust recommendation models and strategies based on corporate feedback.

2. The talent push system according to claim 1, characterized in that: When the big data analysis module generates the job demand model and candidate portrait, it first obtains the job description data and job seeker resume data from the data storage module and performs semantic analysis on the text content of the job description and job seeker resume to extract the corresponding key information. After extracting the key information, the formula S=σ(W s ·T+b s ) performs sentiment classification on the text. Then, based on the semantic analysis and sentiment analysis results of the job description, a job requirement model is constructed. Finally, based on the semantic analysis and sentiment analysis results of the job seeker's resume, a candidate portrait is generated. S is the sentiment score, ranging from 0 to 1, is the Sigmoid activation function, and W s is the weight matrix of the sentiment analysis model, T is the text vector after semantic analysis, and b s is the bias term of the sentiment analysis model.

3. The talent push system according to claim 1, characterized in that: In the process of managing and maintaining user information, recruitment information, resume information, and job information in the system, first, analyze the relationship between user information, recruitment information, resume information, and job information, and then design the corresponding database table structure for each entity. The database table structure includes user information table, recruitment information table, resume information table, and job information table.

4. The talent push system according to claim 1, characterized in that: When training and optimizing the talent matching algorithm, the machine learning module first extracts the data set used to train the model from the data storage module. The data set includes the characteristics of the recruitment position and the characteristics of the job seekers. Then, it extracts features from the job description and the job seeker's resume, and divides the data set into a training set and a validation set. It builds and trains a logistic regression model and initializes the weight parameters of the logistic regression model.

5. The talent push system according to claim 1, characterized in that: When establishing and training a logistic regression model, first, initialize the weight parameters of the logistic regression model, input the processed data into the logistic regression model, and adjust the weight parameters through gradient descent. Through multiple rounds of iterative training, the logistic regression model continuously adjusts the weights and bias terms to minimize the loss function. Use the validation set data to evaluate the accuracy, precision, recall, and F1 score of the logistic regression model. Finally, deploy the trained logistic regression model to the usage environment. The model will output the matching probability in real time based on the input job description and job seeker information.

6. The talent push system according to claim 1, characterized in that: When the intelligent recommendation module recommends the most suitable candidate to the enterprise, it first calculates the matching score between the candidate portrait established by the big data analysis module and the job demand model, and then calculates the matching score based on the enterprise's historical preference data and current job demand in the data storage module through the formula M′=α·M+β·H e The matching score is adjusted individually, and the most suitable candidate is recommended to the enterprise based on the matching score after the individual adjustment, where M′ is the matching score after the individual adjustment, M is the initial matching score, and H e is the historical preference score of the enterprise, which is calculated based on the enterprise's past selection history of candidates. α and β are the weights that adjust the impact of the matching score and the historical preference score, and α+β=1.

7. The talent push system according to claim 1, characterized in that: After recommending candidates, we will use the formula to determine the recommended candidate based on the company’s feedback on the recommended results. Dynamically adjust the parameters of the recommendation model to improve the accuracy of subsequent recommendations. After the model parameters are adjusted, recalculate the matching score and generate a new candidate recommendation list, where Δθ is the adjustment value of the model parameter θ, η is the learning rate, which controls the step size of the parameter adjustment, and L is the loss function, which is defined according to enterprise feedback and represents the difference between the recommendation results and the actual needs of the enterprise.

8. The talent push system according to claim 1, characterized in that: Continuously monitor the progress of the company's recruitment needs and the overall changes in the recruitment market. Whenever a company releases new recruitment information or modifies existing information, the system automatically records these changes, regularly obtains data from multiple recruitment platforms, industry reports, and market analysis tools, and updates the market status. When the company receives the recommended candidates, it will provide feedback through the system. Whenever the company provides feedback, the system updates the loss function based on the feedback and adjusts the model parameters through the gradient descent method. After receiving the feedback, the system uses the formula S new =S old +ΔS(M(t),F e ) recalculate the recommended strategy to ensure that the new strategy can reflect the current market conditions and enterprise needs, where S new represents the adjusted recommendation strategy, S old represents the current recommended strategy, ΔS(M(t), F e ) is based on market changes M(t) and enterprise feedback F e The resulting strategy adjustment amount.

9. The talent push system according to claim 1, characterized in that: After the model and strategy are adjusted, first, by formula Verify whether the adjusted strategy can effectively improve the recommendation accuracy. Then, use the formula Verify whether the adjusted model can effectively improve the recommendation accuracy, where V represents the new strategy S new The validation accuracy of i is the i-th test sample; After completing the adjustment and verification of the model and strategy, the new model and strategy are deployed to actual applications and based on the new recommendation strategy, the candidate recommendation list is recalculated and generated and pushed to the company for the next round of recruitment.

10. A talent push method based on multi-channel data fusion, characterized in that: The following steps are involved: Collect data from multiple channels and fuse the obtained data; Clean, convert and standardize the collected multi-channel data; It uses a combination of structured and unstructured databases to store preprocessed data and can store and retrieve large-scale data; Perform semantic and sentiment analysis on job descriptions and job seekers’ resumes to generate job requirement models and candidate portraits; Manage and maintain user information, recruitment information, resume information, and job information in the system; Train the model and optimize it through iterative techniques; Using the results of big data analysis and machine learning, we recommend the most suitable candidates to companies, and can make personalized recommendations and dynamic adjustments; Monitor corporate recruitment needs and market changes, and adjust recommendation models and strategies based on corporate feedback.

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