A person-job matching method based on deep learning

Abstract

The present invention discloses a person-job matching method based on deep learning. First, a job set and an employee set are constructed and text descriptions are performed. A text matching model is used to calculate the text matching scores between the jobs and the employees. At the same time, a relationship graph between the jobs and the employees is constructed. A relationship matching model based on a relationship graph convolutional network is used to calculate the relationship matching scores between the jobs and the employees. The text matching model and the relationship matching model are jointly trained through a co-teaching mechanism. The trained text matching model and relationship matching model are then used to predict the text matching scores and the relationship matching scores. Finally, the text matching scores and the relationship matching scores are weightedly calculated to obtain the final job-employee matching scores. The present invention combines deep text semantic analysis with a person-job relationship graph network, and performs training optimization through a co-teaching mechanism, which significantly improves the accuracy and robustness of matching. It is particularly suitable for enterprise environments with sparse data and high noise.

Description

Technical Field

[0001] The present invention relates to the field of computer data processing technology, and in particular to a person-job matching method based on deep learning. Background Art

[0002] Currently, traditional enterprise employee-job matching methods often rely on keyword matching or simple text similarity calculations to determine employee-job compatibility. This approach often overlooks the deeper semantic information between job descriptions and employee skills, failing to accurately capture the degree of match between an employee's actual abilities and job requirements, resulting in suboptimal matching results.

[0003] As the demand for accurate employee competency assessment and job matching in enterprise human resource management grows, the limitations of traditional approaches are becoming increasingly apparent. Existing job-person matching methods struggle to address these challenges, particularly when data is sparse, job requirements are complex and varied, and employee skill profiles vary. This results in low matching efficiency and accuracy. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a person-job matching method based on deep learning, which combines deep text semantic analysis and person-job relationship graph network, and performs training optimization through a co-teaching mechanism, significantly improving the accuracy and robustness of matching, and is particularly suitable for enterprise environments with sparse data and high noise.

[0005] The technical solution of the present invention is:

[0006] A deep learning-based person-job matching method specifically includes the following steps:

[0007] (1) Construct a job set and an employee set. Each job in the job set and each employee in the employee set corresponds to a text representation. Build a text matching model based on the BERT model and the dual attention mechanism. Use the text matching model to calculate the text matching scores of the job and the employee.

[0008] (2) Construct a relationship graph between positions and employees, and calculate the relationship matching score between positions and employees based on the relationship matching model of the relationship graph convolutional network;

[0009] (3) The text matching model and the relationship matching model are jointly trained through a co-teaching mechanism to obtain the trained text matching model and relationship matching model;

[0010] (4) Use the trained text matching model and relationship matching model to predict the text matching score and relationship matching score, and then perform weighted calculation on the text matching score and relationship matching score to obtain the final position and employee matching score.

[0011] The specific steps of constructing a set of positions and a set of employees and using a text matching model to calculate text matching scores between positions and employees are as follows:

[0012] S11. Construct the job set J = {j1, j2, ..., j i ,…,j n}、Employee set R=

[0013] {r1,r2,…,r k ,…,r m}, each position j i Corresponding to a job description X ji , each employee r k Corresponding to an employee resume Job Description and employee resumes Both are vectors of text representation;

[0014] S12. Use BERT model to describe the job and employee resumes Encoding is performed to generate semantic representations of job texts and employee texts, as shown in the following formula (1):

[0015]

[0016] In formula (1), The semantic representation matrix representing the job text, Represents the semantic representation matrix of employee text, d is the encoding dimension, and L is the text length;

[0017] S13, adopts a dual attention mechanism consisting of affinity attention mechanism and difference attention mechanism to capture the similarity and difference in text;

[0018] The affinity attention mechanism is used to capture the similarity information between job text and employee text. For each job j i and employees k , the similarity of the text is calculated by attention weight, as shown in the following formula (2):

[0019]

[0020] In formula (2), The affinity attention matrix represents the similarity between the job text and the employee text, T represents the transpose of the matrix, and softmax represents the softmax activation function;

[0021] The differential attention mechanism captures the differences between positions and employees through vector subtraction, as shown in the following formula (3):

[0022]

[0023] In formula (3), The difference attention matrix represents the difference between the job text and the employee text, and softmax represents the softmax activation function;

[0024] Then the affinity attention matrix and the difference attention matrix are weighted fused, as shown in the following formula (4):

[0025] A final =λ×A aff +(1-λ)×A diff (4);

[0026] In formula (4), λ∈[0,1] represents the hyperparameter for adjusting similarity and difference; A final represents the dual attention matrix;

[0027] S14. Calculate the matching score between the position and the employee through the semantic representation after text matching. The calculation process is shown in the following formula (5):

[0028]

[0029] In formula (5), s text (j i ,r k ) indicates position j i and employees k The matching score represents the semantic similarity between the position and the employee.

[0030] The specific steps of constructing the relationship graph between positions and employees and calculating the relationship matching scores between positions and employees based on the relationship matching model of the relationship graph convolutional network are as follows:

[0031] S21. Construct a relationship graph between positions and employees as G = (V, E), where V = J ∪ R is the combination of nodes in the relationship graph, and E is the set of edges representing the relationship between positions and employees;

[0032] S22, using the graph convolution network R-GCN to represent each node v∈V in the graph It is updated by aggregating messages from neighboring nodes, as shown in the following formula (6):

[0033]

[0034] In formula (6), N r (v) is the neighbor set of node v under relationship type r, W r is the parameter matrix of relation type r, is the representation of neighbor nodes, W0 represents the weight matrix of node v’s own features, and σ is the activation function;

[0035] After each node is updated through aggregation, the node representation of the updated position or employee is obtained. When node v represents a job node, That is When node v represents an employee node, That is

[0036]

[0037] S23. Calculate the relationship matching score between positions and employees through the relationship representation learned by the graph neural network, as shown in the following formula (7):

[0038]

[0039] In formula (7), s relation (j i ,r k ) indicates position j i and employees k The relationship matching score, is the node representation of any updated position node, yes The transpose of It is the node representation of any employee node after the update.

[0040] The specific steps of jointly training the text matching model and the relationship matching model through the co-teaching mechanism are as follows: the first batch of training data of the text matching model is first predicted by the relationship matching model, and the first batch of high-quality samples are screened out; the first batch of training data of the relationship matching model is first predicted by the text matching model, and the first batch of high-quality samples are screened out; the text matching model and the relationship matching model are respectively trained based on the first batch of high-quality samples after screening, and then multiple batches of cross-training are repeated, so that the text matching model and the relationship matching model share parameters with each other, and the updated results are finally obtained, and the trained text matching model and relationship matching model are finally obtained.

[0041] The text matching score and the relationship matching score are weighted and calculated, as shown in the following formula (8):

[0042] s final (j i ,r k )=α×s text (j i ,r k )+(1-α)×s relation (j i ,r k) (8);

[0043] In formula (8), α is a hyperparameter used to control the weights of text matching score and relationship matching score.

[0044] Advantages of the present invention:

[0045] The text matching model of the present invention combines the BERT model and the dual-attention mechanism. The BERT model can generate deep semantic representations. However, the semantic complexity of job and employee descriptions, that is, the fine-grained differences that may be contained in the text, is difficult to evaluate using simple vector similarity. Moreover, the correlation between employees and jobs cannot be fully considered based on text semantics. Therefore, the dual-attention mechanism is introduced to capture the similarities and differences in the text.

[0046] The relationship matching model of the present invention performs neural network learning based on the constructed position-employee relationship graph, thereby constructing implicit associations between employees and positions based on skill keywords in position descriptions and employee resumes, capturing the skill associations between positions and employees and the similarities between positions.

[0047] The present invention conducts joint training of two models through a co-teaching mechanism, so that the two models can verify each other during the training process, ensuring that the two models can maintain efficient training effects even when the data is sparse and noisy.

[0048] By introducing a dual attention mechanism and a relational graph convolutional network, this invention solves the shortcomings of traditional methods in semantic capture and data noise processing, significantly improving the accuracy and robustness of employee-job matching within an enterprise. It can not only effectively process the explicit semantic information between employees and jobs, but also mine implicit associations through relational graphs, providing more comprehensive decision-making support for the enterprise's human resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0051] See Figure 1 , a person-job matching method based on deep learning, specifically including the following steps:

[0052] (1) Construct a job set and an employee set. Each job in the job set and each employee in the employee set corresponds to a text representation. Build a text matching model based on the BERT model and the dual attention mechanism. Use the text matching model to calculate the text matching scores of the job and the employee. The specific steps are as follows:

[0053] S11. Construct the job set J = {j1, j2, ..., j i ,…,j n}、Employee set R=

[0054] {r1,r2,…,r k ,…,r m}, each position j i Corresponding to a job description X ji , each employee r k Corresponding to an employee resume Job Description and employee resumes Both are vectors of text representation;

[0055] S12. Use BERT model to describe the job and employee resumes Encoding is performed to generate semantic representations of job texts and employee texts, as shown in the following formula (1):

[0056]

[0057] In formula (1), The semantic representation matrix representing the job text, Represents the semantic representation matrix of employee text, d is the encoding dimension, and L is the text length;

[0058] S13, adopts a dual attention mechanism consisting of affinity attention mechanism and difference attention mechanism to capture the similarity and difference in text;

[0059] The affinity attention mechanism is used to capture the similarity information between job text and employee text. For each job j i and employees k , the similarity of the text is calculated by attention weight, as shown in the following formula (2):

[0060]

[0061] In formula (2), The affinity attention matrix represents the similarity between the job text and the employee text, T represents the transpose of the matrix, and softmax represents the softmax activation function;

[0062] The differential attention mechanism captures the differences between positions and employees through vector subtraction, as shown in the following formula (3):

[0063]

[0064] In formula (3), The difference attention matrix represents the difference between the job text and the employee text, and softmax represents the softmax activation function;

[0065] Then the affinity attention matrix and the difference attention matrix are weighted fused, as shown in the following formula (4):

[0066] A final =λ×A aff +(1-λ)×A diff (4);

[0067] In formula (4), λ∈[0,1] represents the hyperparameter for adjusting similarity and difference; A final represents the dual attention matrix;

[0068] S14. Calculate the matching score between the position and the employee through the semantic representation after text matching. The calculation process is shown in the following formula (5):

[0069]

[0070] In formula (5), s text (j i ,r k ) indicates position j i and employees k The matching score represents the semantic similarity between the position and the employee;

[0071] (2) Construct a relationship graph between positions and employees, and calculate the relationship matching score between positions and employees based on the relationship matching model of the relationship graph convolutional network. The specific steps are as follows:

[0072] S21. Construct a relationship graph between positions and employees as G = (V, E), where V = J ∪ R is the combination of nodes in the relationship graph, and E is the set of edges representing the relationship between positions and employees;

[0073] S22, using the graph convolution network R-GCN to represent each node v∈V in the graph It is updated by aggregating messages from neighboring nodes, as shown in the following formula (6):

[0074]

[0075] In formula (6), N r (v) is the neighbor set of node v under relationship type r, Wr is the parameter matrix of relation type r, is the representation of neighbor nodes, W0 represents the weight matrix of node v’s own features, and σ is the activation function;

[0076] After each node is updated through aggregation, the node representation of the updated position or employee is obtained. When node v represents a job node, That is When node v represents an employee node, That is

[0077]

[0078] S23. Calculate the relationship matching score between positions and employees through the relationship representation learned by the graph neural network, as shown in the following formula (7):

[0079]

[0080] In formula (7), s relation (j i ,r k ) indicates position j i and employees k The relationship matching score, is the node representation of any updated position node, yes The transpose of is the node representation of any employee node after the update;

[0081] (3) The text matching model and the relationship matching model are jointly trained through a co-teaching mechanism, that is, the first batch of training data of the text matching model is first predicted by the relationship matching model, and the first batch of high-quality samples are screened out. The text matching model and the relationship matching model are trained based on the first batch of high-quality samples after screening, and then multiple batches of cross-training are repeated, so that the text matching model and the relationship matching model share parameters with each other, and the updated results are finally obtained. The trained text matching model and relationship matching model are obtained;

[0082] (4) The trained text matching model and relationship matching model are used to predict the text matching score and relationship matching score, and then the text matching score and relationship matching score are weighted to obtain the final position and employee matching score, as shown in the following formula (8):

[0083] s final (j i ,r k )=α×s text(j i ,r k )+(1-α)×s relation (j i ,r k ) (8);

[0084] In formula (8), α is a hyperparameter used to control the weights of text matching score and relationship matching score.

[0085] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A person-job matching method based on deep learning, characterized by: The specific steps include: (1) Construct a job set and an employee set. Each job in the job set and each employee in the employee set corresponds to a text representation. Build a text matching model based on the BERT model and the dual attention mechanism. Use the text matching model to calculate the text matching scores of the job and the employee. The specific steps are as follows: S11. Construct the job set J = {j1, j2, ..., j i ,…,j n }、Employee set R= {r1,r2,…,r k ,…,r m }, each position j i Corresponding to a job description Each employee k Corresponding to an employee resume Job Description and employee resumes Both are vectors of text representation; S12. Use BERT model to describe the job and employee resumes Encoding is performed to generate semantic representations of job texts and employee texts, as shown in the following formula (1): In formula (1), The semantic representation matrix representing the job text, Represents the semantic representation matrix of employee text, d is the encoding dimension, and L is the text length; S13, adopts a dual attention mechanism consisting of affinity attention mechanism and difference attention mechanism to capture the similarity and difference in text; The affinity attention mechanism is used to capture the similarity information between job text and employee text. For each job j i and employees k , the similarity of the text is calculated by attention weight, as shown in the following formula (2): In formula (2), The affinity attention matrix represents the similarity between the job text and the employee text, T represents the transpose of the matrix, and softmax represents the softmax activation function; The differential attention mechanism captures the differences between positions and employees through vector subtraction, as shown in the following formula (3): In formula (3), The difference attention matrix represents the difference between the job text and the employee text, and softmax represents the softmax activation function; Then the affinity attention matrix and the difference attention matrix are weighted fused, as shown in the following formula (4): A final =λ×A aff +(1-λ)×A diff (4); In formula (4), λ∈[0,1] represents the hyperparameter for adjusting similarity and difference; A final represents the dual attention matrix; S14. Calculate the matching score between the position and the employee through the semantic representation after text matching. The calculation process is shown in the following formula (5): In formula (5), s text (j i ,r k ) indicates position j i and employees k The matching score represents the semantic similarity between the position and the employee; (2) Construct a relationship graph between positions and employees, and calculate the relationship matching score between positions and employees based on the relationship matching model of the relationship graph convolutional network. The specific steps are as follows: S21. Construct a relationship graph between positions and employees as G = (V, E), where V = J ∪ R is the combination of nodes in the relationship graph, and E is the set of edges representing the relationship between positions and employees; S22, using the graph convolution network R-GCN to represent each node v∈V in the graph It is updated by aggregating messages from neighboring nodes, as shown in the following formula (6): In formula (6), N r (v) is the neighbor set of node v under relationship type r, W r is the parameter matrix of relation type r, is the representation of neighbor nodes, W0 represents the weight matrix of node v’s own features, and σ is the activation function; After each node is updated through aggregation, the node representation of the updated position or employee is obtained. When node v represents a job node, That is When node v represents an employee node, That is S23. Calculate the relationship matching score between positions and employees through the relationship representation learned by the graph neural network, as shown in the following formula (7): In formula (7), s relation (j I ,r k ) indicates position j i and employees k The relationship matching score, is the node representation of any updated position node, yes The transpose of is the node representation of any employee node after the update; (3) The text matching model and the relationship matching model are jointly trained through a co-teaching mechanism to obtain the trained text matching model and relationship matching model; (4) Use the trained text matching model and relationship matching model to predict the text matching score and relationship matching score, and then perform weighted calculation on the text matching score and relationship matching score to obtain the final position and employee matching score.

2. The deep learning-based person-job matching method according to claim 1, characterized in that: The specific steps of jointly training the text matching model and the relationship matching model through the co-teaching mechanism are as follows: the first batch of training data of the text matching model is first predicted by the relationship matching model, and the first batch of high-quality samples are screened out; the first batch of training data of the relationship matching model is first predicted by the text matching model, and the first batch of high-quality samples are screened out; the text matching model and the relationship matching model are respectively trained based on the first batch of high-quality samples after screening, and then multiple batches of cross-training are repeated, so that the text matching model and the relationship matching model share parameters with each other, and the updated results are finally obtained, and the trained text matching model and relationship matching model are finally obtained.

3. The deep learning-based person-job matching method according to claim 2, characterized in that: The text matching score and the relationship matching score are weighted and calculated, as shown in the following formula (8): s final (j i ,r k )=α×s text (j i ,r k )+(1-α)×s relation (j i ,r k ) (8); In formula (8), α is a hyperparameter used to control the weights of text matching score and relationship matching score.

Images