CN105160498A - Personal value calculation method based on big data - Google Patents
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Abstract
The invention relates to a personal value calculation method and system based on big data. The method comprises steps of extracting person data from a lot of resumes; grading the extracted data according to a grading system; establishing a sparse structure penalty function with an organization structure prior, putting graded fields into a function mode and selecting a field; using the graded fields, establishing a regression model taking salary prediction as an object, selected fields as dependent variables and a revised expect salary as an independent variable , and calculating the coefficient of each field; and obtaining a new personal resume, and calculating the personal value corresponding to the personal resume according to the obtained coefficients. The resume in the table format in the prior art is technically processed, resume elements with real values are extracted from complicated written description and are embodied in a graphical data chart, so that all qualities of a person are clear at a glance. The invention accurately evaluate a person through standard variable calculation.
Description
Technical field
The present invention relates to technical field of data processing, be specifically related to for assessment of technical field of data processing, particularly relate to a kind of personnel's value calculation method based on large data.
Background technology
Be a merchandise valuation, very simply.Only need the production cost adjusting commodity, count raw material cost, physical distribution expenses, labour cost, site cost, equipment loss expense etc., add the profit of wanting to seize, that's all.
But be people's price, should what be adjusted? educational background? age? passing experience? wherein again how much each dimension for this? in Fiel's meeting of complexity, we always cannot weigh it with exact figure.The progress of technology makes the collection of data become more and more easier, thus brings magnanimity, high dimensional data to the numerous areas such as human resources, finance, medical science, information science and statistics.But, often there is bulk redundancy variable and redundancy feature in data.Therefore, from magnanimity, high dimensional data, how extracting important variable is the basic problem faced.
At present, in the prior art, the still full standard estimated for personnel salary of neither one and system, enterprise or individual cannot be worth personnel carry out objective assessment, and can only be subjective judgement.In recruitment process, there is general applicant not cognitive accurately to oneself, when at a loss as to what to do in the face of wages are talked by enterprise time.Enterprise itself also cannot by one intuitively the post of data to recruitment carry out one and fix a price accurately.Therefore, a kind of method can carrying out objective accurate calculation to personnel's value is needed.
Meanwhile, finance, insurance etc. other need also there is similar demand in the industry to personnel's valuation.
Summary of the invention
In view of above-mentioned analysis, the present invention aims to provide a kind of personnel's value calculation method, cannot be worth the problem of carrying out objective accurate assessment in order to solve in prior art to personnel.
Object of the present invention is mainly achieved through the following technical solutions:
Based on personnel's value calculation method of large data, comprise the following steps:
Step one, from resume in enormous quantities, extract demographic data;
Step 2, the Back ground Information extracted step one according to marking system, education/work experience data are given a mark;
Step 3, there is sparse group of structure penalty of institutional framework priori, the field after marking is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field;
Step 4, use the field after marking in step 2, to predict that wages are for target, the field selected using step 3 is as dependent variable, and revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field;
Step 5, from database, obtain new personnel's resume, extract each field data, substitute into marking system and give a mark, according to the coefficient that step 4 obtains, calculate these personnel corresponding to personnel's resume and be worth.
Wherein, described in step one, demographic data comprises further:
1) basic information data: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience data: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavation integrate features prism model is given certain weight calculation to each scale index and is obtained prism index;
Wherein, described step one comprises further:
Extract the compensation data in resume, comprising: expect emolument and the actual wages of part.
Wherein, described in described step 2, marking system comprises:
According to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, comprehensive grading is carried out to school;
To educational background according to below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank give a mark;
To specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state are given a mark according to mute scalar form;
Work experience is given a mark respectively according to company, position.
Wherein, described in described step 2, marking system comprises:
Give a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type, every class spacing calculates according to all personnel's average expectation salary level of such company of place again.
Wherein, revised Expectant salary described in step 4 is carry out revision after contrasting with actual wages to obtain.
Based on personnel's value calculation system of large data, comprise acquiring unit, marking unit, modeling unit, computing unit and display unit; Wherein,
Described acquiring unit is used for from database, obtain resume data, extracts demographic data;
The Back ground Information that described marking unit is used for extracting acquiring unit according to marking system, education/work experience data are given a mark;
Field after marking, for there is sparse group of structure penalty of institutional framework priori, is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field by described modeling unit; Use the field after the marking of marking unit, to predict that wages are for target, using selected field as dependent variable, revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field;
The coefficient of each field that described computing unit calculates according to modeling unit, calculates the new personnel corresponding to personnel's resume and is worth;
Described display unit, is worth and each field data for graphically showing personnel in display interface.
Wherein, described demographic data comprises further:
1) basic information data: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience data: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavation integrate features prism model is given certain weight calculation to each scale index and is obtained prism index;
Wherein, described acquiring unit extracts the compensation data in resume further, comprising: expect emolument and the actual wages of part.
Wherein, in described marking unit, the marking system of institute's foundation comprises:
According to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, comprehensive grading is carried out to school;
To educational background according to below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank give a mark;
To specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state are given a mark according to mute scalar form;
Work experience is given a mark respectively according to company, position.
Wherein, described marking system comprises further:
Give a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type, every class spacing calculates according to all personnel's average expectation salary level of such company of place again.
Beneficial effect of the present invention is:
Sheet format resume of the prior art is carried out technical finesse, and from numerous and diverse character narrate, extract real valuable resume key element, and show with patterned data drawing list, every quality of a people is all very clear.Be calculated as a people by standardized variable accurately to fix a price.
Other features and advantages of the present invention will be set forth in the following description, and, becoming apparent from instructions of part, or understand by implementing the present invention.Object of the present invention and other advantages realize by structure specifically noted in write instructions, claims and accompanying drawing and obtain.
Accompanying drawing explanation
Accompanying drawing only for illustrating the object of specific embodiment, and does not think limitation of the present invention, and in whole accompanying drawing, identical reference symbol represents identical parts.
Fig. 1 is the inventive method process flow diagram.
Embodiment
Specifically describe the preferred embodiments of the present invention below in conjunction with accompanying drawing, wherein, accompanying drawing forms the application's part, and together with embodiments of the present invention for explaining principle of the present invention.
The present invention is intended to the value being estimated personnel by a series of biographic information, behavioral data and psychological analysis data.
Based on personnel's value calculation method of large data, as shown in Figure 1, comprise the following steps:
Step one, from database, obtain resume data in enormous quantities, such as, the present embodiment is extracted 1,000 ten thousand parts of resumes (described resume comprises its behavioral data and psychological analysis data behind) altogether.
Described data comprise:
1) Back ground Information: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavate the prism model that the research of department of psychology of integrate features Beijing University is set up, whether prism index mainly has certain post competency to weigh employee.According to PRISM model each scale index given to certain weight calculation gets.Prism index score is lower, and the competent degree of work is lower, means that current job performance also has many spaces promoted.This model carries out going deep into psychoanalysis Comprehensive Assessment, provides every soft power index;
Meanwhile, be also extracted the compensation data in resume, comprise: expect emolument and the actual wages of part.
Step 2, the Back ground Information extracted step one according to marking system, education/work experience data are given a mark.
Described marking system is as follows:
School carries out comprehensive grading according to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, be divided into 5 grades, give a mark respectively according to 1-5, common non-transfer universities and colleges are 1 point, common transfer universities and colleges are 2 points, non-985 universities and colleges of 211 universities and colleges are 3 points, and 985 universities and colleges are 4 points, and Tsing-Hua University/Peking University is 5 points;
Educational background is distinguished below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank are given a mark, and be divided into 6 grades, score is respectively 0.8,1,2,3,4,5 from low to high;
Specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level, is divided into 130 kinds altogether, and as Marketing Assistant, Marketing Communication, Marketing Supervisor, the market manager, Marketing Director etc., marking scope is between 1-10 divides;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state, all to carry out give a mark (i.e. 01 matrix form) according to mute scalar form; These data do not have concrete numeric representation usually.In the present embodiment, extensive process can be carried out to these data.In some implementations, to by continuous print configuration data discretize, the configuration data after discretize can be encoded, obtains the numeric representation of configuration data.Such as configuration data can be quantized, obtain the configuration data represented by discrete data, then discrete data can be encoded to 0,1 feature, make a discrete data with T value (T is positive integer) be converted into the feature that T value is 0 or 1.
Work experience is given a mark respectively according to company, position; Wherein company gives a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type again, is divided into class 50 class, and every class spacing calculates according to all personnel's average expectation salary level of such company of place.
Step 3, there is sparse group of structure penalty of institutional framework priori, field after marking is substituted into function model, to realize residual error, two penalty add and after be minimised as target, from Back ground Information, education/work experience, behavioral data and psychological analysis data, select the higher field of factor of influence.
There is sparse group of structure penalty of institutional framework priori, to be predicted as index, for high dimensional data, automatically the field that factor of influence is higher is selected, preselected field amounts to 24 fields, the field of accomplishing fluently mark is substituted into model, to realize residual error, two penalty add and after be minimised as target, final selection 11 factors of influence, comprise sex, marital status, educational background, school, the consistance of specialty and engaged in trade, length of service, company, the position of nearest portion work, project experiences integrate score, career development path integrate score, job-hopping number of times, all the other several dimensions are by sparse GroupLasso model, and coefficient of correspondence all levels off to zero, so automatically eliminate, variable is after selecting like this, and what ensure that in set of variables and between group is openness simultaneously.
Step 4, use least-squares regression approach set up regression model:
Use the sample data of accomplishing fluently mark, to predict that wages are for target, above-mentioned 12 factors of influence chosen are as dependent variable, revised Expectant salary (revising after contrasting with actual wages) is as independent variable, use least-squares regression approach to set up regression model, finally calculate the coefficient of each field.
As: nearest a job overall coefficient is 1980, and company's coefficient is 987, and academic coefficient is 1167, and length of service coefficient is 687 etc.
Step 5, from database, obtain new personnel's resume, extract each field data, substitute in marking system, calculate according to the coefficient in model, obtain these personnel corresponding to personnel's resume and be worth.
Obtain after these personnel corresponding to personnel's resume are worth, in display interface, graphically show personnel being worth and each field data.
Based on personnel's value calculation system of large data, comprising:
Acquiring unit, marking unit, modeling unit, computing unit and display unit; Wherein,
Described acquiring unit is used for from database, obtain resume data, extracts demographic data;
The Back ground Information that described marking unit is used for extracting acquiring unit according to marking system, education/work experience data are given a mark;
Field after marking, for there is sparse group of structure penalty of institutional framework priori, is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field by described modeling unit; Use the field after the marking of marking unit, to predict that wages are for target, using selected field as dependent variable, revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field;
The coefficient of each field that described computing unit calculates according to modeling unit, calculates the new personnel corresponding to personnel's resume and is worth;
Described display unit, is worth and each field data for graphically showing personnel in display interface.
Described acquiring unit is used for from database, obtain resume data, extracts field;
From database, obtain resume data in enormous quantities, such as, the present embodiment is extracted 1,000 ten thousand parts of resumes (described resume comprises its behavioral data and psychological analysis data behind) altogether.
Described data comprise:
1) Back ground Information: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavate the prism model that the research of department of psychology of integrate features Beijing University is set up, whether prism index mainly has certain post competency to weigh employee.According to PRISM model each scale index given to certain weight calculation gets.Prism index score is lower, and the competent degree of work is lower, means that current job performance also has many spaces promoted.This model carries out going deep into psychoanalysis Comprehensive Assessment, provides every soft power index;
Meanwhile, be also extracted the compensation data in resume, comprise: expect emolument and the actual wages of part.
The Back ground Information that described marking unit is used for extracting acquiring unit according to marking system, education/work experience data are given a mark;
Described marking system is as follows:
School carries out comprehensive grading according to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, be divided into 5 grades, give a mark respectively according to 1-5, common non-transfer universities and colleges are 1 point, common transfer universities and colleges are 2 points, non-985 universities and colleges of 211 universities and colleges are 3 points, and 985 universities and colleges are 4 points, and Tsing-Hua University/Peking University is 5 points;
Educational background is distinguished below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank are given a mark, and be divided into 6 grades, score is respectively 0.8,1,2,3,4,5 from low to high;
Specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level, is divided into 130 kinds altogether, and as Marketing Assistant, Marketing Communication, Marketing Supervisor, the market manager, Marketing Director etc., marking scope is between 1-10 divides;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state, all to carry out give a mark (i.e. 01 matrix form) according to mute scalar form;
Work experience is given a mark respectively according to company, position; Wherein company gives a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type again, is divided into class 50 class, and every class spacing calculates according to all personnel's average expectation salary level of such company of place.
Field after marking, for there is sparse group of structure penalty of institutional framework priori, is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field by described modeling unit; Use the field after the marking of marking unit, to predict that wages are for target, using selected field as dependent variable, revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field.
First, modeling unit there is sparse group of structure penalty of institutional framework priori, field after marking is substituted into function model, to realize residual error, two penalty add and after be minimised as target, from Back ground Information, education/work experience, behavioral data and psychological analysis data, select the higher field of factor of influence.
There is sparse group of structure penalty of institutional framework priori, to be predicted as index, for high dimensional data, automatically the field that factor of influence is higher is selected, preselected field amounts to 24 fields, the field of accomplishing fluently mark is substituted into model, to realize residual error, two penalty add and after be minimised as target, final selection 11 factors of influence, comprise sex, marital status, educational background, school, the consistance of specialty and engaged in trade, length of service, company, the position of nearest portion work, project experiences integrate score, career development path integrate score, job-hopping number of times, all the other several dimensions are by sparse GroupLasso model, and coefficient of correspondence all levels off to zero, so automatically eliminate, variable is after selecting like this, and what ensure that in set of variables and between group is openness simultaneously.
Secondly, modeling unit uses the sample data of accomplishing fluently mark, to predict that wages are for target, above-mentioned 12 factors of influence chosen are as dependent variable, revised Expectant salary (revising after contrasting with actual wages) is as independent variable, use least-squares regression approach to set up regression model, finally calculate the coefficient of each field.
As: nearest a job overall coefficient is 1980, and company's coefficient is 987, and academic coefficient is 1167, and length of service coefficient is 687 etc.
Described computing unit obtains new personnel's resume from database, extracts each field data, substitutes in marking system, calculates according to the coefficient in model, obtains these personnel corresponding to personnel's resume and is worth.
Obtain after these personnel corresponding to personnel's resume are worth, display unit graphically shows each field data in display interface.
Beneficial effect of the present invention is:
Sheet format resume of the prior art is carried out technical finesse, and from numerous and diverse character narrate, extract real valuable resume key element, and show with patterned data drawing list, every quality of a people is all very clear.Be calculated as a people by standardized variable accurately to fix a price.
It will be understood by those skilled in the art that all or part of flow process realizing above-described embodiment method, the hardware that can carry out instruction relevant by computer program has come, and described program can be stored in computer-readable recording medium.Wherein, described computer-readable recording medium is disk, CD, read-only store-memory body or random store-memory body etc.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.
Claims (10)
1., based on personnel's value calculation method of large data, comprise the following steps:
Step one, from resume in enormous quantities, extract demographic data;
Step 2, the Back ground Information extracted step one according to marking system, education/work experience data are given a mark;
Step 3, there is sparse group of structure penalty of institutional framework priori, the field after marking is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field;
Step 4, use the field after marking in step 2, to predict that wages are for target, the field selected using step 3 is as dependent variable, and revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field;
Step 5, from database, obtain new personnel's resume, extract each field data, substitute into marking system and give a mark, according to the coefficient that step 4 obtains, calculate these personnel corresponding to personnel's resume and be worth.
2. the personnel's value calculation method based on large data according to claim 1, wherein, described in step one, demographic data comprises further:
1) basic information data: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience data: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavation integrate features prism model is given certain weight calculation to each scale index and is obtained prism index.
3. the personnel's value calculation method based on large data according to claim 1, wherein, described step one comprises further:
Extract the compensation data in resume, comprising: expect emolument and the actual wages of part.
4. the personnel's value calculation method based on large data according to claim 1, wherein, described in described step 2, marking system comprises:
According to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, comprehensive grading is carried out to school;
To educational background according to below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank give a mark;
To specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state are given a mark according to mute scalar form;
Work experience is given a mark respectively according to company, position.
5. the personnel's value calculation method based on large data according to claim 4, wherein, described in described step 2, marking system comprises:
Give a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type, every class spacing calculates according to all personnel's average expectation salary level of such company of place again.
6., based on personnel's value calculation system of large data, comprise acquiring unit, marking unit, modeling unit, computing unit and display unit; Wherein,
Described acquiring unit is used for from database, obtain resume data, extracts demographic data;
The Back ground Information that described marking unit is used for extracting acquiring unit according to marking system, education/work experience data are given a mark;
Field after marking, for there is sparse group of structure penalty of institutional framework priori, is substituted into function model, from Back ground Information, education/work experience, behavioral data and psychological analysis data, selects field by described modeling unit; Use the field after the marking of marking unit, to predict that wages are for target, using selected field as dependent variable, revised Expectant salary, as independent variable, uses least-squares regression approach to set up regression model, calculates the coefficient of each field;
The coefficient of each field that described computing unit calculates according to modeling unit, calculates the new personnel corresponding to personnel's resume and is worth;
Described display unit, is worth and each field data for graphically showing personnel in display interface.
7. the personnel's value calculation system based on large data according to claim 6, wherein, described demographic data comprises further:
1) basic information data: age, sex, area, registered permanent residence location, marital status, job hunting state;
2) education/work experience data: educational background, specialty, subject category, school, specialty and the consistance of engaged in trade, length of service, company, company size, company's type, industry, department, job category, position, project experiences, career development path, number of times of job-hoping, the longest portion length of service, technical ability;
3) behavioral data and psychological analysis data: obtain Internet user's behavioral data according to microblogging investigation, excavation integrate features prism model is given certain weight calculation to each scale index and is obtained prism index.
8. the personnel's value calculation system based on large data according to claim 6, wherein, described acquiring unit extracts the compensation data in resume further, comprising: expect emolument and the actual wages of part.
9. the personnel's value calculation system based on large data according to claim 6, wherein, in described marking unit, the marking system of institute's foundation comprises:
According to education of universities and colleges resource, universities and colleges' criteria for classification such as 985/211, comprehensive grading is carried out to school;
To educational background according to below senior middle school, senior middle school/professional high school/middle skill/special secondary school, junior college, undergraduate course, master, doctor/MBA/EMBA rank give a mark;
To specialty be engaged in post professional consistance give a mark;
Position is given a mark according to different professional level;
Age, length of service, job-hopping frequency are all given a mark according to after real data process;
Sex, marital status, job hunting state are given a mark according to mute scalar form;
Work experience is given a mark respectively according to company, position.
10. the personnel's value calculation system based on large data according to claim 9, wherein, described marking system comprises further:
Give a mark for each classification after carrying out cluster to company according to industry, location, company size, company's type, every class spacing calculates according to all personnel's average expectation salary level of such company of place again.
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