CN112465482A - Talent recommendation method and system based on big data - Google Patents
Talent recommendation method and system based on big data Download PDFInfo
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- CN112465482A CN112465482A CN202011589989.6A CN202011589989A CN112465482A CN 112465482 A CN112465482 A CN 112465482A CN 202011589989 A CN202011589989 A CN 202011589989A CN 112465482 A CN112465482 A CN 112465482A
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Abstract
The invention discloses a talent recommendation method and system based on big data, which comprises the steps of filling job hunting information according to a job hunting information interface; extracting job hunting information for verification, outputting a modification interface if the verification fails, and reducing the credit rating of job hunters; performing simulated interview and on-site strain capacity assessment on job seekers, and performing scoring; judging whether the score is higher than a first threshold value; if the recommended interview course information is lower than the first threshold value, outputting a recommended interview course information selection interface; selecting online courses and online expenses or offline courses and offline expenses to perform corresponding learning, performing simulated interviews again within preset time until the score is higher than a first threshold value, and improving the career quality rating of the job seeker; and if the second threshold is higher than the first threshold, resume delivery is carried out. The preliminary examination and screening of job seekers are realized, and the interview post-study is performed after course recommendation and learning when skills are immature, so that reference is made for a recruitment enterprise, the tedious communication between the recruitment enterprise and the job seekers is reduced, and the recruitment efficiency is improved.
Description
Technical Field
The invention relates to the technical field of recruitment, in particular to a talent recommendation method and system based on big data.
Background
With the development of internet technology, network job hunting/recruitment becomes a main way for job seekers to find work and recruit employees by using units. However, the existing network only provides talent recruitment exhibition, does not perform preliminary examination and screening on talents, and reduces recruitment efficiency only by communicating interviews one by one through recruitment enterprises.
Disclosure of Invention
The invention aims to provide a talent recommendation method and system based on big data, and aims to solve the problems that the recruitment efficiency is reduced only by providing talent recruitment display, not performing preliminary examination and screening on talents and communicating interviews one by one through a recruitment enterprise in the conventional network.
In order to achieve the above object, in a first aspect, the present invention provides a talent recommendation method based on big data, including:
filling job hunting information according to the job hunting information interface;
extracting job hunting information for verification, outputting a modification interface if the verification fails, and reducing the credit rating of job hunters;
performing simulated interview and on-site strain capacity assessment on job seekers, and performing scoring;
judging whether the score is higher than a first threshold value;
if the recommended interview course information is lower than the first threshold value, outputting a recommended interview course information selection interface;
selecting a learning course according to the recommended interview course information selection interface, wherein the learning course comprises an online course and cost, an offline course and cost and skipping;
selecting online courses and online expenses or offline courses and offline expenses to perform corresponding learning, performing simulated interviews again within preset time until the score is higher than a first threshold value, and improving the career quality rating of the job seeker;
and if the second threshold is higher than the first threshold, resume delivery is carried out.
In an embodiment, the method further comprises:
when the offline course and the offline course cost are selected, the geographic positions of all the relevant offline courses are obtained to carry out ascending order arrangement of the distances, and the corresponding offline course cost is obtained to carry out ascending order arrangement.
In an embodiment, the method further comprises:
and making a visual chart according to the job hunting information, performing interview probability evaluation, and recommending to perform job hunting information input again if the evaluation score is lower than a second threshold value.
In an embodiment, the method further comprises:
acquiring occupation preference data information of job seekers, performing interview probability evaluation, and outputting a recommended occupation course information selection interface if the evaluation score is lower than a third threshold value.
In one embodiment, after the job hunting information is filled according to the job hunting information interface, the method further includes:
setting authority for checking job hunting information, wherein the authority for hunting the job includes public, hidden and public private resume.
In one embodiment, prior to performing the resume delivery, the method further comprises:
and acquiring the working address of the recruitment enterprise to carry out ascending order arrangement of the distance.
In a second aspect, the present invention also provides a talent recommendation system based on big data, including a module for the talent recommendation method based on big data of the first aspect.
According to the talent recommendation method and system based on big data, job hunting information is filled in according to the job hunting information interface; extracting job hunting information for verification, outputting a modification interface if the verification fails, and reducing the credit rating of job hunters; performing simulated interview and on-site strain capacity assessment on job seekers, and performing scoring; judging whether the score is higher than a first threshold value; if the recommended interview course information is lower than the first threshold value, outputting a recommended interview course information selection interface; selecting a learning course according to the recommended interview course information selection interface, wherein the learning course comprises an online course and cost, an offline course and cost and skipping; selecting online courses and online expenses or offline courses and offline expenses to perform corresponding learning, performing simulated interviews again within preset time until the score is higher than a first threshold value, and improving the career quality rating of the job seeker; and if the second threshold is higher than the first threshold, resume delivery is carried out. The preliminary examination and screening of job seekers are realized, and the interview post-study is performed after course recommendation and learning when skills are immature, so that reference is made for a recruitment enterprise, the tedious communication between the recruitment enterprise and the job seekers is reduced, and the recruitment efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a talent recommendation method based on big data according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a talent recommendation method based on big data according to another embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In a first aspect, please refer to fig. 1, where fig. 1 is a schematic flowchart of a talent recommendation method based on big data according to an embodiment of the present invention. Specifically, the talent recommendation method based on big data may include the following steps:
s101, filling job hunting information according to a job hunting information interface;
in the embodiment of the invention, the job hunting information is basic information filled by job hunters, such as names, ages, academic calendars, work experiences, native locations, employment posts, expected salaries and the like.
In one embodiment, the authority for checking job hunting information is set, and the job hunting information authority comprises public, hidden and public private curriculum vitae. The private record comprises an identity document, a home address and a telephone number. The job hunting information can be selectively disclosed or hidden at different moments. For example, a job seeker can select to disclose job hunting information in the job hunting process, so that a recruitment enterprise can conveniently check and select the job hunting information, after the recruitment enterprise and the job seeker achieve bidirectional selection, a private resume can be disclosed, only the recruitment enterprise can check the private resume, and the random leakage of identity information on the network is avoided; when the job seeker is in the period of trial, the job hunting information can be hidden, so that the convenience of continuing job hunting without meeting the requirement after the trial period can be realized, and the embarrassment of looking up the job hunting information by a trial enterprise in the period of trial is avoided.
S102, extracting job hunting information for verification, outputting a change interface if the verification fails, and reducing the credit rating of job hunters;
in the embodiment of the invention, if the academic calendar in the job hunting information is verified, the academic calendar can be compared with the academic calendar in the learning information network; verifying whether the work experience is true; if the verification is passed, the information is correct, the information can be selected according to the enterprise development condition, if the verification is not passed, the job seeker needs to input correct information again, and the credit rating of the job seeker is reduced, so that the job seeker is prompted to be filled in with integrity, the job seeker can be used as a reference for recruiting the talents of the enterprise, the talents are primarily screened, and the recruitment efficiency is improved.
S103, performing simulated interview and on-site strain capability assessment on job seekers, and performing grading;
in the embodiment of the invention, the on-line recruitment at present only knows the basic written information of the job seeker, and the field capability of the job seeker can be checked only by coming to a company for face-to-face communication, so that the recruitment efficiency is reduced. In the step, preliminary interview examination can be performed on job seekers in advance, and scoring can be performed. Judging whether the score is higher than a first threshold value; if the recommended interview course information is lower than the first threshold value, outputting a recommended interview course information selection interface; selecting a learning course according to the recommended interview course information selection interface, wherein the learning course comprises an online course and cost, an offline course and cost and skipping; selecting online courses and online expenses or offline courses and offline expenses to perform corresponding learning, performing simulated interviews again within preset time until the score is higher than a first threshold value, and improving the career quality rating of the job seeker; for example, if the score of the job seeker in the simulated interview examination is 75 minutes, and the first threshold is 85 minutes, the score is lower than the first threshold, an interview course is recommended to the job seeker, the job seeker can select whether to perform course learning according to the situation of the job seeker, the skill of the job seeker is improved, if the job seeker selects to perform course learning, the job seeker selects to perform on-line course learning or off-line course learning, the on-line course learning can start learning by directly paying related expenses, the on-line course can be selected according to the situation of the job seeker, namely, when the off-line course and the expenses are selected, the geographical positions of all related off-line courses are obtained to perform ascending order arrangement of distances, and the corresponding off-line course expenses are obtained to perform ascending order arrangement. The job seeker can select a proper offline course to study according to the economic condition and the geographic position. If the selection is skipped, the time can be selected again for interview simulation assessment, the previous scores can be recorded in job hunting information before the selection is skipped, so that the recruitment enterprises can conveniently refer to and select the scores, and if the simulation assessment is performed again, the scores can be updated and recorded according to assessment conditions.
And if the second threshold is higher than the first threshold, resume delivery is carried out. Namely, the field strain capacity of the job seeker is enough to meet the later work requirement. Before resume delivery, the method further comprises: and acquiring the working address of the recruitment enterprise to carry out ascending order arrangement of the distance. The recruitment enterprises close to the addresses of the job seekers can be automatically screened, and the trouble of house renting cost or long road distance is reduced.
Referring to fig. 2, fig. 2 is a flowchart illustrating a talent recommendation method based on big data according to another embodiment of the present invention. Specifically, the flowchart of the talent recommendation method based on big data may include the following steps:
s201, filling job hunting information according to the job hunting information interface;
s202, extracting job hunting information for verification, outputting a change interface if the verification fails, and reducing the credit rating of job hunters;
s203, performing simulated interview on job seekers, performing field strain capacity assessment, grading, and judging whether the grade is higher than a first threshold value;
the specific implementation of steps S201-S203 is described in steps S101-S103, and will not be described herein again.
S204, a visual chart is made according to job hunting information, interview probability evaluation is conducted, and if the evaluation score is lower than a second threshold value, job hunting information input is recommended to be conducted again;
s205, acquiring occupation preference data information of the job seeker, performing interview probability evaluation, and outputting a recommended occupation course information selection interface if the evaluation score is lower than a third threshold.
In the embodiment of the invention, a visual chart is made according to job hunting information, interview probability evaluation is carried out, and if the evaluation score is lower than a second threshold value, job hunting information input is recommended to be carried out again. The attraction degree of job hunting information filled by the job hunter can be judged in advance, a reference opinion is given to the job hunter, the change is convenient, and the recruitment efficiency is improved. If the evaluation score of the job seeker is 65 points and the second threshold value is 70 points, and if the evaluation score is lower than the second threshold value, the job seeker is recommended to readjust the cover, experience introduction, future planning and the like of the job hunting information so as to improve the specialty and the interest point of the recruitment enterprise.
Acquiring occupation preference data information of job seekers, performing interview probability evaluation, and outputting a recommended occupation course information selection interface if the evaluation score is lower than a third threshold value. The job seeker has a career tendency, and whether the actual situation of the job seeker can smoothly enter the career field of the psychology can be judged in advance. If the job seeker is a mechanical draftsman, self-learns some mechanical design knowledge, the mental instrument post is a mechanical designer, interviewing probability evaluation is performed on the job seeker according to the design knowledge and working experience mastered by the job seeker, the evaluation score is 72 points, the third threshold value is 80 points, the third threshold value is lower than the third threshold value, the job seeker is recommended to perform professional knowledge training, the job seeker can select whether to perform training according to the actual situation, if the training is selected, the learning score can be recorded into job hunting information, the selection probability is increased, and enterprise reference selection is facilitated.
In one embodiment, the admission notification of the recruitment enterprise is acquired, and after the recruitment enterprise agrees to enter the job, the job hunting information on the network is moved to the cloud storage or downloaded to the terminal storage. Namely, the job seeker signs a long-term cooperative labor relationship with the recruitment enterprise, the job hunting information can be removed from the website, disturbance is avoided, and embarrassment seen by the enrollment enterprise is also avoided; meanwhile, job hunting information is moved to a cloud end for storage or downloaded to a terminal for storage, and can be directly uploaded if work replacement is needed again, so that the job hunting system is convenient and fast.
In one embodiment, data cleaning is performed on job hunting information records on the network. The job hunting information is mounted on the network for a long time, and is easily abused by lawbreakers, thereby causing troubles.
In one embodiment, if an interview invitation is not obtained within a preset time period, a job recommendation scheme is generated according to job hunting information and examination paper examination, and a job hunting information interface is output whether to be approved to be changed or not; if yes, modifying and delivering again after updating; and if not, outputting a recommended learning information interface. The job seeker can select whether to approve the change of job hunting information according to the situation of the job seeker, and if so, the job seeker can modify and deliver the job hunting information again, and if the assessment situation is good and the job hunting information is possibly not properly planned, the job seeker can plan and deliver the job hunting information again; if the user does not agree with the skill, a recommended learning information interface is output, and if the skill of the user is not good enough, the user can choose courses to learn and improve the skill of the user.
In a second aspect, the present invention also provides a talent recommendation system based on big data, including a module for the talent recommendation method based on big data of the first aspect.
For specific embodiments, reference is made to the above description, which is not repeated herein.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A talent recommendation method based on big data is characterized by comprising the following steps:
filling job hunting information according to the job hunting information interface;
extracting job hunting information for verification, outputting a modification interface if the verification fails, and reducing the credit rating of job hunters;
performing simulated interview and on-site strain capacity assessment on job seekers, and performing scoring;
judging whether the score is higher than a first threshold value;
if the recommended interview course information is lower than the first threshold value, outputting a recommended interview course information selection interface;
selecting a learning course according to the recommended interview course information selection interface, wherein the learning course comprises an online course and cost, an offline course and cost and skipping;
selecting online courses and online expenses or offline courses and offline expenses to perform corresponding learning, performing simulated interviews again within preset time until the score is higher than a first threshold value, and improving the career quality rating of the job seeker;
and if the second threshold is higher than the first threshold, resume delivery is carried out.
2. The big-data based talent recommendation method of claim 1, further comprising:
when the offline course and the offline course cost are selected, the geographic positions of all the relevant offline courses are obtained to carry out ascending order arrangement of the distances, and the corresponding offline course cost is obtained to carry out ascending order arrangement.
3. The big-data based talent recommendation method of claim 1, further comprising:
and making a visual chart according to the job hunting information, performing interview probability evaluation, and recommending to perform job hunting information input again if the evaluation score is lower than a second threshold value.
4. The big-data based talent recommendation method of claim 1, further comprising:
acquiring occupation preference data information of job seekers, performing interview probability evaluation, and outputting a recommended occupation course information selection interface if the evaluation score is lower than a third threshold value.
5. The big-data-based talent recommendation method according to claim 1, wherein after completing job hunting information according to the job hunting information interface, the method further comprises:
setting authority for checking job hunting information, wherein the authority for hunting the job includes public, hidden and public private resume.
6. The big-data based talent recommendation method of claim 1, wherein prior to making a resume delivery, the method further comprises:
and acquiring the working address of the recruitment enterprise to carry out ascending order arrangement of the distance.
7. A big data based talent recommendation system, comprising means for performing the big data based talent recommendation method of any of claims 1-6.
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Cited By (1)
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| CN113869672A (en) * | 2021-09-10 | 2021-12-31 | 安徽梦森科技有限公司 | Talent training index system educational administration management system |
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