CN113807827A - Human resource matching algorithm based on big data - Google Patents
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- CN113807827A CN113807827A CN202111215039.1A CN202111215039A CN113807827A CN 113807827 A CN113807827 A CN 113807827A CN 202111215039 A CN202111215039 A CN 202111215039A CN 113807827 A CN113807827 A CN 113807827A
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Abstract
The invention relates to the technical field of human resource management, and discloses a human resource matching algorithm based on big data, which comprises the following steps: s1, acquiring a resume index system and a demand index system in the resume text of the job seeker to obtain a matching score W1; s2, acquiring a resume index system and a demand index system in the resume text of the job seeker to obtain a matching score W2; s3, multiplying W1 in S1 and W2 in S2 to obtain a total value, setting the total value as a bar chart, arranging the values from high to low, and selecting a recruitment company from high to low.
Description
Technical Field
The invention relates to the technical field of human resource management, in particular to a human resource matching algorithm based on big data.
Background
With the increasing demand of recruitment and job hunting, globalization of information and high-speed development of networks, a brand-new job hunting and job hunting mode-network job hunting is brought, various job hunting websites are layered endlessly, a networked social contact mode also provides a good human resource platform, due to the richness of information of the network job hunting platform and the convenience of operation, more and more job hunters inquire appropriate job hunting information through the network job hunting platform for job hunting, the job hunters send resumes, and the job hunters obtain whether the collected resumes are matched with the directions of the job hunting positions through a matching algorithm of the service platform, so that whether the resumes are recorded or not is considered. However, the existing matching algorithm is single and simple, and the obtained matching value cannot guarantee high accuracy. Therefore, those skilled in the art provide a human resource matching algorithm based on big data to solve the problems mentioned in the above background art.
Disclosure of Invention
The invention aims to provide a human resource matching algorithm based on big data to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a human resource matching algorithm based on big data comprises the following steps:
s1, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing first-level matching with an index requirement system of a recruitment company to obtain a matching score W1;
s2, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing second-level matching with an index requirement system of a recruitment company to obtain a matching score W2;
and S3, multiplying the W1 in the S1 and the W2 in the S2 to obtain total values, and setting the total values as bar charts which are arranged from high to low, wherein the recruiter can pick from high to low.
As a still further scheme of the invention: the resume index systems in S1 and S2 are divided into basic information including native place, sex, age and marriage, learning ability including school and academic calendar, working ability including working experience and working time limit, language ability including Mandarin and other languages, learning ability, working ability, language ability, network job seeking frequency and application position.
As a still further scheme of the invention: the demand index system in S1 and S2 includes a desired job site, salaries including base salaries and growth, and basic benefits including five-insurance-one-money, bonuses, and holiday arrangements.
As a still further scheme of the invention: the S1 resume index system is matched with the index requirement system of the recruitment company by using a feature extraction blocking method, firstly, the application post is subjected to feature extraction, basic information, learning capacity, working capacity, language capacity and network job hunting frequency are divided into five blocks, the content filled in the application post is extracted, the content is matched with the post in the index requirement system of the recruitment company, after the matching is finished, four blocks in the resume index system are matched with four blocks in the index requirement system of the corresponding post, and the matching step comprises the following steps:
s4, matching the native place, sex, age and marriage in the basic information with the corresponding items in the index requirement system, if the native place, sex, age and marriage in the basic information satisfy the requirement, recording the native place as 0, and recording the native place as 1 if one satisfies, thereby deducing that the native place, sex, age and marriage in the basic information satisfy the four items, and recording the native place as 4 if the native place, sex, age and marriage in the index requirement system satisfy the requirement;
s5, performing decomposition matching on schools and calendars in learning ability, and recording 6, 5, 4, 3, 2, 1 and 0 from front to back for the calendars of students, Master students, Benedict, Special subject, high school, primary school and no calendars respectively;
s6, extracting specific work keywords in the working capacity to match with the application posts, if the specific work keywords are different, recording 0, if the specific work keywords are the same, recording 1 if the working time limit is less than one year, recording 2 if the working time limit is more than one year and less than three years, and recording 3 if the working time limit is more than three years;
s7, respectively setting Mandarin and other languages in the language ability as 0 and 1;
and S8, adding the numbers of the S5, S6, S7 and S8 together to obtain the total number W1.
As a still further scheme of the invention: the academic calendar, the application post and the working experience in the S2 resume index system are set as weights A, the academic calendar is 0.64, the academic calendar does not meet the requirement and is recorded as 0, the application post is 0.12, the post does not meet the requirement and is recorded as 0, the working experience is 0.24, and the working experience does not meet the requirement and is recorded as 0;
comparing the expected work place in the S2 demand index system with the city where the post is located to set the weight B and dividing the expected work place into the same city, the same province across cities and across provinces which are respectively marked as 1, 0.5 and 0.3;
comparing the pay of the current post with the pay in S2 to obtain a weight C, and if the pay of the current post is more than or equal to the pay in S2, marking as 1;
setting the network job hunting frequency in the S2 resume index system as weight D, and recording the frequency as 1, 0.7 and 0.5 respectively for more than five times a week, three to five times a week and one to three times a week.
As a still further scheme of the invention: the weight A, the weight B and the weight C are calculated according to a formula of (academic calendar + application post + work experience + expected work place + present post salary/base salary or 1) multiplied by network job hunting frequency, the result is a matching score value, and the value is set as W2.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through setting two algorithms of a feature extraction blocking method and a weight, each information of job seekers is extracted, compared and matched through the feature extraction blocking method, each matching numerical value is obtained visually and in detail, the weight adopts a Delphi method to obtain the matching numerical value more specifically and scientifically, and finally the two numerical values are combined together, so that the matching value with high accuracy is obtained, and the recruitment company can conveniently consider whether to be used for recording or not by referring to the matching value of the resume of the job seekers.
Detailed Description
In the embodiment of the invention, a human resource matching algorithm based on big data comprises the following steps:
s1, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing first-level matching with an index requirement system of a recruitment company to obtain a matching score W1;
s2, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing second-level matching with an index requirement system of a recruitment company to obtain a matching score W2;
and S3, multiplying the W1 in the S1 and the W2 in the S2 to obtain total values, and setting the total values as bar charts which are arranged from high to low, wherein the recruiter can pick from high to low.
Preferably, the resume index systems in S1 and S2 are divided into basic information, learning ability, working ability, language ability, network job seeking frequency and application position, wherein the basic information comprises native place, gender, age and marriage, the learning ability comprises school and school calendar, the working ability comprises working experience and working time limit, and the language ability comprises Mandarin and other languages.
Preferably, the demand index system in S1 and S2 includes desired job site, salary including base salary and growth, and basic welfare including five-insurance-one-money, bonus and holiday schedule.
Preferably, the S1 resume index system is matched with the index requirement system of the recruiter by using a feature extraction blocking method, the method comprises the steps of firstly extracting features of the application post, dividing the basic information, the learning ability, the working ability, the language ability and the network job hunting frequency into five blocks, extracting the content filled in the application post, matching the content with the post in the index requirement system of the recruiter, and matching four blocks in the resume index system with four blocks in the index requirement system of the corresponding post after matching, wherein the matching step comprises:
s4, matching the native place, sex, age and marriage in the basic information with the corresponding items in the index requirement system, if the native place, sex, age and marriage in the basic information satisfy the rules of zero, then recording 0, if one satisfies 1, and thus deducing that if four satisfies 4,
s5, performing decomposition matching on schools and calendars in learning ability, and recording 6, 5, 4, 3, 2, 1 and 0 from front to back for the calendars of students, Master students, Benedict, Special subject, high school, primary school and no calendars respectively;
s6, extracting specific work keywords in the working capacity to match with the application posts, if the specific work keywords are different, recording 0, if the specific work keywords are the same, recording 1 if the working time limit is less than one year, recording 2 if the working time limit is more than one year and less than three years, and recording 3 if the working time limit is more than three years;
s7, respectively setting Mandarin and other languages in the language ability as 0 and 1;
s8, the total number is marked as W1 by adding the numbers of the S5, the S6, the S7 and the S8, and the higher the W1 is, the more matched the result is.
Preferably, the academic calendar, the application post and the working experience in the S2 resume index system are set as the weight A, the academic calendar is 0.64, the academic calendar does not meet the requirement and is recorded as 0, the application post is 0.12, the post does not meet the requirement and is recorded as 0, the working experience is 0.24, and the working experience does not meet the requirement and is recorded as 0;
comparing the expected work place in the S2 demand index system with the city where the post is located to set the weight B and dividing the expected work place into the same city, the same province across cities and across provinces which are respectively marked as 1, 0.5 and 0.3;
comparing the pay of the current post with the pay in S2 to obtain a weight C, and if the pay of the current post is more than or equal to the pay in S2, marking as 1;
setting the network job hunting frequency in the S2 resume index system as weight D, and recording the frequency as 1, 0.7 and 0.5 respectively for more than five times a week, three to five times a week and one to three times a week.
Preferably, the weight a, the weight B and the weight C are calculated according to a formula of [ academic calendar + applied post + work experience + expected work place + own post salary/bottom salary or 1 ] x network job hunting frequency, the result is a matching score value, and the value is set to be W2, and a higher W2 indicates a better match.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.
Claims (6)
1. A human resource matching algorithm based on big data is characterized by comprising the following steps:
s1, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing first-level matching with an index requirement system of a recruitment company to obtain a matching score W1;
s2, acquiring a resume index system and a demand index system in the resume text of the job seeker, and performing second-level matching with an index requirement system of a recruitment company to obtain a matching score W2;
and S3, multiplying the W1 in the S1 and the W2 in the S2 to obtain total values, and setting the total values as bar charts which are arranged from high to low, wherein the recruiter can pick from high to low.
2. The big data-based human resources matching algorithm as claimed in claim 1, wherein the resume index systems in S1 and S2 are divided into basic information, learning ability, working ability, language ability, network job frequency and application position, the basic information includes native place, sex, age and marriage, the learning ability includes school and academic calendar, the working ability includes working experience and working time limit, and the language ability includes Mandarin and other languages.
3. The big-data based human resources matching algorithm as claimed in claim 2, wherein said demand targets system in S1 and S2 comprise expected workplace, salary and basic welfare, said salary comprising base salary and growth, said basic welfare comprising five-risk one-money, bonus and holiday schedule.
4. The human resource matching algorithm based on big data as claimed in claim 2, wherein the S1 resume index system uses a feature extraction blocking method to match with the index requirement system of the recruiter, firstly performs feature extraction on the application post and divides the basic information, learning ability, working ability, language ability and network job frequency into five blocks, extracts the content filled in the application post, matches the content with the post in the index requirement system of the recruiter, matches the four blocks in the resume index system with the four blocks in the index requirement system of the corresponding post after matching, and the matching step includes:
s4, matching the native place, sex, age and marriage in the basic information with the corresponding items in the index requirement system, if the native place, sex, age and marriage in the basic information satisfy the requirement, recording the native place as 0, and recording the native place as 1 if one satisfies, thereby deducing that the native place, sex, age and marriage in the basic information satisfy the four items, and recording the native place as 4 if the native place, sex, age and marriage in the index requirement system satisfy the requirement;
s5, performing decomposition matching on schools and calendars in learning ability, and recording 6, 5, 4, 3, 2, 1 and 0 from front to back for the calendars of students, Master students, Benedict, Special subject, high school, primary school and no calendars respectively;
s6, extracting specific work keywords in the working capacity to match with the application posts, if the specific work keywords are different, recording 0, if the specific work keywords are the same, recording 1 if the working time limit is less than one year, recording 2 if the working time limit is more than one year and less than three years, and recording 3 if the working time limit is more than three years;
s7, respectively setting Mandarin and other languages in the language ability as 0 and 1;
and S8, adding the numbers of the S5, S6, S7 and S8 together to obtain the total number W1.
5. The human resource matching algorithm based on big data as claimed in claim 3, wherein the academic calendar, the application position and the working experience in the S2 resume index system are set as weight A, the academic calendar is 0.64, the academic calendar does not meet the requirement and is marked as 0, the application position is 0.12, the position does not meet the requirement and is marked as 0, the working experience is 0.24, and the working experience does not meet the requirement and is marked as 0;
comparing the expected work place in the S2 demand index system with the city where the post is located to set the weight B and dividing the expected work place into the same city, the same province across cities and across provinces which are respectively marked as 1, 0.5 and 0.3;
comparing the pay of the current post with the pay in S2 to obtain a weight C, and if the pay of the current post is more than or equal to the pay in S2, marking as 1;
setting the network job hunting frequency in the S2 resume index system as weight D, and recording the frequency as 1, 0.7 and 0.5 respectively for more than five times a week, three to five times a week and one to three times a week.
6. The human resources matching algorithm based on big data as claimed in claim 4, wherein the weight A, the weight B and the weight C are calculated according to the formula of [ academic calendar + application post + work experience + expected work place + present post salary/bottom salary or 1 ] x network job hunting frequency, the result is a matching score value, and the value is set as W2.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116452163A (en) * | 2023-02-14 | 2023-07-18 | 广东尊一互动科技有限公司 | Talent recruitment management system and method based on big data |
| CN117473251A (en) * | 2023-10-10 | 2024-01-30 | 北京华普亿方科技集团股份有限公司 | User job-seeking intention analysis method and system based on big data |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150347954A1 (en) * | 2014-06-02 | 2015-12-03 | JungoLogic, Inc. | Matching system |
| CN107832776A (en) * | 2017-10-12 | 2018-03-23 | 如是科技（大连）有限公司 | The processing method and processing device that position is recommended |
| KR20180051377A (en) * | 2017-10-27 | 2018-05-16 | 석 영 정 | Job matching service method based on NCS and the system thereof |
| CN109558429A (en) * | 2018-11-16 | 2019-04-02 | 广东百城人才网络股份有限公司 | The two-way recommendation method and system of talent service based on internet big data |
| CN109992721A (en) * | 2019-04-19 | 2019-07-09 | 安徽工商职业学院 | Accurate proposed algorithm based on weighted eigenvalue |
| CN110059162A (en) * | 2019-04-28 | 2019-07-26 | 苏州创汇智信息技术有限公司 | A kind of matching process and device of job seeker resume and position vacant |
| CN110377702A (en) * | 2019-07-18 | 2019-10-25 | 中科鼎富（北京）科技发展有限公司 | A kind of biographic information matching process and device |
| CN112100999A (en) * | 2020-09-11 | 2020-12-18 | 河北冀联人力资源服务集团有限公司 | Resume text similarity matching method and system |
| CN112150120A (en) * | 2020-10-13 | 2020-12-29 | 未来现实海囤科技(青岛)有限公司 | Method and device for calculating matching degree of job seeker and recruitment post |
| CN113435841A (en) * | 2021-06-24 | 2021-09-24 | 浙江工贸职业技术学院 | Talent intelligent matching recruitment system based on big data |
-
2021
- 2021-10-19 CN CN202111215039.1A patent/CN113807827A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150347954A1 (en) * | 2014-06-02 | 2015-12-03 | JungoLogic, Inc. | Matching system |
| CN107832776A (en) * | 2017-10-12 | 2018-03-23 | 如是科技（大连）有限公司 | The processing method and processing device that position is recommended |
| KR20180051377A (en) * | 2017-10-27 | 2018-05-16 | 석 영 정 | Job matching service method based on NCS and the system thereof |
| CN109558429A (en) * | 2018-11-16 | 2019-04-02 | 广东百城人才网络股份有限公司 | The two-way recommendation method and system of talent service based on internet big data |
| CN109992721A (en) * | 2019-04-19 | 2019-07-09 | 安徽工商职业学院 | Accurate proposed algorithm based on weighted eigenvalue |
| CN110059162A (en) * | 2019-04-28 | 2019-07-26 | 苏州创汇智信息技术有限公司 | A kind of matching process and device of job seeker resume and position vacant |
| CN110377702A (en) * | 2019-07-18 | 2019-10-25 | 中科鼎富（北京）科技发展有限公司 | A kind of biographic information matching process and device |
| CN112100999A (en) * | 2020-09-11 | 2020-12-18 | 河北冀联人力资源服务集团有限公司 | Resume text similarity matching method and system |
| CN112150120A (en) * | 2020-10-13 | 2020-12-29 | 未来现实海囤科技(青岛)有限公司 | Method and device for calculating matching degree of job seeker and recruitment post |
| CN113435841A (en) * | 2021-06-24 | 2021-09-24 | 浙江工贸职业技术学院 | Talent intelligent matching recruitment system based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 杨红霞;: "基于雷达图的定制化企业网络招聘人职匹配研究", 电子商务, no. 07 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116452163A (en) * | 2023-02-14 | 2023-07-18 | 广东尊一互动科技有限公司 | Talent recruitment management system and method based on big data |
| CN116452163B (en) * | 2023-02-14 | 2023-11-28 | 广东尊一互动科技有限公司 | Talent recruitment management system and method based on big data |
| CN117473251A (en) * | 2023-10-10 | 2024-01-30 | 北京华普亿方科技集团股份有限公司 | User job-seeking intention analysis method and system based on big data |
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