CN117094691A - Human resource management method based on big data platform - Google Patents
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Abstract
The invention discloses a human resource management method based on a big data platform, which comprises the steps of establishing a human resource service database, inputting resume information and preference information of job seekers, setting sensitive labels, matching the level of the resume, the working years, the gender and the profession according to the sensitive labels, establishing a human resource assessment model, and scoring the resume of the job seekers; loading all the resumes meeting the enterprise demands into a primary screening resume library according to the scoring result of the resume, and screening out high-quality resume and inferior resume in the primary screening resume library; the enterprise HR further screens job seekers from the quality resume for interviews. According to the invention, through setting the resume screening standard with multiple layers and multiple parameters, the resume screening precision is high, and the working intensity of enterprises on human resource management and talent recruitment is reduced.
Description
Technical Field
The invention relates to the field of human resource management, in particular to a human resource management method based on a big data platform.
Background
Human resource management refers to a series of human resource policies of an enterprise and corresponding management activities. The activities mainly comprise establishment of enterprise manpower resource strategy, recruitment and selection of staff, training and development, performance management, salary management, staff flow management, staff relationship management, staff safety and health management and the like. The enterprise uses modern management method to carry out a series of activities such as planning, organizing, commanding, controlling and coordinating on the aspects of human resource acquisition (selection), development (breeding), maintenance (retention), utilization (utilization), and the like, and finally achieves a management behavior for realizing the development target of the enterprise.
For modern emerging large enterprises, talent recruitment and selection are key in determining the development direction and development basis of the enterprises, along with the development of internet big data, more and more staff acquire position information through a network recruitment platform, and for the enterprises, the acquisition of high-quality talents through the network recruitment platform is also key, and for massive talents on the network recruitment platform, how to efficiently screen the high-quality resume matched with the enterprises is key, and the difficulty of the massive resume on the network to automatically screen through the enterprise HR is definitely large, and the browsing amount of the enterprise HR is limited, so that the acquisition of the high-quality resume is also limited, thereby preventing the recruitment and selection of the enterprises to the excellent talents. Therefore, a human resource management method based on a big data platform is needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a human resource management method based on a big data platform, which is used for accommodating and screening a large number of job seeker resume based on an established human resource service database, and screening resume with high matching degree and high quality with enterprise talent demands based on dual screening of resume information and preference information.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the human resource management method based on the big data platform comprises the following steps:
s1: establishing a human resource service database, and inputting resume information and preference information of job seekers, wherein the resume information comprises an school grade, a working year, gender and profession;
s2: setting a sensitive label related to the academic level, a sensitive label of the working life, a sensitive label of the sex and a professional sensitive label;
s3: the method comprises the steps of respectively matching a sensitive label of an academic grade, a sensitive label of a working year, a sensitive label of gender and a sensitive label of a professional with the academic grade, the working year, the gender and the professional filled in a resume of a job seeker, establishing an evaluation model of human resources, and scoring the resume of the job seeker;
s4: loading all the resumes meeting the enterprise demands into a primary screening resume library according to the scoring result of the resume, and screening out high-quality resume and inferior resume in the primary screening resume library by utilizing preference information on job seekers resume;
s5: packaging the screened high-quality resume, and forming mail information or short message information to be sent to a mailbox of an enterprise HR or sending a short message to the enterprise HR; the enterprise HR further screens job seekers from the quality resume for interviews.
Further, step S3 includes:
s31: matching the academic information filled by the job seeker by using the sensitive label of the academic level, and scoring the academy of the job seeker, wherein the method specifically comprises the following steps of:
the sensitive labels of the academic grades are phrases related to the academic, each phrase is divided into single characters by taking each phrase as a unit, the single characters comprise [ high, medium ], [ special, family ], [ book, family ], [ major, doctor ] and [ doctor ], the academic information filled in the resume of the job seeker is matched with the sensitive labels of each academic grade, and the academic grade of the job seeker is scored according to the matching degree;
scoring criteria for the academic scale were: [ high, medium ]]=(a) [ Special, department of]=(a+1), [ book, department]=(a+2), [ Shuoshi]=(a+3) and [ doctor ]]=(a+4)；
Dividing the academic information filled in the resume of the job seeker into single characters, and sequentially matching all the characters related to the academic information with the characters of the sensitive label of each academic level by utilizing the characters related to the academic information, if the academic isThe character related to the information is matched with the character of the sensitive label of one of the academic grades by two or more characters, and then the score corresponding to the academic grade is outputn；
S32: matching the sensitive label of the working years with the working years information filled in the resume of the job seeker to score the working years of the job seeker;
the sensitive label of the working period is the range of the working period: [0, 1) year, [1, 3) year, [3, 5) year, [5, + -infinity) year;
scoring criteria for different operational age ranges are: [0, 1) year ]aYear =1, 3 =a+1, [3, 5) year ]a+2, [5 ], ++ infinity) year =a+3；
Comparing the working years filled in on the job seeker resume with the range of each working year, outputting the score of the corresponding working year range as the score of the working year according to the working year range in which the working year of the job seeker fallsm；
S33: marking the sex of job seekers by using a sex sensitive tag, wherein the sex sensitive tag is the requirement of a target working position required by an enterprise on the sex; if the gender of the job seeker meets the gender requirement of the target working position, the gender of the job seeker is scoredu=aIf the gender of the job seeker does not meet the gender requirement of the target working post, the gender of the job seeker is scoredu=(a-1)；
S34: matching professional information filled in the job seeker resume by using the professional sensitive tag, and scoring the profession of the job seeker according to the matching degree;
setting a sensitive label of the job seeker specialty in the screening resume, wherein the sensitive label comprises a plurality of groups of special characteristics required by the target working post to form a professional label group related to the specialty:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,sis the number of the phrase;
splitting the professional names filled in the resume of the job seeker intovEach phrase is respectively associated withEach tag in the professional tag group is matched and recordedvNumber of words that can be matched with tags in a professional tag groupwCalculating the matching degree of the profession of job seekers and the profession required by the target working post：/>；
Scoring the profession of the job seeker according to the matching degree:
if it isProfessional scoring of job seekersy=a；
If it isProfessional scoring of job seekersy=a+1；
If it isProfessional scoring of job seekersy=a+2；
If it isProfessional scoring of job seekersy=a+3；
If it isProfessional scoring of job seekersy=a+4；
If it isProfessional scoring of job seekersy=a+5；
S35: and establishing an evaluation model of the human resources, and scoring the resume of the job seeker according to the evaluation model.
Further, the evaluation model is:
；
wherein,x 1 、x 2 、x 3 、x 4 impact weights respectively scoring the resume of the job seeker for the school grade, the working year, the sex and the specialty,Ethe score of the resume is given to the job seeker.
Further, step S4 includes:
s41: scoring threshold defining enterprise screening job seeker profilesE Threshold value The method comprises the steps of carrying out a first treatment on the surface of the If the resume of the job seeker makes a scoreE≥E Threshold value Judging that the resume of the job seeker meets the requirements of the enterprise, and sending the resume of the job seeker to a primary screening resume library of the enterprise; if the resume of the job seeker makes a scoreE＜E Threshold value Judging that the resume of the job seeker does not meet the requirements of enterprises, and leaving the resume of the job seeker in a human resource service database;
s42: traversing each resume in the human resource service database, and counting whether the number of the resume in the primary screening resume library meets the requirement; if yes, stopping the enterprise from screening the resume from the human resource service database, and entering step S43; otherwise, continuing to screen job seeker resume meeting enterprise requirements from the human resource service database;
s43: extracting each resume in the initial-screening resume library, and acquiring preference information on each resume;
setting a preference information label with positive influence on a target working position, wherein the positive influence preference information label comprises a label vocabulary which can be matched with the positive preference information to form a positive preference label group，eThe number of forward labels;
setting a taste information label with negative influence on a target working position, wherein the taste information label with negative influence comprises a label vocabulary matched with the negative taste information to form a negative taste label group，/>Number of negative labels;
s44: splitting each preference information on each resume into a plurality of phrases, respectively matching the phrases with positive labels in the positive preference label group and negative labels in the negative preference label group, and recording the number of the phrases which can be matched with the positive labelsi 1 Number of matches with negative labels in several phrasesi 2 ；
S45: number of passesi 1 And quantity ofi 2 Determining whether the preference information is a positive preference or a negative preference:
if it isi 1 =0 andi 2 =0, then determine the preference information as a forward preference;
if it isi 1 Not equal to 0 andi 2 not equal to 0, then the number of comparisonsi 1 And quantity ofi 2 Is of the size of (2):
if it isi 1 ≥i 2 The preference information is a forward preference;
if it isi 1 ＜i 2 The preference information is a negative preference;
s46: traversing each preference information on the resumes in the preliminary screening resume library, dividing each preference information into positive preference or negative preference, and counting the number of the positive preference and the negative preference on the resumeI 1 AndI 2 and compare the numberI 1 Sum and quantityI 2 Is of the size of (2):
if it isI 1 ≥I 2 The resume in the initial-screening resume library is a high-quality resume, ifI 1 ＜I 2 And the resume in the preliminary screening resume library is an inferior resume, and the inferior resume is returned to the human resource service database.
The beneficial effects of the invention are as follows: according to the invention, the resume information of a huge amount of job seekers is recorded through the established human resource service database, the resume with high matching degree with the demands of the enterprise is screened through the resume information on each job seeker resume, the initial resume library is formed, the high-quality resume and the inferior resume in the initial resume library are further screened in an auxiliary mode through preference information, the enterprise HR only needs to interview the evaluator corresponding to the high-quality resume, the workload of the enterprise HR is greatly reduced, a multi-level and multi-parameter resume screening standard is set, the resume screening precision is high, and the working intensity of the enterprise on human resource management and talent recruitment is reduced.
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Fig. 1 is a flowchart of a human resource management method based on a big data platform.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the human resource management method based on the big data platform in the present solution includes the following steps:
s1: establishing a human resource service database, and inputting resume information and preference information of job seekers, wherein the resume information comprises an school grade, a working year, gender and profession;
s2: setting a sensitive label related to the academic level, a sensitive label of the working life, a sensitive label of the sex and a professional sensitive label;
s3: the method comprises the steps of respectively matching a sensitive label of an academic grade, a sensitive label of a working year, a sensitive label of gender and a sensitive label of a professional with the academic grade, the working year, the gender and the professional filled in a resume of a job seeker, establishing an evaluation model of human resources, and scoring the resume of the job seeker;
the step S3 comprises the following steps:
s31: matching the academic information filled by the job seeker by using the sensitive label of the academic level, and scoring the academy of the job seeker, wherein the method specifically comprises the following steps of:
the sensitive labels of the academic grades are phrases related to the academic, each phrase is divided into single characters by taking each phrase as a unit, the single characters comprise [ high, medium ], [ special, family ], [ book, family ], [ major, doctor ] and [ doctor ], the academic information filled in the resume of the job seeker is matched with the sensitive labels of each academic grade, and the academic grade of the job seeker is scored according to the matching degree;
scoring criteria for the academic scale were: [ high, medium ]]=(a) [ Special, department of]=(a+1), [ book, department]=(a+2), [ Shuoshi]=(a+3) and [ doctor ]]=(a+4)；
Dividing the academic information filled in the resume of the job seeker into single characters, sequentially matching all the characters related to the academic information with the characters of the sensitive label of each academic level, and outputting the score corresponding to one academic level if the characters related to the academic information are matched with the characters of the sensitive label of one academic level by two or more than two charactersn；
For example, if the academic information filled in the resume of a job seeker is "college family" and the academic information is split into individual characters including [ university, academic, book, department, academic, calendar ]]Matching with the character of the sensitive label of academic grade can be just matched with the character [ book, family ]]On matching, the score corresponding to the academic graden=a+2；
S32: matching the sensitive label of the working years with the working years information filled in the resume of the job seeker to score the working years of the job seeker;
the sensitive label of the working period is the range of the working period: [0, 1) year, [1, 3) year, [3, 5) year, [5, + -infinity) year;
scoring criteria for different operational age ranges are: [0, 1) year ]aYear =1, 3 =a+1, [3, 5) year ]a+2, [5 ], ++ infinity) year =a+3；
Comparing the working years filled in the job seeker resume with the range of each working year, and outputting the pair according to the working year range in which the working years of the job seeker fallScore of working period range as score of working periodm；
For example, if the working years filled in on the resume of a job seeker is 4 years, the job will fall into the range of the working years [3,5 ] and the score will be outputm=a+2；
S33: marking the sex of job seekers by using a sex sensitive tag, wherein the sex sensitive tag is the requirement of a target working position required by an enterprise on the sex; if the gender of the job seeker meets the gender requirement of the target working position, the gender of the job seeker is scoredu=aIf the gender of the job seeker does not meet the gender requirement of the target working post, the gender of the job seeker is scoredu=(a-1)；
S34: matching professional information filled in the job seeker resume by using the professional sensitive tag, and scoring the profession of the job seeker according to the matching degree;
setting a sensitive label of the job seeker specialty in the screening resume, wherein the sensitive label comprises a plurality of groups of special characteristics required by the target working post to form a professional label group related to the specialty:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,sis the number of the phrase;
splitting the professional names filled in the resume of the job seeker intovEach phrase is matched with each label in the professional label group and recordedvNumber of words that can be matched with tags in a professional tag groupwCalculating the matching degree of the profession of job seekers and the profession required by the target working post：/>；
For example, a professional name written on the resume by a job seeker is "mechanical design and manufacture and automation thereof", and the detachable phrase is "mechanical", "design", "manufacture", "self-helpMobilizing, adding four phrases, if matching with two professional labels in the professional label group, matching degree；
Scoring the profession of the job seeker according to the matching degree:
if it isProfessional scoring of job seekersy=a；
If it isProfessional scoring of job seekersy=a+1；
If it isProfessional scoring of job seekersy=a+2；
If it isProfessional scoring of job seekersy=a+3；
If it isProfessional scoring of job seekersy=a+4；
If it isProfessional scoring of job seekersy=a+5；
S35: and establishing an evaluation model of the human resources, and scoring the resume of the job seeker according to the evaluation model.
The evaluation model is:
；
wherein,x 1 、x 2 、x 3 、x 4 respectively for academic grade, working years, sex and professional job huntingThe impact weight of the personnel's resume scoring,Ethe score of the resume is given to the job seeker.
For the academic grade, the working life and the specialty filled in on the resume of the job seeker, the calculated academic grade score, the working life score and the specialty score can help to promote the score of the resume only by exceeding half of the highest score, the score exceeds half of the highest score, the academic, the working life or the specialty of the job seeker can be considered to be relatively excellent, the score can be increased for the resume, and if the score does not exceed half of the highest score, the academic, the working life or the specialty of the job seeker is determined to be relatively inferior, and the score needs to be reduced.
S4: loading all the resumes meeting the enterprise demands into a primary screening resume library according to the scoring result of the resume, and screening out high-quality resume and inferior resume in the primary screening resume library by utilizing preference information on job seekers resume;
the step S4 includes:
s41: scoring threshold defining enterprise screening job seeker profilesE Threshold value The method comprises the steps of carrying out a first treatment on the surface of the If the resume of the job seeker makes a scoreE≥E Threshold value Judging that the resume of the job seeker meets the requirements of the enterprise, and sending the resume of the job seeker to a primary screening resume library of the enterprise; if the resume of the job seeker makes a scoreE＜E Threshold value Judging that the resume of the job seeker does not meet the requirements of enterprises, and leaving the resume of the job seeker in a human resource service database;
s42: traversing each resume in the human resource service database, and counting whether the number of the resume in the primary screening resume library meets the requirement; if yes, stopping the enterprise from screening the resume from the human resource service database, and entering step S43; otherwise, continuing to screen job seeker resume meeting enterprise requirements from the human resource service database;
s43: extracting each resume in the initial-screening resume library, and acquiring preference information on each resume;
setting a preference information label with positive influence on a target working position, wherein the positive influence preference information label comprises a positive preference information labelThe matched tag words form a forward hobby tag group，eThe number of forward labels;
setting a taste information label with negative influence on a target working position, wherein the taste information label with negative influence comprises a label vocabulary matched with the negative taste information to form a negative taste label group，/>Number of negative labels;
s44: splitting each preference information on each resume into a plurality of phrases, respectively matching the phrases with positive labels in the positive preference label group and negative labels in the negative preference label group, and recording the number of the phrases which can be matched with the positive labelsi 1 Number of matches with negative labels in several phrasesi 2 ；
S45: number of passesi 1 And quantity ofi 2 Determining whether the preference information is a positive preference or a negative preference:
if it isi 1 =0 andi 2 =0, then determine the preference information as a forward preference;
if it isi 1 Not equal to 0 andi 2 not equal to 0, then the number of comparisonsi 1 And quantity ofi 2 Is of the size of (2):
if it isi 1 ≥i 2 The preference information is a forward preference;
if it isi 1 ＜i 2 The preference information is a negative preference;
s46: traversing each preference information on the resumes in the preliminary screening resume library, dividing each preference information into positive preference or negative preference, and counting the number of the positive preference and the negative preference on the resumeI 1 AndI 2 and compare the numberI 1 Sum and quantityI 2 Is of the size of (2):
if it isI 1 ≥I 2 The resume in the initial-screening resume library is a high-quality resume, ifI 1 ＜I 2 And the resume in the preliminary screening resume library is an inferior resume, and the inferior resume is returned to the human resource service database.
S5: packaging the screened high-quality resume, and forming mail information or short message information to be sent to a mailbox of an enterprise HR or sending a short message to the enterprise HR; the enterprise HR further screens job seekers from the quality resume for interviews.
According to the invention, the resume information of a huge amount of job seekers is recorded through the established human resource service database, the resume with high matching degree with the demands of the enterprise is screened through the resume information on each job seeker resume, the initial resume library is formed, the high-quality resume and the inferior resume in the initial resume library are further screened in an auxiliary mode through preference information, the enterprise HR only needs to interview the evaluator corresponding to the high-quality resume, the workload of the enterprise HR is greatly reduced, a multi-level and multi-parameter resume screening standard is set, the resume screening precision is high, and the working intensity of the enterprise on human resource management and talent recruitment is reduced.
Claims (4)
1. The human resource management method based on the big data platform is characterized by comprising the following steps:
s1: establishing a human resource service database, and inputting resume information and preference information of job seekers, wherein the resume information comprises an school grade, a working year, gender and profession;
s2: setting a sensitive label related to the academic level, a sensitive label of the working life, a sensitive label of the sex and a professional sensitive label;
s3: the method comprises the steps of respectively matching a sensitive label of an academic grade, a sensitive label of a working year, a sensitive label of gender and a sensitive label of a professional with the academic grade, the working year, the gender and the professional filled in a resume of a job seeker, establishing an evaluation model of human resources, and scoring the resume of the job seeker;
s4: loading all the resume meeting the requirements of enterprises into a primary screening resume library according to the scoring result of the resume, and screening out high-quality resume and inferior resume in the primary screening resume library by utilizing preference information on job seekers resume;
s5: packaging the screened high-quality resume, and forming mail information or short message information to be sent to a mailbox of an enterprise HR or sending a short message to the enterprise HR; the enterprise HR further screens job seekers from the quality resume for interviews.
2. The human resources management method based on big data platform according to claim 1, wherein the step S3 comprises:
s31: matching the academic information filled by the job seeker by using the sensitive label of the academic level, and scoring the academy of the job seeker, wherein the method specifically comprises the following steps of:
the sensitive labels of the academic grades are phrases related to the academic, each phrase is divided into single characters by taking each phrase as a unit, the single characters comprise [ high, medium ], [ special, family ], [ book, family ], [ major, doctor ] and [ doctor ], the academic information filled in the resume of the job seeker is matched with the sensitive labels of each academic grade, and the academic grade of the job seeker is scored according to the matching degree;
scoring criteria for the academic scale were: [ high, medium ]]=(a) [ Special, department of]=(a+1), [ book, department]=(a+2), [ Shuoshi]=(a+3) and [ doctor ]]=(a+4)；
Dividing the academic information filled in the resume of the job seeker into single characters, sequentially matching all the characters related to the academic information with the characters of the sensitive label of each academic level, and outputting the score corresponding to one academic level if the characters related to the academic information are matched with the characters of the sensitive label of one academic level by two or more than two charactersn；
S32: matching the sensitive label of the working years with the working years information filled in the resume of the job seeker to score the working years of the job seeker;
the sensitive label of the working period is the range of the working period: [0, 1) year, [1, 3) year, [3, 5) year, [5, + -infinity) year;
scoring criteria for different operational age ranges are: [0, 1) year ]aYear =1, 3 =a+1, [3, 5) year ]a+2, [5 ], ++ infinity) year =a+3；
Comparing the working years filled in on the job seeker resume with the range of each working year, outputting the score of the corresponding working year range as the score of the working year according to the working year range in which the working year of the job seeker fallsm；
S33: marking the sex of job seekers by using a sex sensitive tag, wherein the sex sensitive tag is the requirement of a target working position required by an enterprise on the sex; if the gender of the job seeker meets the gender requirement of the target working position, the gender of the job seeker is scoredu=aIf the gender of the job seeker does not meet the gender requirement of the target working post, the gender of the job seeker is scoredu=(a-1)；
S34: matching professional information filled in the job seeker resume by using the professional sensitive tag, and scoring the profession of the job seeker according to the matching degree;
setting a sensitive label of the job seeker specialty in the screening resume, wherein the sensitive label comprises a plurality of groups of special characteristics required by the target working post to form a professional label group related to the specialty:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein,sis the number of the phrase;
splitting the professional names filled in the resume of the job seeker intovEach phrase is matched with each label in the professional label group and recordedvNumber of words that can be matched with tags in a professional tag groupwCalculating the matching degree of the profession of job seekers and the profession required by the target working post：/>；
Scoring the profession of the job seeker according to the matching degree:
if it isProfessional scoring of job seekersy=a；
If it isProfessional scoring of job seekersy=a+1；
If it isProfessional scoring of job seekersy=a+2；
If it isProfessional scoring of job seekersy=a+3；
If it isProfessional scoring of job seekersy=a+4；
If it isProfessional scoring of job seekersy=a+5；
S35: and establishing an evaluation model of the human resources, and scoring the resume of the job seeker according to the evaluation model.
3. The human resource management method based on big data platform according to claim 2, wherein the evaluation model is:
；
wherein,x 1 、x 2 、x 3 、x 4 impact weights respectively scoring the resume of the job seeker for the school grade, the working year, the sex and the specialty,Ethe score of the resume is given to the job seeker.
4. The human resources management method based on big data platform according to claim 1, wherein the step S4 comprises:
s41: scoring threshold defining enterprise screening job seeker profilesE Threshold value The method comprises the steps of carrying out a first treatment on the surface of the If the resume of the job seeker makes a scoreE≥E Threshold value Judging that the resume of the job seeker meets the requirements of the enterprise, and sending the resume of the job seeker to a primary screening resume library of the enterprise; if the resume of the job seeker makes a scoreE＜E Threshold value Judging that the resume of the job seeker does not meet the requirements of enterprises, and leaving the resume of the job seeker in a human resource service database;
s42: traversing each resume in the human resource service database, and counting whether the number of the resume in the primary screening resume library meets the requirement; if yes, stopping the enterprise from screening the resume from the human resource service database, and entering step S43; otherwise, continuing to screen job seeker resume meeting enterprise requirements from the human resource service database;
s43: extracting each resume in the initial-screening resume library, and acquiring preference information on each resume;
setting a preference information label with positive influence on a target working position, wherein the positive influence preference information label comprises a label vocabulary which can be matched with the positive preference information to form a positive preference label group，eThe number of forward labels;
setting a taste information label with negative influence on a target working position, wherein the taste information label with negative influence comprises a label vocabulary and a shape which can be matched with the negative taste informationNegative hobby label group，/>Number of negative labels;
s44: splitting each preference information on each resume into a plurality of phrases, respectively matching the phrases with positive labels in the positive preference label group and negative labels in the negative preference label group, and recording the number of the phrases which can be matched with the positive labelsi 1 Number of matches with negative labels in several phrasesi 2 ；
S45: number of passesi 1 And quantity ofi 2 Determining whether the preference information is a positive preference or a negative preference:
if it isi 1 =0 andi 2 =0, then determine the preference information as a forward preference;
if it isi 1 Not equal to 0 andi 2 not equal to 0, then the number of comparisonsi 1 And quantity ofi 2 Is of the size of (2):
if it isi 1 ≥i 2 The preference information is a forward preference;
if it isi 1 ＜i 2 The preference information is a negative preference;
s46: traversing each preference information on the resumes in the preliminary screening resume library, dividing each preference information into positive preference or negative preference, and counting the number of the positive preference and the negative preference on the resumeI 1 AndI 2 and compare the numberI 1 Sum and quantityI 2 Is of the size of (2):
if it isI 1 ≥I 2 The resume in the initial-screening resume library is a high-quality resume, ifI 1 ＜I 2 And the resume in the preliminary screening resume library is an inferior resume, and the inferior resume is returned to the human resource service database.
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