CN117236322A - A virtual headhunting assistant system and talent search method based on NLP - Google Patents

Abstract

The invention discloses a virtual headhunting assistant system based on NLP, which includes an NLP processing module: at least used to parse resumes and job description texts to obtain candidate information and job requirement information respectively; a position matching module: at least used to process NLP The module compares the candidate information and position requirement information obtained by the module to find the most matching candidate; talent activation module: at least used to send activation information to candidates to discover talents with the intention to change jobs; interactive module: at least Used to interact with users. This invention uses natural language processing technology to realize the automation and intelligence of the recruitment process, greatly reducing the workload of human resources specialists in the talent search process. At the same time, it can also simulate headhunters to send activation information, improve recruitment efficiency, and reduce manpower. Cost, it has brought major innovation to the human resources industry, with high practical value and broad market prospects.

Description

A virtual headhunting assistant system and talent search method based on NLP

Technical field

The present invention relates to the technical fields of artificial intelligence and human resources management, and in particular to a virtual headhunting assistant system and talent search method based on NLP.

Background technique

Currently, many steps in the recruitment process, such as resume screening, job matching and candidate communication, require a lot of manual operations and time investment costs. These tasks are complex, time-consuming, and require specialized knowledge and skills. In addition, the efficiency and quality of these tasks often depend on the experience and skills of the human resources specialist who performs the tasks. Therefore, try to design a virtual headhunting assistant system to improve the efficiency and effectiveness of the recruitment process while reducing the talent search process for human resources specialists. workload in.

Contents of the invention

The purpose of the present invention is to provide a virtual headhunting assistant system and talent search method based on NLP to improve the efficiency and effectiveness of the recruitment process while reducing the workload of human resources specialists.

In order to achieve the above purpose, the following technical solutions are adopted:

A virtual headhunting assistant system based on NLP, including

NLP processing module: at least used to parse resume and job description text to obtain candidate information and job requirement information respectively;

Job matching module: at least used to compare the candidate information and job requirement information obtained through the NLP processing module to find the most matching candidate;

Talent activation module: at least used to send activation information to candidates to discover talents who are interested in changing jobs;

Interaction module: at least used for information interaction with users.

Further, the NLP processing module parses resumes and job description texts through lexical analysis, syntactic analysis or semantic analysis, where the candidate information includes candidate basic information, educational background, work experience, and professional skills. Job requirement information includes job title, job responsibilities, and job requirements.

Further, the position matching module evaluates the matching degree between the candidate and the position by constructing the feature vectors of the candidate and the position respectively, and then calculating the similarity between the two, and performs the matching according to the level of matching. Sort.

Further, the feature vector includes the candidate's professional skills, the candidate's work experience, the candidate's educational background, job responsibilities, job requirements, and job benefits; the similarity is calculated using the cosine similarity algorithm, or Jaccard similarity. degree algorithm.

Further, the activation information includes emails, text messages, and private messages on social media.

Further, the NLP-based virtual headhunting assistant system also includes an A/B testing module; the A/B testing module is at least used to compare different activation strategies to optimize the content and timing of sending activation information.

A talent search method, including the above-mentioned NLP virtual headhunting assistant system, includes the following steps:

S1: The recruiter submits the candidate resume and job description text respectively through the interactive module;

S2: Analyze candidate resumes and job description texts through the NLP processing module to extract candidate information and job requirement information respectively;

S3: Compare candidate information and job requirement information through the job matching module to find the most matching candidate and recommend the candidate information to the recruiter;

S4: Send activation information to candidates through the talent activation module to discover talents who are interested in changing jobs, and feed back the activation feedback results to the recruiter.

Further, in the step S2, the resume and job description text are parsed through lexical analysis, syntactic analysis or semantic analysis, where the candidate information includes the candidate's basic information, educational background, work experience, and professional skills, so The above-mentioned job requirement information includes job title, job responsibilities, and job requirements.

Further, the step S3 specifically includes the following steps:

S31: Construct feature vectors of candidates and positions respectively, where the feature vectors include the candidate’s professional skills, the candidate’s work experience, the candidate’s educational background, job responsibilities, job requirements, and job benefits;

S32: Based on the cosine similarity algorithm or Jaccard similarity algorithm, calculate the similarity between the candidate and the position, and use this to evaluate the matching between the two;

S33: Sort according to the degree of matching, and recommend the candidates with the highest matching degree to the recruiter.

Further, the activation information in S4 includes emails, text messages, and private messages on social media.

Adopting the above solution, the beneficial effects of the present invention are:

This invention uses natural language processing technology to realize the automation and intelligence of the recruitment process, greatly reducing the workload of human resources specialists. At the same time, it can also simulate headhunting to send activation information, improve recruitment efficiency, reduce labor costs, and provide human resources The resources industry has brought about major innovations with high practical value and broad market prospects.

Description of drawings

Figure 1 is a flow chart of the method of the present invention;

Figure 2 is a workflow block diagram of the job matching module of the present invention;

Figure 3 is a workflow block diagram of the talent activation module of the present invention.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

Referring to Figures 1 to 3, the present invention provides a virtual headhunting assistant system based on NLP. In one embodiment, it includes

NLP processing module: at least used to parse resume and job description text to obtain candidate information and job requirement information respectively;

Job matching module: at least used to compare the candidate information and job requirement information obtained through the NLP processing module to find the most matching candidate;

Talent activation module: at least used to send activation information to candidates to discover talents who are interested in changing jobs;

Interaction module: at least used for information interaction with users.

In this embodiment, the NLP processing module is the core of the system and is mainly responsible for processing unstructured text data such as resumes and job descriptions. It can first deeply understand the job description text through lexical analysis, syntactic analysis, semantic analysis and other methods, and then extract useful information, such as the job seeker's skills, experience, educational background, etc., as well as the job requirements, job content, etc. . This module is also able to understand natural human language, including semantics, context, and emotion, to accurately understand the needs of candidates and positions. Specifically, for resume processing, the system will extract the candidate's basic information (including name, contact information, etc.), educational background, work experience, skills and other key information from the resume. When processing the job description text, the system will extract information such as job title, job responsibilities, and job requirements. In addition, through deep learning technology, such as word embedding (Word Embedding) and BERT model, the system can understand the contextual meaning of words and sentences and extract information more accurately.

For the job matching module, it is mainly responsible for comparing the candidate information extracted by the NLP processing module with the job requirement information to find the most matching candidate. It uses a sophisticated algorithm that not only compares keyword matches, but also considers the candidate's overall background and the overall requirements of the position. For example, we will consider whether the applicant's skills meet the job requirements, whether the work experience is sufficient, and whether the educational background matches, etc.

In one embodiment, the position matching module evaluates the matching degree between the candidate and the position by constructing feature vectors of the candidate and the position respectively, and then calculating the similarity between the two, and based on the matching degree Sort by high or low.

In this embodiment, the feature vector includes the candidate's professional skills, the candidate's work experience, the candidate's educational background, job responsibilities, job requirements, and position benefits; the similarity is calculated using the cosine similarity algorithm, Or Jaccard similarity algorithm.

For the talent activation module, it mainly simulates headhunting to send activation information, including emails, phone calls, social media messages, etc., to discover talents who are interested in changing jobs. At the same time, it can predict whether candidates are willing to change jobs based on their behavior and feedback, and send activation information in time to guide them to participate in the recruitment process. The system will also take candidates into consideration when designing activation information. The background, characteristics of the position, and potential needs of the candidate will also be included. The information will also include details of the position, an introduction to the company, and why the candidate is considered suitable for the position.

In addition, in one embodiment, in order to improve the success rate of activation, an A/B testing module is also included. Through the A/B testing module, different activation strategies can be compared to optimize the content and sending timing of activation information.

To sum up, the virtual headhunting assistant system designed in this application is mainly to achieve the following purposes:

1) Automated recruitment process: Through natural language processing technology, the virtual headhunting assistant can automatically extract information from resumes and job description texts to achieve automatic screening of candidates and job matching;

2) Improve recruitment efficiency: Through automated screening and matching, as well as simulated communication between headhunters and candidates, this virtual headhunter assistant can greatly shorten the recruitment cycle and improve recruitment efficiency;

3) Improve candidate experience: Through automatic and personalized communication, the virtual headhunting assistant can provide a better candidate experience, thereby attracting more outstanding talents;

4) Improve recruitment accuracy: Using natural language processing technology, the virtual headhunting assistant can more accurately understand the requirements of candidates and positions, thereby improving the accuracy of job matching.

In one embodiment, a talent search method is also provided, including the above-mentioned NLP virtual headhunting assistant system, including the following steps:

S1: Recruiters submit candidate resumes and job description texts through the interactive module.

In this step, the interactive module provides an intuitive and easy-to-use user interface, and users can conveniently submit information, view results, and manage candidates, which allows users to better utilize the system and improve user experience.

S2: Analyze candidate resumes and job description texts through the NLP processing module to extract candidate information and job requirement information respectively.

Specifically, in step S2, the resume and job description text are parsed through lexical analysis, syntactic analysis or semantic analysis, where the candidate information includes the candidate's basic information, educational background, work experience, and professional skills. Job requirement information includes job title, job responsibilities, and job requirements.

S3: Compare candidate information and job requirement information through the job matching module to find the most matching candidate and recommend the candidate information to the recruiter.

Specifically: Step S3 specifically includes the following steps:

S31: Construct feature vectors of candidates and positions respectively, where the feature vectors include the candidate’s professional skills, the candidate’s work experience, the candidate’s educational background, job responsibilities, job requirements, and job benefits;

S32: Based on the cosine similarity algorithm or Jaccard similarity algorithm, calculate the similarity between the candidate and the position, and use this to evaluate the matching between the two;

S33: Sort according to the degree of matching, and recommend the candidates with the highest matching degree to the recruiter.

S4: Send activation information to candidates through the talent activation module to discover talents who are interested in changing jobs, and feed back the activation feedback results to the recruiter. The activation information in step S4 includes emails, text messages, and private messages on social media.

In summary, this method has the following characteristics:

1) Intelligent: Using natural language processing technology, it can understand and process human natural language, thereby accurately obtaining and understanding the information in resumes and job descriptions. In addition, it can also predict and discover talents who are interested in changing jobs, and improve recruitment. efficiency;

2) Automation: It has a high degree of automation. All steps from resume parsing, job matching, candidate activation and feedback management can be completed automatically, which greatly improves the efficiency of recruitment and reduces the workload of human resources specialists. ;

3) Accuracy: A complex algorithm is used for job matching, which not only considers the matching degree of keywords, but also integrates the overall background of the candidate and the comprehensive requirements of the position, which makes the matching results more accurate and improves the quality of recruitment.

Through the above system and method, the present invention has the following technical effects:

1) Natural language processing technology can automatically parse and analyze resumes, which includes extracting key information, such as the job seeker’s skills, experience, educational background, etc., and evaluating and ranking based on this information, so that headhunters can screen more effectively Candidates who meet the job requirements are selected, greatly reducing the workload of preliminary screening;

2) Automated job matching can be realized. By analyzing the job description text and the job seeker's resume, it can automatically recommend the most matching candidates, which is more accurate than traditional keyword matching (because NLP can understand context and semantics, so as to Understand the needs of the position and job seekers to a certain extent);

3) Automated communication and coordination functions can be realized. For example, the system can replace manual preliminary screening of candidates, or automatically answer candidates' frequently asked questions about positions. This can not only reduce the workload of headhunters, but also improve the candidate's experience. ;

4) It can automatically find and activate talents who are interested in changing jobs. By analyzing public data on social media or professional social networking sites, it can automatically predict which talents may be interested in changing jobs (this prediction can be based on various factors, such as career development path, job satisfaction, industry trends, etc.), thereby improving recruitment efficiency.

The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention. Inside.

Claims (10)

1. A virtual headhunting assistant system based on NLP, characterized by including an NLP processing module: at least used to parse resumes and job description texts to obtain candidate information and job requirement information respectively; Job matching module: at least used to compare the candidate information and job requirement information obtained through the NLP processing module to find the most matching candidate; Talent activation module: at least used to send activation information to candidates to discover talents who are interested in changing jobs; Interaction module: at least used for information interaction with users. 2. The NLP-based virtual headhunting assistant system according to claim 1, characterized in that the NLP processing module parses resumes and job description texts through lexical analysis, syntactic analysis or semantic analysis, wherein the candidate The information includes the candidate's basic information, educational background, work experience, and professional skills. The job requirement information includes the job title, job responsibilities, and job requirements. 3. The NLP-based virtual headhunting assistant system according to claim 1, characterized in that the position matching module constructs feature vectors of candidates and positions respectively, and then calculates the similarity between the two. To evaluate the compatibility between candidates and positions, and rank them according to the degree of compatibility. 4. The NLP-based virtual headhunting assistant system according to claim 3, characterized in that the feature vector includes the candidate's professional skills, the candidate's work experience, the candidate's educational background, job responsibilities, job requirements, Job benefits; the similarity is calculated using the cosine similarity algorithm or the Jaccard similarity algorithm. 5. The NLP-based virtual headhunting assistant system according to claim 1, characterized in that the activation information includes emails, text messages, and private messages on social media. 6. The NLP-based virtual headhunting assistant system according to claim 5, characterized in that the NLP-based virtual headhunting assistant system further includes an A/B testing module; the A/B testing module is at least used for comparison. Different activation strategies to optimize the content and timing of activation messages. 7. A talent search method, including the NLP virtual headhunting assistant system according to any one of claims 1 to 6, characterized in that it includes the following steps: S1: The recruiter submits the candidate resume and job description text respectively through the interactive module; S2: Analyze candidate resumes and job description texts through the NLP processing module to extract candidate information and job requirement information respectively; S3: Compare candidate information and job requirement information through the job matching module to find the most matching candidate and recommend the candidate information to the recruiter; S4: Send activation information to candidates through the talent activation module to discover talents who are interested in changing jobs, and feed back the activation feedback results to the recruiter. 8. The talent search method according to claim 7, characterized in that in step S2, resumes and job description texts are parsed through lexical analysis, syntactic analysis or semantic analysis, wherein the candidate information includes candidates Basic information about the person, educational background, work experience, and professional skills. The job requirement information includes job title, job responsibilities, and job requirements. 9. The talent search method according to claim 8, characterized in that step S3 specifically includes the following steps: S31: Construct feature vectors of candidates and positions respectively, where the feature vectors include the candidate’s professional skills, the candidate’s work experience, the candidate’s educational background, job responsibilities, job requirements, and job benefits; S32: Based on the cosine similarity algorithm or Jaccard similarity algorithm, calculate the similarity between the candidate and the position, and use this to evaluate the matching between the two; S33: Sort according to the degree of matching, and recommend the candidates with the highest matching degree to the recruiter. 10. The talent search method according to claim 7, characterized in that the activation information in S4 includes emails, text messages, and private messages on social media.