CN115619364B - Recruitment information release method, device and system based on artificial intelligence - Google Patents
Recruitment information release method, device and system based on artificial intelligence Download PDFInfo
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Abstract
The disclosure provides a recruitment information issuing method, device and system based on artificial intelligence, and relates to the field of artificial intelligence. The scheme is as follows: determining recruitment characteristic information of an enterprise and each job seeker matched with the recruitment characteristic information; acquiring attention characteristic information of the job seeker based on historical operation data of the job seeker, and determining attention values of the job seeker for various recruitment posts according to the attention characteristic information of the job seeker; according to the attention value of the job seeker to various recruitment posts, determining the corresponding intention job seeker; determining the post to be recruited by the enterprise according to the current recruitment characteristic information of the enterprise; when the intention job-seeking post comprises the current recruitment post of the enterprise, resume information of the job seeker is sent to the enterprise, and recruitment information of the enterprise is sent to the job seeker. Therefore, the job seeker can be quickly and accurately matched with the enterprise, recruitment efficiency of the enterprise and job seeker finding efficiency are improved, great convenience is provided for the enterprise and the job seeker, and the cost is low.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, in particular to a recruitment information issuing method, device and system based on artificial intelligence.
Background
With the popularization of internet applications, more and more enterprises recruit through the network, and more job seekers begin to do the recruitment on the network. The internet recruitment which occurs on the internet is usually realized based on the following form that a recruiter issues position information on the internet, the recruiter acquires working positions meeting self-application conditions to conduct the recruitment, and the recruiter screens the recruiter meeting the self-application conditions and sends the recruitment information to conduct the recruitment.
However, the recruiter and the job seeker need to search, and both sides may lose their own information due to missing detection, which not only consumes a lot of manpower and time, but also has very narrow browsing surface, so that it is difficult for the enterprise or job seeker to obtain the information actually matching with their own needs. Therefore, how to efficiently issue recruitment information, so that recruiters and job seekers can quickly, accurately and reliably obtain information meeting own needs is a problem to be solved currently.
Disclosure of Invention
The disclosure provides a recruitment information issuing method, device and system based on artificial intelligence.
According to a first aspect of the present disclosure, there is provided an artificial intelligence based recruitment information publishing method, comprising:
determining current recruitment characteristic information of any enterprise and each job seeker matched with the recruitment characteristic information;
acquiring attention characteristic information of each job seeker based on historical operation data of each job seeker, and determining attention values of each job seeker to various recruitment posts according to the attention characteristic information of each job seeker;
determining the corresponding intention job-seeking positions of each job seeker according to the attention value of each job seeker to each recruitment position;
determining at least one post to be recruited currently by any enterprise according to the current recruitment characteristic information of the any enterprise;
and under the condition that the intention job-seeking post of any job seeker comprises at least one post currently to be recruited by any enterprise, sending the initial resume information of the any job seeker to the any enterprise, and sending the recruitment information of the any enterprise to the any job seeker.
According to a second aspect of the present disclosure, there is provided an artificial intelligence based recruitment information issuing apparatus comprising:
the first determining module is used for determining the current recruitment characteristic information of any enterprise and each job seeker matched with the recruitment characteristic information;
the acquisition module is used for acquiring the attention characteristic information of each job seeker based on the historical operation data of each job seeker, and determining the attention value of each job seeker to various recruitment posts according to the attention characteristic information of each job seeker;
the second determining module is used for determining the corresponding intention job-seeking position of each job seeker according to the attention value of each job seeker to each recruitment position;
the third determining module is used for determining at least one post to be recruited currently by any enterprise according to the current recruitment characteristic information of the any enterprise;
the sending module is used for sending the initial resume information of any job seeker to any enterprise and sending recruitment information of any enterprise to any job seeker under the condition that the intention job seeker position of any job seeker comprises at least one position currently to be recruited by any enterprise.
According to a third aspect of the present disclosure, a recruitment information publishing system is provided, including an enterprise management module, a recruitment management module, a complaint management module, a violation management module, and a safety log management module.
The enterprise management module is used for authenticating and auditing any enterprise to be recruited and managing enterprise information of any enterprise and recruitment information to be issued;
the recruitment management module is used for receiving recruitment information to be issued, which is sent by the enterprise management module, matching each target job seeker according to the recruitment information to be issued, and sending the recruitment information of any enterprise to the target job seeker;
the complaint management module is used for acquiring different types of complaint information of each user of the current system, auditing the complaint information to generate an auditing result, and displaying an auditing state of an auditing process to the user, wherein the auditing result comprises complaint reasons, and the auditing state comprises that the auditing is in progress, the auditing is passed and the auditing is failed;
the violation management module is used for determining the type of the violation operation under the condition that the operation of any enterprise is judged to belong to the violation operation, displaying the punishment result corresponding to the type to any enterprise according to the type of the violation operation,
The security log management module is used for recording login process data when any enterprise logs in the system, wherein the login process data comprises login time, login duration, a client address, enterprise identification and login identity, and sending warning information to an administrator when the login process data is detected to be in an abnormal state, and the warning information comprises identification information of any enterprise.
The following beneficial effects can be achieved through the present disclosure:
in the embodiment of the disclosure, first, current recruitment characteristic information of any enterprise is determined, each job seeker matched with the recruitment characteristic information is obtained, then, attention degree characteristic information of each job seeker is obtained based on historical operation data of each job seeker, attention value of each job seeker to various recruitment positions is determined according to the attention degree characteristic information of each job seeker, then, the corresponding intention job position of each job seeker is determined according to the attention value of each job seeker to various recruitment positions, then, at least one position of any enterprise currently required to be recruited is determined according to the current recruitment characteristic information of any enterprise, and then, initial resume information of any job seeker is sent to any enterprise under the condition that the current required job position of any enterprise is contained in the position of any job seeker, and the recruitment information of any enterprise is sent to any job seeker. Therefore, each job seeker meeting recruitment requirements can be obtained according to the current recruitment characteristic information of the enterprise, namely recruitment requirements, secondary screening is conducted, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, initial resume information of the job seekers is sent to the enterprise, so that the enterprise can communicate with the job seekers later, matching degree between the enterprise and the job seekers is improved, and in addition, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, recruitment information of the enterprise can be recommended to each job seeker, so that the job seekers can obtain the enterprise recruitment information more meeting own work requirements. Therefore, the job seeker can be quickly, accurately and efficiently matched with the enterprise, recruitment efficiency of the enterprise and job seeker finding efficiency are improved, the method is accurate and reliable, great convenience is provided for the enterprise and the job seeker, the cost is low, and the method is easy to realize.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a flowchart of an artificial intelligence based recruitment information distribution method provided in accordance with an embodiment of the present disclosure;
fig. 2 is a flowchart of an artificial intelligence based recruitment information delivery method provided in accordance with an embodiment of the present disclosure;
fig. 3 is a block diagram of an artificial intelligence based recruitment information publication device provided in accordance with an embodiment of the present disclosure;
fig. 4 is a block diagram of an artificial intelligence based recruitment information distribution system provided in accordance with an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing an artificial intelligence based recruitment information distribution method in accordance with an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The recruitment information publishing method, device and system based on artificial intelligence according to the embodiment of the disclosure are described below with reference to the accompanying drawings.
The recruitment information issuing method based on the artificial intelligence provided by the disclosure can be executed by the recruitment information issuing device based on the artificial intelligence provided by the disclosure, and also can be executed by the recruitment information issuing system based on the artificial intelligence provided by the disclosure.
Fig. 1 is a flowchart of a recruitment information distribution method based on artificial intelligence provided in accordance with an embodiment of the present disclosure. As shown in fig. 1, the method includes:
step 101, determining current recruitment feature information of any enterprise and each job seeker matched with the recruitment feature information.
The recruitment feature information may include, but is not limited to, post features, school features, academic features, professional features, work experience features, character features, payroll requirements features, job site features, and the like.
More specifically, the post feature may be a post name to be recruited, such as a technical post, an operation post, a sales post, etc., of any one of the enterprises at present, and the school feature may be a family school or a research school that any one of the enterprises needs to be satisfied by a job seeker, such as university a, university B, etc., without limitation. The academic features may be any kind of academic history that the enterprise needs to meet by job seekers, such as special departments, family, study, doctor, but are not limited thereto. Professional characteristics may be professional attributes that any enterprise needs to meet by job seekers, such as automation, electrical engineering and its automation, machinery, and so forth. The working experience feature can be the working years that any enterprise needs to meet by job seekers, such as the due experience, one-year experience, 2-year working experience and the like. The character features may be character features that any enterprise needs to satisfy by job seekers, such as inward, quiet, active, cool, and not limited herein.
It should be noted that, some self-media enterprises generally have certain requirements on characters of job seekers in order to build a personal device. The salary requirement feature may be a salary range that may be provided by any enterprise, such as 2k-20k, but is not limited thereto. The salary requirement feature may be a geographical location where any enterprise needs a job seeker, such as tokyo, beijing, and hainan, which is not limited herein.
Specifically, the recruitment feature information may be demand information pre-entered by the enterprise in the system. Or, the characteristic information obtained by analyzing the recruitment information issued by the current enterprise by the system can be also adopted.
When determining each job seeker matched with the recruitment feature information, the initial resume information of each job seeker on line in the current system can be matched with the recruitment feature information, so that the matching degree between the initial resume information and the recruitment feature information is determined, and under the condition that the matching degree corresponding to any job seeker is greater than a specified threshold value, any job seeker can be determined as the job seeker matched with the enterprise.
The initial resume information generally includes, but is not limited to, a school, an academic, a required post, a required salary, a required work place, and the like, which are pre-recorded by a job seeker. It should be noted that, the initial resume information is a basic personal information actively pre-entered by the job seeker, and the system is obtained by a compliance mode.
Specifically, when the matching degree between the initial resume information and the recruitment feature information is calculated, the cosine similarity between the initial resume information and the recruitment feature information can be calculated, so that the cosine similarity is determined as the matching degree between the initial resume information and the recruitment feature information, and the method is not limited herein, and other methods can be used.
Step 102, acquiring attention characteristic information of each job seeker based on historical operation data of each job seeker, and determining attention values of each job seeker to various recruitment posts according to the attention characteristic information of each job seeker.
The attention characteristic information at least comprises browsing frequency, browsing average duration, post page forwarding frequency, post related topic evaluation frequency and job seeking frequency of various recruitment posts.
Specifically, the system may digitize the browsing frequency, the browsing average time length, the post page forwarding frequency, the post related topic evaluation frequency, and the job hunting frequency of each job seeker on the basis of a preset conversion relationship, so as to determine each parameter value corresponding to each job seeker on each type of job seeker, where each parameter value includes a first parameter value corresponding to the browsing frequency, a second parameter value corresponding to the browsing average time length, a third parameter value corresponding to the post page forwarding frequency, a fourth parameter value corresponding to the post related topic comment frequency, and a fifth parameter value corresponding to the job seeker, and then, the system may calculate the attention value of each job seeker on each type of job seeker according to the first parameter value, the second parameter value, the third parameter value, the fourth parameter value, and the preset weight corresponding to each job seeker.
The job seeker is in direct proportion to the browsing frequency, the browsing average duration, the job page forwarding frequency, the job related topic evaluation frequency, the job seeking frequency and the converted parameter value, that is, the higher the browsing frequency is, the larger the first parameter value is, the higher the browsing average duration is, the larger the second parameter value is, the higher the job page forwarding frequency is, the larger the third parameter value is, the higher the job related topic comment frequency is, the larger the fourth parameter value is, and the higher the job seeking frequency is, and the higher the fifth parameter value is.
The system can respectively digitize the browsing frequency, the browsing average duration, the post page forwarding frequency, the post related topic evaluation frequency and the job seeking frequency of various recruitment posts according to a preset conversion relation, such as based on a preset mathematical model, so that the magnitude of a first parameter value can represent the browsing frequency, and other parameter values are the same.
The weights corresponding to the different parameter values may be the same or different.
For example, the first parameter value of the job seeker Q for any post S is X1, the weight corresponding to the first parameter value is V1, the second parameter value is X2, the weight corresponding to the second parameter value is V2, the third parameter value is X3, the weight corresponding to the third parameter value is V3, the fourth parameter value is X4, the weight corresponding to the fourth parameter value is V4, the fifth parameter value is X5, the weight corresponding to the first parameter value is V5, and the attention value f=x1×v1+x2+v2+x3+v3+for the job seeker Q for any post S can be calculated
X4*V4+X5*V5。
The above examples are merely illustrative, and the present disclosure is not limited thereto.
Therefore, the last calculated attention value can be very accurate and reliable by combining the characteristics of browsing frequency, browsing average duration, post page forwarding frequency, post related topic evaluation frequency, job seeking frequency and the like.
And step 103, determining the corresponding intention job-seeking positions of each job seeker according to the attention value of each job seeker to various recruitment positions.
The intention job-seeking position can be a position with higher intention for job seekers.
Optionally, the system may determine any recruitment as the intended job position corresponding to the job seeker when the attention value corresponding to the any recruitment is greater than the preset threshold.
For example, if the preset threshold is 0.5, the attention value of the job seeker to the job a is 0.3, the attention value to the job B is 0.56, the attention value to the job C is 0.1, and the attention value to the job D is 0.8, the job B and the job D may be regarded as the corresponding intention job seeker.
It should be noted that the foregoing examples are merely illustrative, and the disclosure is not limited thereto.
Step 104, determining at least one post to be recruited currently by any enterprise according to the current recruitment characteristic information of any enterprise.
Specifically, the system can determine the position to be recruited by any enterprise according to the position characteristics in the current recruitment characteristic information of any enterprise.
Step 105, in the case that the intention job-seeking post of any job seeker includes at least one post currently to be recruited by any enterprise, sending initial resume information of any job seeker to any enterprise, and sending recruitment information of any enterprise to any job seeker.
For example, if the current recruited position of any enterprise a is, for example, position 1, position 2, position 3, position 4, and position 5, the intended job position corresponding to the job seeker S is, for example, position 4, position 6, and position 7, that is, the current recruited position of any enterprise includes the intended job position corresponding to the job seeker S, and position 4, the initial resume information of the job seeker S may be sent to any enterprise a, and the recruited information of the enterprise a may be sent to the job seeker S.
The above examples are merely illustrative of the present disclosure, and the present disclosure is not limited thereto.
The initial resume information generally includes, but is not limited to, a school, an academic, a required post, a required salary, a required work place, and the like, which are pre-recorded by a job seeker. It should be noted that, the initial resume information is a basic personal information actively pre-entered by the job seeker, and the system is obtained by a compliance mode.
Optionally, when the device sends recruitment information of any enterprise to any job seeker, enterprise change information, history violation processing record data of any enterprise and release time of the recruitment information released by any enterprise can be acquired first, then a risk prompt level corresponding to any enterprise is determined according to the history violation processing record data, and then the risk prompt level corresponding to any enterprise, the release time of the recruitment information released, the enterprise change information and the recruitment information are sent to any job seeker.
The enterprise change information may include an enterprise name change, an industry change, an operation range change, and the like. It should be noted that, the system can obtain the enterprise change information and the history violation processing record data of any enterprise according to the history operation data, the history punishment data and the history warning data of any enterprise. For example, the history violation processing record data may be a date A-violation operation B-penalty C, which is not limited herein.
The system can determine the risk prompt level corresponding to any enterprise according to the history violation processing record data, for example, the risk prompt level corresponding to any enterprise can be determined according to the alerted level and the penalized level contained in the punishment result of any enterprise. The risk prompt level can be no risk, low risk, medium risk and high risk.
It should be noted that, when the system sends the risk prompt level corresponding to any enterprise to the job seeker, the job seeker can timely prevent the enterprise, and damage to benefits or other risks is avoided.
By sending the release time of release recruitment information, the enterprise change information and the recruitment information corresponding to any enterprise to the job seeker, the job seeker can timely obtain the current recruitment condition of the enterprise, and the sensitivity of the job seeker to the enterprise can be improved based on the release time of release recruitment information, so that a job seeker can timely make a job seeker strategy. Through sending the enterprise change information corresponding to any enterprise to the job seeker, the job seeker can objectively survey and understand the enterprise, so that the job seeker can better deal with posts and the enterprise. For example, if any enterprise is converted from the education industry to the science and technology industry, the staff with the education background can learn own advantages.
In the embodiment of the disclosure, first, current recruitment characteristic information of any enterprise is determined, each job seeker matched with the recruitment characteristic information is obtained, then, attention degree characteristic information of each job seeker is obtained based on historical operation data of each job seeker, attention value of each job seeker to various recruitment positions is determined according to the attention degree characteristic information of each job seeker, then, the corresponding intention job position of each job seeker is determined according to the attention value of each job seeker to various recruitment positions, then, at least one position of any enterprise currently required to be recruited is determined according to the current recruitment characteristic information of any enterprise, and then, initial resume information of any job seeker is sent to any enterprise under the condition that the current required job position of any enterprise is contained in the position of any job seeker, and the recruitment information of any enterprise is sent to any job seeker. Therefore, each job seeker meeting recruitment requirements can be obtained according to the current recruitment characteristic information of the enterprise, namely recruitment requirements, secondary screening is conducted, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, initial resume information of the job seekers is sent to the enterprise, so that the enterprise can communicate with the job seekers later, matching degree between the enterprise and the job seekers is improved, and in addition, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, recruitment information of the enterprise can be recommended to each job seeker, so that the job seekers can obtain the enterprise recruitment information more meeting own work requirements. Therefore, the job seeker can be quickly, accurately and efficiently matched with the enterprise, recruitment efficiency of the enterprise and job seeker finding efficiency are improved, the method is accurate and reliable, great convenience is provided for the enterprise and the job seeker, the cost is low, and the method is easy to realize.
Fig. 2 is a flowchart of an artificial intelligence based recruitment information delivery method provided in accordance with an embodiment of the present disclosure;
as shown in fig. 2, the method includes:
step 201, in response to determining that any enterprise passes the audit certification, monitoring a recruitment status of the any enterprise, so as to record recruitment posts and attribute information of the recruitment posts contained in the recruitment information issued by the any enterprise in real time.
The enterprise application needs authentication verification of an administrator to pass. There are two types of enterprise authentication states. One is that in the application, the other is that pass, if the authentication state is in the application, the administrator can click to enter the audit to check the authentication information, if pass, the audit result is clicked to pass, then the authentication information is submitted, if not pass, the audit result is clicked to fail, and the information can be written on the audit result by remarking the information. If the verification authentication state is passed, the administrator can click to check the verification, and the authentication information of the enterprise can be checked. The administrator may also jump to the business details page by clicking on the business name. Specifically, under the condition that an enterprise passes the audit certification, the system can monitor the recruitment state of any enterprise, so as to record the recruitment posts contained in the recruitment information issued by any enterprise and the attribute information of the recruitment posts in real time, wherein the attribute information can be post type, professional type, working life, salary and work place, and thus, the record can be carried out when any attribute information changes.
Step 202, responding to a recruitment information issuing instruction, and determining the recruitment information to be issued currently according to the attribute information of the recruitment position contained in the issuing instruction and the recruitment position and the attribute information of the recruitment position contained in the historic issuing recruitment information of any enterprise in the historic record.
If the historical release recruitment information of any enterprise in the historical record contains the current recruitment position and the attribute information of the current recruitment position, the position contained in the recruitment information release instruction is the historical release, that is, the enterprise repeatedly releases the recruitment information, and the unpublished recruitment information serving as the current recruitment information to be released can be stated at the moment.
For example, if the recruitment posts included in the issuing instruction are a, B, and C, where a, B are the recruitment posts issued in the history, and the attribute information corresponding to a, B is the same as the attribute information issued in the history, the recruitment post C may be used as the recruitment information to be issued, so as to avoid repeated issuing.
And step 203, determining recruitment characteristic information corresponding to the recruitment information to be issued.
Specifically, the feature corresponding to the recruitment information to be issued may be used as the recruitment feature information.
Step 204, determining each job seeker that matches the recruitment feature information.
Step 205, based on the historical operation data of each job seeker, attention characteristic information of each job seeker is obtained, and attention values of each job seeker to various recruitment posts are determined according to the attention characteristic information of each job seeker.
And 206, determining the intention job-seeking position corresponding to each job seeker according to the attention value of each job seeker to various recruitment positions.
Step 207, determining at least one post currently to be recruited by any one of the enterprises according to the current recruitment feature information of any one of the enterprises.
Step 208, in the case that the intention job-seeking post of any job seeker includes at least one post currently to be recruited by any enterprise, sending the initial resume information of any job seeker to any enterprise, and sending the recruitment information of any enterprise to any job seeker.
It should be noted that, the specific implementation manner of the steps 204 to 208 may refer to the above embodiment, and will not be described herein.
In the embodiment of the disclosure, firstly, monitoring the recruitment state of any enterprise in response to the fact that any enterprise is confirmed to pass through audit certification, recording recruitment information contained in recruitment information issued by the any enterprise and attribute information of the recruitment in real time, then, responding to receiving an recruitment information issuing instruction, according to the attribute information of the recruitment contained in the issuing instruction, and historical information of the recruitment contained in the recruitment information issued by the historical record, determining the recruitment information issued by the current enterprise, then, determining recruitment feature information corresponding to the recruitment information issued by the current enterprise, then, determining each job applicant matched with the recruitment feature information, acquiring attention feature information of each job applicant based on historical operation data of each job applicant, determining attention feature information of each job applicant to each job applicant, and sending at least one job applicant value to each job applicant, and then, determining the current job information to each job applicant, and finally, determining the current job information to each job applicant according to the at least one job applicant, and the current job information is sent to each job applicant. Therefore, the enterprise can be authenticated and audited, the recruitment information can be issued under the condition that the audit is passed, and the recruitment information is screened, so that repeated transmission of the recruitment information is avoided, only the recruitment information which is not transmitted is transmitted, and the recruitment characteristic information is more accurate and reliable.
Fig. 3 is a block diagram of an artificial intelligence based recruitment information distribution system provided in accordance with an embodiment of the present disclosure.
As shown in fig. 3, the recruitment information distribution system 300 based on artificial intelligence includes an enterprise management module 310, a recruitment management module 320, a complaint management module 330, a violation management module 340, and a safety log management module 350.
The enterprise management module 310 is configured to perform authentication and audit on any enterprise to be recruited, and manage enterprise information of the any enterprise and recruitment information to be published;
the enterprise information includes, among other things, academic requirements, gender requirements, recruitment status, salary requirements, and the like.
The recruitment information to be released can be the recruitment to be released, the position requirement of the recruitment, salary and the like.
It should be noted that, after any enterprise registers the enterprise information with the system, the system may automatically audit the enterprise information, such as comparing whether it is true, whether it contains a sensitive word, whether it is null data, etc., and then notify the administrator to make a second audit.
The recruitment management module 320 is configured to receive recruitment information to be issued sent by the enterprise management module, match each target job seeker according to the recruitment information to be issued, and send the recruitment information of any enterprise to the target job seeker;
The complaint management module 330 is configured to obtain different types of complaint information of each user of the current system, and audit the complaint information to generate an audit result, and display an audit state of an audit process to the user, where the audit result includes a complaint reason, and the audit state includes an audit in-process, an audit passed, and an audit failed;
the violation management module 340 is configured to determine a type of the violation operation if it is determined that the operation of the any enterprise belongs to the violation operation, and display a penalty result corresponding to the type to the any enterprise according to the type of the violation operation.
It should be noted that, the violation management module 340 may also be configured to determine whether the operation of any employee belongs to a violation operation. The penalty results include warnings and stops.
Optionally, the system further comprises a dictionary management module, which is used for adding a dictionary, deleting a dictionary cache, regrouping, editing the dictionary, etc., clicking the add grouping to pop up the add grouping page, then writing grouping information, and then obtaining a group, editing and deleting the group, and when managing a certain dictionary, clicking the dictionary to be managed, performing the operation functions of adding the dictionary value, adding in batches, sorting, editing the dictionary, deleting the dictionary, etc.
The security log management module 350 is configured to record login process data when any enterprise logs in to the system, where the login process data includes login time, login duration, client address, enterprise identifier, and login identity, and send warning information to an administrator when it is detected that the login process data is in an abnormal state, where the warning information includes identification information of any enterprise.
It should be noted that, the comprehensive page corresponding to the security log management module can see information such as who is, which unit, operation time, client address, service module, operation type, operation details of the access terminal, etc. in detail, if the information is to be quickly found, search conditions can be input on the information, so that an administrator can be helped to quickly find the information.
Through the embodiment, the enterprise user and the job seeker can be better served, the user with more target intention is selected for the enterprise, recruitment efficiency of the enterprise and job seeker finding efficiency are improved, the method is accurate and reliable, great convenience is provided for the enterprise and the job seeker, the cost is low, the method is easy to realize, complaint information can be timely processed, adverse effects of the illegal enterprise on the job seeker are avoided, warning and punishment are timely carried out on the illegal enterprise, and accuracy and usability of various data are guaranteed.
Fig. 4 is a schematic structural diagram of an artificial intelligence based recruitment information presenting apparatus provided in accordance with an embodiment of the present disclosure. As shown in figure 4 of the drawings,
the artificial intelligence based recruitment information publication device 400 may include a first determination module 410, an acquisition module 420, a second determination module 430, a third determination module 440, and a transmission module 450, wherein,
a first determining module 410, configured to determine current recruitment feature information of any one enterprise, and each job seeker matched with the recruitment feature information;
the obtaining module 420 is configured to obtain attention characteristic information of each job seeker based on historical operation data of each job seeker, and determine an attention value of each job seeker for each recruitment post according to the attention characteristic information of each job seeker;
a second determining module 430, configured to determine, according to the attention value of each job seeker for each recruitment post, an intention job seeker corresponding to each job seeker;
a third determining module 440, configured to determine at least one post currently to be recruited by the any one enterprise according to the current recruitment feature information of the any one enterprise;
and the sending module 450 is configured to send the initial resume information of any job seeker to any one of the enterprises and send recruitment information of any one of the enterprises to any one of the job seekers when the intended job seeker position of the any one of the job seekers includes at least one position currently to be recruited by the any one of the enterprises.
Optionally, the attention feature information at least comprises browsing frequency, browsing average duration, post page forwarding frequency, post related topic evaluation frequency and job hunting frequency of various recruitment posts;
the acquisition module is specifically configured to:
based on a preset conversion relation, the browsing frequency of each job seeker on each recruitment post, the browsing average duration, the post page forwarding frequency, the post related topic comment frequency and the job seeking frequency are digitized to determine each parameter value corresponding to each recruitment post by each job seeker,
the parameter values comprise a first parameter value corresponding to the browsing frequency, a second parameter value corresponding to the browsing average duration, a third parameter value corresponding to the post page forwarding frequency, a fourth parameter value corresponding to the post related topic comment frequency and a fifth parameter value corresponding to the job hunting frequency;
and calculating the attention value of each job seeker to various recruitment posts according to the first parameter value, the second parameter value, the third parameter value, the fourth parameter value, the fifth parameter value and the preset weight corresponding to each job seeker.
Optionally, the sending module is specifically configured to:
acquiring enterprise change information of any enterprise, history violation processing record data and release time of recruitment information released by any enterprise;
determining a risk prompt level corresponding to any enterprise according to the history violation processing record data;
and sending the risk prompt level corresponding to any enterprise, the release time of the release recruitment information, the enterprise change information and the recruitment information to any job seeker.
Optionally, the third determining module is specifically configured to:
and when the attention value corresponding to any recruitment is larger than a preset threshold, determining the any recruitment as the intention job-seeking position corresponding to the job seeker.
Optionally, the first determining module is specifically configured to:
monitoring recruitment status of any enterprise in response to determining that any enterprise passes the audit certification, so as to record recruitment posts and attribute information of the recruitment posts contained in the recruitment information issued by any enterprise in real time;
responding to a recruitment information issuing instruction, and determining the recruitment information to be issued currently according to the attribute information of the recruitment position contained in the issuing instruction and the attribute information of the recruitment position and the recruitment position contained in the historic issuing recruitment information of any enterprise in a historic record;
And determining recruitment characteristic information corresponding to the recruitment information to be published.
In the embodiment of the disclosure, first, current recruitment characteristic information of any enterprise is determined, each job seeker matched with the recruitment characteristic information is obtained, then, attention degree characteristic information of each job seeker is obtained based on historical operation data of each job seeker, attention value of each job seeker to various recruitment positions is determined according to the attention degree characteristic information of each job seeker, then, the corresponding intention job position of each job seeker is determined according to the attention value of each job seeker to various recruitment positions, then, at least one position of any enterprise currently required to be recruited is determined according to the current recruitment characteristic information of any enterprise, and then, initial resume information of any job seeker is sent to any enterprise under the condition that the current required job position of any enterprise is contained in the position of any job seeker, and the recruitment information of any enterprise is sent to any job seeker. Therefore, each job seeker meeting recruitment requirements can be obtained according to the current recruitment characteristic information of the enterprise, namely recruitment requirements, secondary screening is conducted, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, initial resume information of the job seekers is sent to the enterprise, so that the enterprise can communicate with the job seekers later, matching degree between the enterprise and the job seekers is improved, and in addition, when the posts provided by the current enterprise contain the intention job seekers of the job seekers, recruitment information of the enterprise can be recommended to each job seeker, so that the job seekers can obtain the enterprise recruitment information more meeting own work requirements. Therefore, the job seeker can be quickly, accurately and efficiently matched with the enterprise, recruitment efficiency of the enterprise and job seeker finding efficiency are improved, the method is accurate and reliable, great convenience is provided for the enterprise and the job seeker, the cost is low, and the method is easy to realize.
Fig. 5 illustrates a schematic block diagram of an example electronic device 500 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 includes a computing unit 501 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the artificial intelligence-based recruitment information distribution method. For example, in some embodiments, the artificial intelligence based recruitment information distribution method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more of the steps of the artificial intelligence based recruitment information distribution method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the artificial intelligence based recruitment information distribution method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (6)
1. The recruitment information issuing method based on the artificial intelligence is characterized by comprising the following steps of:
determining current recruitment characteristic information of any enterprise and each job seeker matched with the recruitment characteristic information;
acquiring attention characteristic information of each job seeker based on historical operation data of each job seeker, and determining attention values of each job seeker to various recruitment posts according to the attention characteristic information of each job seeker;
Determining the corresponding intention job-seeking positions of each job seeker according to the attention value of each job seeker to each recruitment position;
determining at least one post to be recruited currently by any enterprise according to the current recruitment characteristic information of the any enterprise;
under the condition that the intention job-seeking post of any job seeker comprises at least one post currently to be recruited by any enterprise, sending initial resume information of the any job seeker to the any enterprise, and sending recruitment information of the any enterprise to the any job seeker;
the attention feature information at least comprises browsing frequency, browsing average duration, post page forwarding frequency, post related topic evaluation frequency and job hunting frequency of various recruitment posts;
the step of determining the attention value of each job seeker to various recruitment posts according to the attention feature information of each job seeker comprises the following steps:
based on a preset conversion relation, the browsing frequency of each job seeker on each recruitment post, the browsing average duration, the post page forwarding frequency, the post related topic comment frequency and the job seeking frequency are digitized to determine each parameter value corresponding to each recruitment post by each job seeker,
The parameter values comprise a first parameter value corresponding to the browsing frequency, a second parameter value corresponding to the browsing average duration, a third parameter value corresponding to the post page forwarding frequency, a fourth parameter value corresponding to the post related topic comment frequency and a fifth parameter value corresponding to the job hunting frequency;
calculating the attention value of each job seeker to various recruitment posts according to the first parameter value, the second parameter value, the third parameter value, the fourth parameter value, the fifth parameter value and the preset weight corresponding to each job seeker;
the sending recruitment information of the any enterprise to the any job seeker further includes:
acquiring enterprise change information of any enterprise, history violation processing record data and release time of recruitment information released by any enterprise;
determining a risk prompt level corresponding to any enterprise according to the history violation processing record data;
and sending the risk prompt level corresponding to any enterprise, the release time of the release recruitment information, the enterprise change information and the recruitment information to any job seeker.
2. The method of claim 1, wherein the determining the intended job position corresponding to each job seeker based on the magnitude of the attention value of each job seeker to the various recruitment positions comprises:
and when the attention value corresponding to any recruitment is larger than a preset threshold, determining the any recruitment as the intention job-seeking position corresponding to the job seeker.
3. The method of claim 1, wherein the determining the current recruitment characteristic information for any one of the enterprises further comprises:
monitoring recruitment status of any enterprise in response to determining that any enterprise passes the audit certification, so as to record recruitment posts and attribute information of the recruitment posts contained in the recruitment information issued by any enterprise in real time;
responding to a recruitment information issuing instruction, and determining the recruitment information to be issued currently according to the attribute information of the recruitment position contained in the issuing instruction and the attribute information of the recruitment position and the recruitment position contained in the historic issuing recruitment information of any enterprise in a historic record;
and determining recruitment characteristic information corresponding to the recruitment information to be published.
4. Recruitment information issuing device based on artificial intelligence, characterized by comprising:
the first determining module is used for determining the current recruitment characteristic information of any enterprise and each job seeker matched with the recruitment characteristic information;
the acquisition module is used for acquiring the attention characteristic information of each job seeker based on the historical operation data of each job seeker, and determining the attention value of each job seeker to various recruitment posts according to the attention characteristic information of each job seeker;
the second determining module is used for determining the corresponding intention job-seeking position of each job seeker according to the attention value of each job seeker to each recruitment position;
the third determining module is used for determining at least one post to be recruited currently by any enterprise according to the current recruitment characteristic information of the any enterprise;
the sending module is used for sending the initial resume information of any job seeker to any enterprise and sending recruitment information of any enterprise to any job seeker when the intention job seeker position of any job seeker comprises at least one position currently to be recruited by any enterprise;
The attention feature information at least comprises browsing frequency, browsing average duration, post page forwarding frequency, post related topic evaluation frequency and job hunting frequency of various recruitment posts;
the acquisition module is specifically configured to:
based on a preset conversion relation, the browsing frequency of each job seeker on each recruitment post, the browsing average duration, the post page forwarding frequency, the post related topic comment frequency and the job seeking frequency are digitized to determine each parameter value corresponding to each recruitment post by each job seeker,
the parameter values comprise a first parameter value corresponding to the browsing frequency, a second parameter value corresponding to the browsing average duration, a third parameter value corresponding to the post page forwarding frequency, a fourth parameter value corresponding to the post related topic comment frequency and a fifth parameter value corresponding to the job hunting frequency;
calculating the attention value of each job seeker to various recruitment posts according to the first parameter value, the second parameter value, the third parameter value, the fourth parameter value, the fifth parameter value and the preset weight corresponding to each job seeker;
The sending module is specifically configured to:
acquiring enterprise change information of any enterprise, history violation processing record data and release time of recruitment information released by any enterprise;
determining a risk prompt level corresponding to any enterprise according to the history violation processing record data;
and sending the risk prompt level corresponding to any enterprise, the release time of the release recruitment information, the enterprise change information and the recruitment information to any job seeker.
5. The apparatus according to claim 4, wherein the third determining module is specifically configured to:
and when the attention value corresponding to any recruitment is larger than a preset threshold, determining the any recruitment as the intention job-seeking position corresponding to the job seeker.
6. The recruitment information issuing system based on artificial intelligence is characterized in that the recruitment information issuing method according to claim 1 is applied, the recruitment information issuing system comprises an enterprise management module, a recruitment management module, a complaint management module, a violation management module and a safety log management module, wherein,
the enterprise management module is used for authenticating and auditing any enterprise to be recruited and managing enterprise information of any enterprise and recruitment information to be issued;
The recruitment management module is used for receiving recruitment information to be issued, which is sent by the enterprise management module, matching each target job seeker according to the recruitment information to be issued, and sending the recruitment information of any enterprise to the target job seeker;
the complaint management module is used for acquiring different types of complaint information of each user of the current system, auditing the complaint information to generate an auditing result, and displaying an auditing state of an auditing process to the user, wherein the auditing result comprises complaint reasons, and the auditing state comprises that the auditing is in progress, the auditing is passed and the auditing is failed;
the violation management module is used for determining the type of the violation operation under the condition that the operation of any enterprise is judged to belong to the violation operation, and displaying a punishment result corresponding to the type to any enterprise according to the type of the violation operation;
the security log management module is used for recording login process data when any enterprise logs in the system, wherein the login process data comprises login time, login duration, a client address, enterprise identification and login identity, and sending warning information to an administrator when the login process data is detected to be in an abnormal state, and the warning information comprises identification information of any enterprise.
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