CN112258032A - Recruitment service method based on talent data - Google Patents
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Abstract
The invention discloses a recruitment service method based on talent data; s1: collecting a large amount of information data; s2: grading and screening the job seeker; s3: the job hunting information is combined and proportioned through a combination method; s4: gathering and editing feature sentences on job hunting information through neural network-deep learning; s5: the enterprise selects to send an interview invitation; according to the invention, information of job seekers is integrated through a combination method, an optimization method and neural network-deep learning, excellent skills are highlighted, the job seekers can be quickly matched with enterprise requirements through a model, self-recommendation paragraphs of the job seekers are compiled through the neural network-deep learning, so that the job seekers and enterprises can conveniently watch, before job hunting books are put in directionally, the job seekers are evaluated, so that the skills and qualities of the job seekers can be sorted, and the enterprises can conveniently watch the job seekers.
Description
Technical Field
The invention belongs to the technical field of talent recruitment, and particularly relates to a recruitment service method based on talent data.
Background
With the rapid development of communication technology and the explosive growth of information, how to select required contents from massive data becomes an important research direction in the field of information processing, and the aspect that work and study of people are influenced by information matching and effective use at present. Resume information is also one part of the massive data, recruitment is one of important works in human resource management, resume screening is the first link of recruitment, and currently, the acquisition of resumes by using a network recruitment platform is a mode frequently used by human resource managers. In the prior art, a network recruitment website generally performs simple screening and matching according to conditions roughly set by an enterprise, for example, performs condition screening according to target positions, work places, academic professions and the like, and sends the resume meeting the simple conditions to the enterprise. However, in the using process, the matching mode is poor in effect, the provided resume cannot meet the requirements of enterprises, and the recruiters are often required to manually screen again; on the other hand, the resume has more information, so that the recruiter is lack of effective quantitative evaluation on the resume, and the resume requiring talents may be missed, however, various talent recruitment methods on the market still have various problems.
For example, the grant bulletin number CN111445202A discloses a recruitment service method based on talent data, which realizes that although a postman does not need to repeatedly deliver the same resume to a plurality of companies, an enterprise can also find information such as corresponding work experience of the postman in a talent database according to a label, thereby reducing the tedious process of repeated delivery of the postman and synchronously increasing the selection range of the postman and the enterprise; the enterprise can directly match the corresponding post label through the label in the talent database to form a set of objective standards, so that the subjective judgment of human resources is reduced, and the accuracy of candidate selection is improved; the enterprise and the candidate can be contacted at the first time by depending on an internet platform, so that the loss of suitable candidates is avoided; the recruitment service method based on talent data can help enterprises increase the number of received resumes to increase the selection area of the enterprises, but the problems that feature extraction and enterprise requirements matching cannot be achieved for job seekers, a large amount of working labor is generated for job seekers and enterprise recruitment, the job seekers cannot be effectively evaluated, and self-recommendation document sections cannot be repaired by assisting the job seekers are solved, and therefore the recruitment service method based on talent data is provided.
Disclosure of Invention
The invention aims to provide a recruitment service method based on talent data so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a recruitment service method based on talent data comprises the following steps:
s1: collecting a large amount of information data; collecting various job hunting information and enterprise demand information, and carrying out model construction on the job hunting information and the enterprise demand information, wherein the model is constructed for screening bidirectional ratio of job hunting and enterprises;
s2: grading and screening the job seeker; processing the stored information of the job seeker, and grading and judging the job seeker according to work experience, character and the like to realize primary screening of the job seeker;
s3: the job hunting information is combined and proportioned through a combination method; combining and matching the job hunting information through a combination method to form a label, extracting prominent features of the label combined in the job hunting information through an optimization method, and matching the label obtained through the combination method and the optimization method with enterprise requirements;
s4: gathering and editing feature sentences on job hunting information through neural network-deep learning; generating self-recommendation paragraphs by the information of job seekers through a neural network-deep learning method to form smooth sentences, and sending the self-recommendation paragraphs to enterprises;
s5: the enterprise selects to send an interview invitation; the enterprise carries out a preliminary understanding to the job seeker through grading to self-recommendation paragraph and job seeker, and after the enterprise knows the job seeker, can realize sending the interview invitation to the job seeker or refute the notice.
Preferably, the model construction in S1 includes a combination method, an optimization method, and a neural network-deep learning, and the model construction is based on block chain construction, so that the model detection can collect data information of different block chain points.
Preferably, the combination method combines technical ability and job hunting intention in job hunting information so as to make the job hunting information concise and clear.
Preferably, the scoring and screening of the job seeker in S2 includes talent skill scoring and comprehensive quality scoring, talents in the same industry are compared and scored, the training sample is trained by using a machine deep learning algorithm, and a talent data scoring model is obtained; the talent data scoring model is used for scoring the massive electronic resumes, the talents in the same industry are scored to obtain a resume library with scoring values, the comprehensive quality scoring is used for scoring seven dimensions of moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability of the talents, users upload honor and prize-obtaining certificates of any activity competition and data capable of showing the moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability, and the cloud server scores and ranks the data.
Preferably, the label in S3 is obtained by extracting excellent skills of the job seeker through a combination method and an optimization method, and is paired with the enterprise requirement through the excellent skills.
Preferably, the self-recommendation passage in S4 implements extraction of features of the job seeker through a combination method and an optimization method, and optimizes the recommendation passage to bring up the advantages of self in the recommendation passage.
Preferably, the job hunting information in S1 includes name, age, gender, academic calendar, graduation institution, specialty, contact information, talent present address, birth address, future expected development address and ability.
Preferably, the model in S1 is subjected to vertical search when capturing information of job seekers, and the vertical search excludes data that does not have reference meaning through a machine learning algorithm, specifically, the method includes manually collecting a large amount of irrigation content as a positive sample of the machine learning algorithm, then training a classification model through the sample, and finally judging whether the collected data is invalid content by using the classification model.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the invention, information of job seekers is integrated through a combination method, an optimization method and neural network-deep learning, excellent skills are highlighted, the job seekers can be quickly matched with enterprise requirements through a model, directional job hunting is completed, bidirectional pressure of the job seekers and enterprises is reduced, self-recommendation paragraphs of the job seekers are compiled through the neural network-deep learning, and the job seekers and the enterprises can be conveniently watched.
(2) According to the invention, before the job hunting books are directionally put in, the job hunters are evaluated, so that the skills and qualities of the job hunters can be sorted, the enterprises can conveniently watch the job hunters, the model can rapidly complete the pairing between the job hunters and the enterprises, and the job hunting rate is improved.
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Fig. 1 is a schematic diagram illustrating a service method step structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: a recruitment service method based on talent data comprises the following steps:
s1: collecting a large amount of information data; collecting various job hunting information and enterprise demand information, and carrying out model construction on the job hunting information and the enterprise demand information, wherein the model is constructed for screening bidirectional ratio of job hunting and enterprises;
s2: grading and screening the job seeker; processing the stored information of the job seeker, and grading and judging the job seeker according to work experience, character and the like to realize primary screening of the job seeker;
s3: the job hunting information is combined and proportioned through a combination method; combining and matching the job hunting information through a combination method to form a label, extracting prominent features of the label combined in the job hunting information through an optimization method, and matching the label obtained through the combination method and the optimization method with enterprise requirements;
s4: gathering and editing feature sentences on job hunting information through neural network-deep learning; generating self-recommendation paragraphs by the information of job seekers through a neural network-deep learning method to form smooth sentences, and sending the self-recommendation paragraphs to enterprises;
s5: the enterprise selects to send an interview invitation; the enterprise carries out a preliminary understanding to the job seeker through grading to self-recommendation paragraph and job seeker, and after the enterprise knows the job seeker, can realize sending the interview invitation to the job seeker or refute the notice.
In order to realize the outstanding feature of arranging the information of the job seeker and realize the information sharing of each blockchain point, in this embodiment, preferably, the model construction in S1 includes a combination method, an optimization method and a neural network-deep learning, and the model construction is based on blockchain construction, so that the model detection can collect data information of different blockchain points.
In order to integrate the ability of the job seeker and make the ability of the job seeker clear, in this embodiment, preferably, the combination method combines the technical ability and the job hunting intention in the job hunting information so that the job hunting information is concise and clear.
In order to optimize the ability characteristics of the job seeker, highlight the ability emphasis of the job seeker and facilitate pairing, in the embodiment, preferably, the optimization method adopts a newton method, the newton method is a second-order convergence algorithm, and the second order of the newton method has the significance that the newton method can not only descend along the direction with the maximum gradient, but also consider that the gradient of the next step is not large, and the newton method can globally approach the target function with far sight.
In order to write a job hunting text segment for a job seeker and increase the success rate of job hunting, in this embodiment, preferably, the deep learning is to learn the internal rules and the representation levels of sample data, the information obtained in the learning process is helpful to explain data such as characters, images, and sounds, and the like, and the final objective of the deep learning is to make a machine have an analysis learning capability like a human being and recognize data such as characters, images, and sounds.
In order to implement bidirectional scoring of competencies and qualities of job seekers and facilitate enterprise selection, in this embodiment, preferably, the scoring and screening of job seekers in S2 includes talent skill scoring and comprehensive quality scoring, talents in the same industry are compared and scored, and the training samples are trained by using a machine deep learning algorithm to obtain a talent data scoring model; the talent data scoring model is used for scoring the massive electronic resumes, the talents in the same industry are scored to obtain a resume library with scoring values, the comprehensive quality scoring is used for scoring seven dimensions of moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability of the talents, users upload honor and prize-obtaining certificates of any activity competition and data capable of showing the moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability, and the cloud server scores and ranks the data.
In order to achieve purposeful release of resume and increase success rate of job hunting, in this embodiment, preferably, the label in S3 is extracted by a combination method and an optimization method according to superior skills of the job seeker, and is paired with the enterprise requirement by the superior skill.
In order to enable the self-recommendation of the job seeker to attract enterprises and fully show the ability of the job seeker, in the embodiment, preferably, the self-recommendation section in S4 implements extraction of features of the job seeker through a combination method and an optimization method, and optimizes the recommendation section to realize the advantages of self in the recommendation section.
In order to complete the information of the job seeker, facilitate the model processing, and facilitate the enterprise selection, in this embodiment, it is preferable that the job hunting information in S1 includes name, age, gender, academic calendar, graduation institution, specialty, contact information, talent present address, birth address, future expected development address, and capability.
In order to capture a large number of job seekers, in this embodiment, it is preferable that the model in S1 uses a vertical search when capturing information of job seekers, and the vertical search excludes data that does not have reference meaning through a machine learning algorithm.
The working principle and the using process of the invention are as follows:
the first step is as follows: collecting a large amount of information data; collecting various job hunting information and enterprise demand information, and carrying out model construction on the job hunting information and the enterprise demand information, wherein the model is constructed for screening bidirectional ratio of job hunting and enterprises;
the second step is that: grading and screening the job seeker; processing the stored information of the job seeker, and grading and judging the job seeker according to work experience, character and the like to realize primary screening of the job seeker;
the third step: the job hunting information is combined and proportioned through a combination method; combining and matching the job hunting information through a combination method to form a label, extracting prominent features of the label combined in the job hunting information through an optimization method, and matching the label obtained through the combination method and the optimization method with enterprise requirements;
the fourth step: gathering and editing feature sentences on job hunting information through neural network-deep learning; generating self-recommendation paragraphs by the information of job seekers through a neural network-deep learning method to form smooth sentences, and sending the self-recommendation paragraphs to enterprises;
the fifth step: the enterprise selects to send an interview invitation; the enterprise carries out a preliminary understanding to the job seeker through grading to self-recommendation paragraph and job seeker, and after the enterprise knows the job seeker, can realize sending the interview invitation to the job seeker or refute the notice.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A recruitment service method based on talent data is characterized in that: the method comprises the following steps:
s1: collecting a large amount of information data; collecting various job hunting information and enterprise demand information, and carrying out model construction on the job hunting information and the enterprise demand information, wherein the model is constructed for screening bidirectional ratio of job hunting and enterprises;
s2: grading and screening the job seeker; processing the stored information of the job seeker, and grading and judging the job seeker according to work experience, character and the like to realize primary screening of the job seeker;
s3: the job hunting information is combined and proportioned through a combination method; combining and matching the job hunting information through a combination method to form a label, extracting prominent features of the label combined in the job hunting information through an optimization method, and matching the label obtained through the combination method and the optimization method with enterprise requirements;
s4: gathering and editing feature sentences on job hunting information through neural network-deep learning; generating self-recommendation paragraphs by the information of job seekers through a neural network-deep learning method to form smooth sentences, and sending the self-recommendation paragraphs to enterprises;
s5: the enterprise selects to send an interview invitation; the enterprise carries out a preliminary understanding to the job seeker through grading to self-recommendation paragraph and job seeker, and after the enterprise knows the job seeker, can realize sending the interview invitation to the job seeker or refute the notice.
2. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the model construction in the step S1 includes a combination method, an optimization method, and a neural network-deep learning, and the model construction is based on block chain construction, so that the model detection can collect data information of different block chain points.
3. The talent data-based recruitment service method according to claim 2, wherein the recruitment service method comprises the following steps: the combination method combines the technical ability and job hunting intention in job hunting information so as to enable the job hunting information to be concise and clear.
4. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the scoring and screening of the job seeker in the S2 comprises talent skill scoring and comprehensive quality scoring, talents in the same industry are compared and scored, the training sample is trained by using a machine deep learning algorithm, and a talent data scoring model is obtained; the talent data scoring model is used for scoring the massive electronic resumes, the talents in the same industry are scored to obtain a resume library with scoring values, the comprehensive quality scoring is used for scoring seven dimensions of moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability of the talents, users upload honor and prize-obtaining certificates of any activity competition and data capable of showing the moral quality, citizen literacy, learning ability, communication cooperation and practice innovation, sports and health, aesthetic and performance ability, and the cloud server scores and ranks the data.
5. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the label in the S3 is obtained by extracting excellent skills of the job seeker through a combination method and an optimization method, and is paired with the enterprise requirement through the excellent skill.
6. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the self-recommendation paragraph in the S4 realizes the extraction of the features of the job seeker through a combination method and an optimization method, optimizes the recommendation paragraph, and brings up the advantages of self in the recommendation paragraph.
7. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the job hunting information in S1 includes name, age, gender, academic calendar, graduation institution, specialty, contact address, current location of talent, birth address, future expected development address and ability.
8. The talent data-based recruitment service method according to claim 1, wherein the recruitment service method comprises the following steps: the model in the step S1 adopts vertical search when capturing information of job seekers, and the vertical search excludes data that does not have reference meaning through a machine learning algorithm.
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