KR20210091680A - How to recommend a business and how to recommend a curriculum - Google Patents

Abstract

The present invention discloses a method for generating a job description, a method for generating a capability measurement tool, a method for recommending a new business, and a method for recommending a curriculum. In an embodiment, the method for generating a job description includes the steps of: receiving an input of required job competency from a terminal; deriving at least one similar job competency similar to the required job competency; and generating a job description from the required job competency and the similar job competency. Accordingly, a recruiter who does not have high knowledge of a job in the field of recruitment can write detailed and specific job description for the job in the field of recruitment.

Description

How to recommend business and curriculum {omit}

The present invention discloses a method for recommending a new business and a method for recommending a curriculum.

It is a very important task for companies to hire talented people who meet the skills required to perform their jobs in the industrial field. In the past, companies have recognized academic background as an important criterion for judging job performance irrespective of the job, and often did not provide sufficient job descriptions or clear hiring criteria during the hiring process. There are many cases in which talents unsuitable for the job are recruited through the hiring process of these companies, and there is a problem in that a lot of time and money are incurred in job training until the hired talents are put into practice.

In particular, there are many cases in which talented people who are put into practice fail to adapt to their jobs and leave or change jobs early, which can cause greater damage to companies and human resources.

Meanwhile, the government introduced the National Competency Standards (NCS) in order to identify the knowledge, skills, and abilities required for each job in the industrial field and use them as important criteria when hiring talent. The goal is to spread it not only to institutions, public institutions, and educational institutions, but also to private companies.

However, private companies are reluctant to introduce NCS because they need to understand and comply with the NCS standard system in order to introduce NCS, and it requires efforts and preparations to reflect related contents in the hiring process.

In particular, it is pointed out as a problem that there is a gap between the actual jobs experienced or experienced by workers or retirees who have worked in companies and departments and job classification through NCS.

Meanwhile, with the recent development of big data analysis technologies, many attempts have been made to improve job mismatching by using NCS. However, most companies lack awareness of NCS and are still passive in providing job information, which acts as a limitation in not being able to effectively improve job mismatch.

The present invention provides a method for solving the job mismatch problem.

The present invention provides a method by which a hiring manager, who does not have high knowledge of a job in the field of employment, can write a detailed and specific job description for a job in the field of employment.

The present invention provides a method for providing a rational basis in salary negotiation.

The present invention provides a method for an educational institution to reflect the actual needs of a company when setting a curriculum.

The object of the present invention is not limited thereto, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

The present invention provides a method for generating a job description. In an embodiment, the method for generating a job description includes: receiving an input of job competency required from a terminal; deriving one or more similar job competencies similar to the required job competencies; and generating a job description from the required job competency and the similar job competency.

According to another aspect, there is provided a method for generating a job description, the method comprising: receiving an input of job capability required from a terminal; deriving one or more similar job competencies similar to the required job competencies; providing the one or more similar job capabilities to the terminal; receiving one or more additional job capabilities selected from the one or more similar job capabilities from the terminal; and generating a job description from the required job competency and the additional job competency.

In an embodiment, after receiving the one or more additional job capabilities selected from the one or more similar job capabilities from the terminal: providing the required job capability and the additional job capability to the terminal; The method may further include receiving a weight for the required job capability and the additional job capability from the terminal.

In an embodiment, the required job capability, the additional job capability, and the weight may be stored as information about a company.

In an embodiment, the deriving the similar job competency includes: recommending the similar job competency based on the input job capability and a filter, wherein the filter is composed of data related to the required competency according to the job. Doedoe generated based on a dictionary including a plurality of job competency units, at least one of a degree of relevance between the job competency units and words extracted from data constituting the job competency units and a degree of correlation between the job competency units may include.

In an embodiment, in the filter, a word extracted from data constituting the job competency units in the first unit of any one of a row unit and a column unit, and the job competency units are arranged in the other second unit. may include a two-dimensional matrix in which are arranged, and the two-dimensional matrix may have, as a component value, a degree of association between each job competency unit of the first unit and each word of the second unit.

In one embodiment, the filter includes a two-dimensional matrix in which job competency units are arranged in a first unit of any one of a row unit and a column unit, and the job competency units are arranged in another second unit, , the two-dimensional matrix has a degree of relevance between a first job capability unit associated with each job capability unit of the first unit and a second job capability unit associated with each job capability unit of the second unit as each component value. The present invention provides a method for generating a competency measurement tool. In one embodiment, the method comprising the steps of: receiving a job capability required from the terminal; deriving one or more similar job competencies similar to the required job competencies; and generating a competency measurement tool from the required job competency and the similar job competency.

According to another aspect, there is provided a method for generating a capability measurement tool, the method comprising: receiving an input of job capability required from a terminal; deriving one or more similar job competencies similar to the required job competencies; providing the one or more similar job capabilities to the terminal; receiving one or more additional job capabilities selected from the one or more similar job capabilities from the terminal; and generating a competency measurement tool from the required job competency and the additional job competency.

In an embodiment, after receiving the one or more additional job capabilities selected from the one or more similar job capabilities from the terminal: providing the required job capability and the additional job capability to the terminal; The method may further include receiving a weight for the required job capability and the additional job capability from the terminal.

The present invention provides a new business recommendation method. In one embodiment, the method comprising the steps of: receiving a job capability required from the terminal; deriving one or more similar job competencies similar to the required job competencies; generating a competency measurement tool from the required job competency and the similar job competency; receiving the competency of one or more employees from the capability measurement tool; calculating a competency of the enterprise from the competency of the one or more employees; comparing the stored capabilities of one or more other enterprises; deriving a similar competency enterprise having similar capabilities to the enterprise among the one or more other enterprises; The business information of the derived similar competency company is provided to the company.

The present invention provides a method for recommending a company, job or job. In an embodiment, the method comprising the steps of: receiving a job capability required from a terminal; deriving one or more similar job capabilities similar to the required job capability; generating a competency measurement tool from the required job competency and the similar job competency; receiving the competency of the first measured person from the capability measurement tool; comparing the capabilities of the first measured person and the stored capabilities of one or more measured persons; extracting a second subject having similar capabilities to the first subject from among the one or more stored subjects; Any one or more of a company, a job, or a job is recommended to the first person from any one or more of company information, job information, and job information of the second person to be measured.

The present invention provides a curriculum recommendation method. In one embodiment, the method comprising the steps of: receiving a job capability required from the terminal; deriving one or more similar job competencies similar to the required job competencies; providing the one or more similar job capabilities to the terminal; receiving one or more additional job capabilities selected from the one or more similar job capabilities from the terminal; deriving the required job competency of the enterprise from the required job capability and the additional job capability; and recommending a curriculum to the educational institution by comparing the required job capability of the company with the job capability according to the curriculum of the educational institution.

According to an embodiment of the present invention, the problem of job mismatch can be solved.

According to an embodiment of the present invention, a hiring manager who does not have high knowledge about the job in the hiring field may write a detailed and specific job description for the job in the hiring field.

According to an embodiment of the present invention, a reasonable basis can be provided in salary negotiation.

According to an embodiment of the present invention, when an educational institution sets a curriculum, it is possible to reflect the actual needs of a company.

Effects of the present invention are not limited to the above-described effects, and effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention pertains from the present specification and accompanying drawings.

1 is a diagram illustrating an apparatus according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a process in which a processing unit recommends a similar job competency from the input required job competency and derives the required job competency, according to an exemplary embodiment.
3 is a flowchart illustrating a process from generating a job description of a company from the input required capability to connecting it to a job posting, according to an embodiment.
FIG. 4 is an exemplary screen of a user terminal illustrating an extension from the job capability input by the user from the similar job recommendation unit 31 to the similar job capability according to an embodiment.
5 is a method of generating a competency measurement tool from input job competency and negotiating salary, recommending a new business to a company, or recommending an employee, and a new company or a new job or a new job for the employee, according to an embodiment; It is a flow chart showing a method for recommending
6, 7, and 8 illustrate the data integration unit 36 according to an embodiment.
9 is a block diagram illustrating an example in which a database stores job competency data of a company according to an example.
10 is a graph illustrating an example of comparing company capabilities when recommending a new business for company 1 according to an example.
11 is a block diagram illustrating an example in which a database stores job competency data of a professional in one example.
12 is a graph illustrating an example of recommending a new company, a new job, or a new job for Professional 1;
13 is a flowchart illustrating a method of recommending an educational curriculum for an educational institution according to an exemplary embodiment.
14 is a flowchart illustrating a method of recommending an educational curriculum to an educational institution according to another exemplary embodiment.
15 is a block diagram illustrating a database holding required job competency of a company and curriculum information of an educational institution.
16 is a graph illustrating an example of recommending a curriculum to an educational institution.
17 is a block diagram of a job recommendation server constituting a job recommendation device according to an embodiment of the present invention.
18 is a block diagram of a filter generator constituting a job recommendation apparatus according to an embodiment of the present invention.
19 is a configuration diagram of a job recommendation unit constituting a job recommendation device according to an embodiment of the present invention.
20 is a block diagram of a first filter generating unit constituting the job recommendation apparatus according to an embodiment of the present invention.
21A and 21B are diagrams illustrating a document defining the job competency units 6124 of the NCS as an example of a dictionary including a plurality of job competency units.
22 is an exemplary view of a first filter according to the first embodiment of the present invention.
23 is a configuration diagram of a first job recommendation unit constituting a job recommendation apparatus according to the first embodiment of the present invention.
24 is a block diagram of a second filter generating unit constituting the job recommendation apparatus according to an embodiment of the present invention.
25 is an exemplary diagram of a second filter according to the first embodiment of the present invention.
26 is a configuration diagram of a second job recommendation unit according to the first embodiment constituting the job recommendation apparatus according to the embodiment of the present invention.

Other advantages and features of the present invention, and a method of achieving them, will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, and the present invention is only defined by the scope of the claims. Unless defined, all terms (including technical or scientific terms) used herein have the same meaning as commonly accepted by common skill in the prior art to which this invention belongs. A general description of known configurations may be omitted so as not to obscure the gist of the present invention. In the drawings of the present invention, the same reference numerals are used as far as possible for the same or corresponding components.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as "comprise", "have" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one It should be understood that it does not preclude the possibility of the presence or addition of or more other features or numbers, steps, operations, components, parts, or combinations thereof.

As used throughout this specification, '~ unit' is a unit that processes at least one function or operation, and may refer to, for example, a hardware component such as software, FPGA, or ASIC. However, '~part' is not meant to be limited to software or hardware. The '~ unit' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. As an example, '~ part' denotes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, and sub It may include routines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. A function provided by a component and '~ unit' may be performed separately by a plurality of components and '~ unit', or may be integrated with other additional components.

In the present invention, the term 'job competency' is used. The term 'job competency' used in the present invention refers to knowledge, skills, and attitudes required for job performance in an industrial field, and for example, the National Competency Standard (NCS, National Competency) in the Human Resources Development Service of Korea or the Korea Vocational Competency Development Institute. It can be defined as a capability unit according to the concept systematized as Standards). Specifically, about NCS, information can be obtained at https://www.ncs.go.kr/, and the main classification (24) classified by referring to the Korean Employment Classification of Occupations (KECO) → According to the sub-category (79) → sub-category (253) → sub-category (1,001), knowledge and skills of the elements of the competency unit provided in the NCS competency unit (report) included by each NCS competency unit included in the NCS competency unit provided in each sub-category , can be the attitude. On the other hand, job competency should not be interpreted as a concept limited to the above-mentioned NCS competency unit or competency unit element. Job competency may refer to all items described in the NCS, such as the name and definition of the competency unit, basic vocational competency, evaluation items, and performance criteria for each competency unit element. In addition, job competency may be a concept classified according to a certain standard to include knowledge, skills, attitudes, etc. required for job performance in an industrial field not described above.

1 is a diagram illustrating an apparatus 1 according to an embodiment of the present invention.

Referring to FIG. 1 , an apparatus 1 according to an embodiment includes a terminal 20 , a processing unit 30 , and a database 40 .

The terminal 20 is a terminal provided to a user or a job seeker to input information, for example, a smartphone, a tablet personal computer, a mobile phone, a video phone, an e-book reader ( e-book reader), desktop PC (desktop PC), laptop PC (laptop PC), netbook computer (workstation), server (server), PDA (personal digital assistant), PMP (portable multimedia player) ), an MP3 player, a mobile medical device, a camera, or a wearable device. Also, although not described, another terminal that is provided to the user and can receive information from the user may be provided.

The processing unit 30 includes a similar job recommendation unit 31 , a company information update unit 32 , a job description generation unit 33 , a competency sheet generation unit 34 , a new business recommendation unit 35 , and a data integration unit 36 . , it may include any one or more of the employee recommendation unit 37, the company / job / job recommendation unit 38, and the educational curriculum recommendation unit (39). The configuration of the processing unit 30 will be described later in detail.

The database 40 may exist in the processing unit 30 or may be configured as a server computer separate from the processing unit 30 . Data processed by the processing unit 30 may be stored in the database 40 , and data stored in the database 40 may be brought into the processing unit 30 and processed.

The job recommendation server 30 may access the job seeker terminal, the recruiter terminal, and the database 40 through various types of communication. Here, various types of communication may be implemented by a network interface capable of wired and/or wireless communication, for example, short-range communication (eg, Bluetooth, WiFi direct, or infrared data association (IrDA)) or long-distance communication (eg, cellular). network, Internet, or computer network (eg, LAN or WAN)). Wired/wireless network interfaces communicate according to various communication standards, such as, for example, IEEE, Zigbee, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), Long Term Evoloution (LTE), and 5th Generation (5G). It may be configured as a network interface that performs, but is not limited thereto.

FIG. 2 is a flowchart illustrating a process in which a processing unit recommends a similar job competency from the input required job competency and derives the required job competency, according to an exemplary embodiment. 3 is a flowchart illustrating a process from generating a job description of a company from an input job capability to a processing unit and connecting the job description to a job posting, according to an embodiment. The use of the same reference numerals as in FIG. 2 in FIG. 3 is the same as the description of FIG. 2 . The description of FIG. 2 may be substituted. The similar job recommendation unit 31 , the company information update unit 32 , and the job description generation unit 33 will be described with reference to FIG. 3 .

4 is an exemplary screen of a user terminal illustrating an extension from a job capability input from a user to a similar job capability from the similar job recommendation unit 31 according to an embodiment. 2 and 3 will be further described with reference to FIG. 4 . The similar job recommendation unit 31 recommends similar job capabilities. For example, assuming that the first job capability has a predetermined similarity to the second job capability, when the first job capability is input from the terminal 20 , the similar job recommendation unit 31 provides a second job capability similar to the first job capability. Derive job competency and third job competency.

An example will be described. In an embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown on the user line 100 , and matters processed by the processing unit 100 are displayed on the processing unit line 200 . is shown

First, the required job capability is input from the terminal 20 (1110). for example. The first job capability 118 is input from the terminal 20 (if there are a plurality of required job capabilities, a plurality of job capabilities will be input). The similar job recommendation unit 31 derives similar job capabilities from the input first job capability 118 ( S1210 ). The similar job recommendation unit 31 generates a first similar job capability and a second similar job capability similar to the first job capability (if there are several tens or more similar job capabilities, it is possible to generate several tens or more similar job capabilities from the number). will be). In the present invention, the term 'similar job ability' is used, which is a similar job recommended by the similar job recommendation unit 31, and the term similar job ability is used for convenience, and is substantially the same concept as job ability.

The similar job recommendation unit 31 generates a similar job capability list 125 using the same ( 1220 ), and provides it back to the terminal 20 ( 1230 ). The user of the terminal 20 includes a similar job capability list 125 including the first similar job capability 126 and the second similar job capability 127 extended from the first job capability 118 input into the terminal 20 . will be provided The similar job capability list 125 is a list of similar job capabilities similar to the input job capability, and for example, a first similar job capability and a second similar job capability may be listed.

The user can select a similar job ability with expanded job ability that he or she did not think of from the similar job ability list 125 received from the terminal 20 . The user may select an additional job competency required from the similar job capability list 125 ( 1120 ). For example, if the first similar job capability 126 is a job capability required by the company, it may be selected 128 .

Expanding the required job competency is very important from a company's point of view. For example, if the work performed through the first job competency occupies 70% of the work to be performed in the required job position, the hiring manager of the company is not easily aware of the remaining 30% of the work. This is because the hiring manager is not engaged in the relevant position, so it is not clearly understood at the time of writing the job description. Such corrections have serious consequences, especially in small and medium-sized enterprises (SMEs) where the manpower is required. For example, Mr. B, who joined Company A, understanding that only the first job that requires only the first job competency in the specific job description written by Company A will be his job. 2 It will be difficult to maintain attendance at company A due to circumstances such as demanding job competency and requesting a second job in addition to the first job. This is a frequent occurrence in small and medium-sized enterprises (SMEs) where there is a shortage of manpower.

After extending the required job competency by additionally selecting the required job competency from the recommended similar job capability, the user of the terminal 20 needs the input first job capability 118 and the first similar job capability 126 . It is stored as a job capability (1240). The stored required job competency may be stored as company information to update the company information ( 1280 ). And the stored required job competency is generated as a required job capability list 135 ( 1250 ). The generated required job competency list 135 is provided to the user ( 1260 ). The user may input a weight 136 for each required job competency from the provided required job capability list 135 ( 1130 ). For example, the weight input for the first job capability 118 may be 0.8, and the weight input for the first similar job capability 126 may be 0.2. The weight may be what defines the level required by the enterprise to be judged by the user. The input weight for each required job competency is stored through the processing unit 30 ( 1270 ). The stored weight for each required job competency may be used as information about the company. For example, the company information may be updated while being stored as company information ( 1280 ). The updated company information can serve as data to recommend similar job capabilities in the future. For example, if Company A requires a competency that has a weight of 0.8 for the first job competency for the first position and requires a capability with a weight of 0.2 for the first similar job competency, Company A receives a weight of 0.8 It may be a company that requires the first job capability and the first similar job capability with a weight of 0.2. This may be used to recommend the first similar job ability when the first job ability is input from the terminal 20 later, or to recommend the first job ability when the first similar job ability is input. These numerical values are converted into meaningful numerical values as the number of input positions increases, which will be described later in detail.

The processing unit 30 generates a draft job description from the input required job competency and weights for each required job competency. The draft job description may be generated from detailed data (eg, detailed description of knowledge, skills, attitude, etc.) derived from data stored according to each job capability and pre-entered data such as a company's pre-entered job description.

The processing unit 30 may generate a draft job description ( 2210 ). The processing unit 30 may provide the generated draft job description to the user ( 2210 ). The user may receive a draft job description from the terminal 20 and appropriately modify the draft job description as needed ( 2210 ). A job description is generated by reflecting the contents modified by the user (2230). The job description generated through the above-described steps may be linked to a job posting ( 2240 ).

The job description generated through the above method expands even job capabilities that the company is not aware of, so that the job description can include more accurate job skills required, and by providing a draft job description, hiring managers who lack writing skills By providing assistance to employees, job descriptions can be written in more detail and accurately. As a result, communication between job seekers and recruiters can be facilitated and the problem of job mismatch can be resolved.

5 is a method of generating a competency measurement tool from input job competency and negotiating salary, recommending a new business to a company, or recommending an employee, and a new company or a new job or a new job to the employee, according to an embodiment; It is a flow chart showing a method for recommending The use of the same reference numerals as in FIG. 2 in FIG. 5 is the same as the description of FIG. 2 . The description of FIG. 2 may be substituted. An example will be described. In an embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown on the user line 100 , and matters processed by the processing unit 100 are displayed on the processing unit line 200 . As shown, information received from an employee through the terminal 20 or another terminal is shown in the employee line 300 . A method of negotiating an annual salary, a method of recommending a new business, a method of recommending an employee, and a method of recommending a company/job/job to an employee according to the embodiment will be described with reference to FIG. 5 . Also, with reference to FIG. 5 , the capability sheet generating unit 34 , the new business recommendation unit 35 , the employee recommendation unit 37 , and the company/job/job recommendation unit 38 will be described.

After expanding the required job competency by additionally selecting the required job ability from the similar job ability recommended from the same method as the description using FIG. 2 and selecting the required job competency (1000), the competency sheet generating unit 34 is input A draft competency measurement tool is generated from the received required job competency and weights for each required job competency (30). The competency measurement tool is a tool that can measure an employee's job competency, and may include several questionnaires. For example, the questionnaire may be a question extracted from the performance criteria included in the NCS competency unit (report) (see FIG. 21). Data included in the classification according to each job competency may include evaluation factors of job competency, and these evaluation factors can be automatically extracted to create a draft competency measurement tool. For example, a draft may be created as shown in [Table 1] below. The draft competency measurement tool can be provided with the selection of evaluation elements adjusted according to the difficulty of each evaluation element in consideration of the weight for each required job competency.

Draft Exemplary Competency Measurement Tool job ability evaluation factor Select detailed selection Analyze the development environment of partner countries It is possible to analyze the feasibility of promoting official development assistance by examining the partner country's policies, systems, and laws. Korea's comparative advantage can be analyzed by identifying support characteristics, operational strategies, and implementation plans of other donors who support partner countries. By analyzing the stakeholders of Korea and partner countries, it is possible to suggest a plan for smooth cooperation. It is possible to analyze the specifics of project execution by examining the domestic and foreign environment and other risk factors of partner countries for official development assistance. Analyze the project environment The internal environment can be analyzed according to the internal factors of the organization. The external environment can be analyzed according to factors external to the organization. Assumptions and prerequisites can be identified through project environment analysis. Project work documents and contract contents, which are the main inputs of the project site, can be reviewed in advance. Investigate project feasibility Through environmental analysis, technology changes and trends of the project can be identified. The technical and economic feasibility of the project can be investigated through environmental analysis. For effective project selection, size, complexity, list diagram, and analysis target can be defined and an effective analysis plan can be established. Based on the results of the project feasibility study, a feasibility study report can be prepared.

In the example according to [Table 1] above, 'analyzing the development environment of a partner country' corresponds to the first job competency, 'analyzing the project environment' corresponds to the first similar job competency, and 'to investigate project feasibility' corresponds to the first job competency. 2 It may correspond to similar job competencies. According to an embodiment, the evaluation elements have been exemplified to include only the evaluation elements according to the performance criteria, but from the items in the [Knowledge] column, I know about the development policies and support status for partner countries such as international organizations, bilateral aid organizations, and NGOs. ', or items such as 'I have the ability to communicate in a foreign language' from the items in the [Description] column, or items such as 'I have the will to think objectively and logically' from the items in the [Attitude] column can also be derived.

The draft competency measurement tool described above is provided to the user ( 3220 ). The user provided with the draft capability measurement tool may edit the draft capability measurement tool or select only necessary items ( 3110 ). For example, in the selection of a necessary item, a group of evaluation elements according to job competency units may be selected or detailed selection may be made for each evaluation element.

A draft competency measurement tool modified or selected from the user in a number of ways described above is generated as a capability measurement tool ( 3230 ). For example, the capability measurement tool can be created as shown in [Table 2] below.

competency measurement tool job ability evaluation factor Yes usually no Analyze the development environment of partner countries It is possible to analyze the feasibility of promoting official development assistance by examining the partner country's policies, systems, and laws. Korea's comparative advantage can be analyzed by identifying support characteristics, operational strategies, and implementation plans of other donors who support partner countries. By analyzing the stakeholders of Korea and partner countries, it is possible to suggest a plan for smooth cooperation. It is possible to analyze the specifics of project execution by examining the domestic and foreign environment and other risk factors of partner countries for official development assistance. Analyze the project environment The internal environment can be analyzed according to the internal factors of the organization. The external environment can be analyzed according to factors external to the organization. Assumptions and prerequisites can be identified through project environment analysis. Project work documents and contract contents, which are the main inputs of the project site, can be reviewed in advance.

The competency measurement tool is provided to the employee 300 ( 3240 ). The employee 300 may be provided with a competency measurement tool to measure their own competency ( 3310 ). As an example, the employee may measure the competency improvement by receiving the competency measurement tool provided in the year of employment, measuring the competency, and measuring the competency again from the competency measurement tool for salary negotiation in the next year. This may serve as a basis for salary negotiation (3500). In addition, the competencies measured by the employees themselves using the competency measurement tool may lack objectivity, so they may be modified by the manager. The processing unit 30 may provide information to the manager by providing a notification about the evaluation result indicating the excessive increase. The new business recommendation unit 35 recommends a new business to the company from the result of measuring the competency of the employee (3600). ). Also, the employee recommendation unit 37 recommends an employee to the company from the measured competency ( 3700 ). In addition, the company/job/job recommendation unit 38 recommends a company or job or job to the employee based on the measured competency ( 4000 ). 6 shows the data integration to derive the employee's job competency from the employee's job ability, and FIGS. 7 and 8 show that the new business recommendation unit 35 recommends a new business, or the employee recommendation unit 37 recommends a new business. It shows how data is integrated to recommend an employee. FIG. 5 will be described with further reference to FIGS. 6, 7, and 8 .

The data integration unit 36 may integrate data. For example, if the job capability 1, the job capability 2, and the job capability 3 of the employee 1 measured from the employee 1 are integrated through the data integration unit 36, the job capability of the employee 1 can be derived. And if the data for Employee 2, Employee 3, and Employee 4 are integrated in the same way as Employee 1, the job competency of Employee 2, the job competency of Employee 3, and the job competency of Employee 4 are obtained. The job competency of the company may be derived by integrating the job capabilities of the employees belonging to the company. For example, the job competency of company 1 having employee 1 and employee 2 is as shown in FIG. 8 . It can be derived in the form of integrating the job competency of Employee 1 and the job competency of Employee 2. On the other hand, whether or not a company has a specific machine may be corrected as job competency data of the company.

The job competency of the company 1 thus derived is stored in the database 40 . Also, as referenced through FIG. 9 , the database 40 may store job competency data of company 2, company 3, ... company n. For example, in recommending a new business to Company 1, it is possible to propose a new business to Company 1 by providing business information of the company with the most similar job competency through comparison with Company 2, Company 3, ... Company n. can

As an example, FIG. 10 is a graph illustrating an example of comparing company capabilities when recommending a new business for company 1. As referenced through FIG. 10 , the company 1 has similar job capabilities to the company 3, and by providing the business field of the company 3 to the company 1, a new business can be recommended to the company 1. Conversely, by providing the business field of Firm 1 to Firm 3, a new business can be recommended to Firm 3. In addition, if Company 1 is engaged in a business field similar to Company 2, while Company 1's job ability 5 is relatively weak compared to Company 2, an employee with superior job ability 5 for Company 1 (job applicant or other company) of staff, etc.) can be recommended.

11 is a block diagram illustrating an example in which a database stores job competency data of a professional in one example. 12 is a graph illustrating an example of recommending a new company, a new job, or a new job for Professional 1; The company/job/job recommendation unit 38 will be described with reference to FIGS. 11 and 12 . 11 , the information of individual employees stored through the above-described examples may be defined and stored as professionals. An employee is an employee when belonging to a company, but corresponds to a professional when viewed from a social point of view, so in the description of the present invention, an employee is defined as a professional by looking at the employee from a position outside the company. For example, the database 40 may store job competency data of occupational 1, occupational 2, occupational 3, ... occupational n. Each professional may be affiliated with the same company, may be affiliated with different companies, may be freelancers, may be entrepreneurs or self-employed, or may have other undefined occupations. 12, when comparing the job competency of Professional 1, Professional 2, and Professional 3, Professional 1 has job competencies similar to Professional 2, and provides information on the company or occupation or job of Professional 2 for Professional 1 By doing so, it is possible to recommend a company, occupation, or job to professional person 1. Conversely, by providing the company or occupation or job information of the professional 1 to the professional 2, the company or occupation or job information can be recommended to the professional 2.

13 is a flowchart illustrating a method of recommending an educational curriculum for an educational institution according to an exemplary embodiment. The use of the same reference numerals as those of FIG. 2 in FIG. 13 is the same as the description of FIG. 2 , and thus may be replaced with the description of FIG. 2 . In an embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown on the user line 100 , and matters processed by the processing unit 100 are displayed on the processing unit line 200 . The information received by the educational institution through the terminal 20 or may be another terminal is illustrated in the educational institution line 400 . A method of recommending an educational curriculum to the educational curriculum recommendation unit 39 and an educational institution will be described with reference to FIG. 13 .

In one embodiment, in addition to writing the job description described in FIG. 2 and linking it with the job posting, the training curriculum recommendation unit 39 may recommend an educational curriculum based on the required job skills required by the company. There is (5000).

14 is a flowchart illustrating a method of recommending an educational curriculum to an educational institution according to another exemplary embodiment. The use of the same reference numerals as those of FIG. 4 in FIG. 14 is the same as the description of FIG. 4 , and thus may be replaced with the description of FIG. 4 . In an embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown on the user line 100 , and matters processed by the processing unit 100 are displayed on the processing unit line 200 . The information received by the educational institution through the terminal 20 or may be another terminal is illustrated in the educational institution line 400 . A method of recommending an educational curriculum to the educational curriculum recommendation unit 39 and an educational institution will be described with reference to FIG. 14 .

In one embodiment, by generating and providing the competency measurement sheet described in FIG. 4 and extending the salary negotiation through competency measurement (or recommending a new business to a company, recommending an employee, or a company / In addition to recommending a job/job), the educational curriculum recommendation unit 39 may recommend an educational curriculum based on the required job competency required by the company and the job competency that the company has in the actual industrial field (5000).

15 is a block diagram illustrating a database holding required job competency of a company and curriculum information of an educational institution, and FIG. 16 is a graph illustrating an example of recommending a curriculum to an educational institution. It will be described with reference to FIG. 14 or FIG. 14 and FIGS. 15 and 16 .

The database 40 has job competencies required by the company and job competencies according to the curriculum of the educational institution. The required job competency of the company stored in the database 40 is compared with the job competency according to the curriculum of the educational institution. 16 , the job competency required by the company and the job competency according to the curriculum of the educational institution may be different from each other. Looking at an example according to FIG. 16 , on the other hand, the company greatly requires job competency 1, job capability 3, and job competency 5, whereas the curriculum of educational institutions provides little education on job capability 5, whereas the company requires They may be training on job competency 2 that they do not do. The educational curriculum recommendation unit 39 according to an embodiment of the present invention may suggest a curriculum that increases the weight of job competency 5 to an educational institution while reducing the weight of job competency 2.

Hereinafter, a method of extracting similar job capabilities from the input job capabilities will be described.

17 is a block diagram of a job recommendation server constituting a job recommendation device according to an embodiment of the present invention. Referring to FIG. 17 , the job recommendation server 31 may include a filter generating unit 6100 , a filter 6200 , and a job recommendation unit 6300 .

The filter generating unit 6100 may generate one or a plurality of different filters 6200 based on a dictionary including a plurality of job competency units.

A dictionary containing a plurality of job competency units (hereinafter referred to as a dictionary collection) includes job competency and job competency extracted from specific job-related data (eg, job-seeker's self-introduction letter, job description of a recruiter, classification data according to NCS, etc.) It refers to a dataized set in which related data (eg, words, sentences, etc. related to job competency) are classified and organized according to each defined job competency unit. Each job competency unit may include an index representing job competency and data related to job competency.

The filter 6200 derives an output value related to the input value when an input value is provided. For example, the input value may be a word related to job ability or job ability. For example, the output value may be a word related to job ability or job ability. In an embodiment, the filter 6200 may be calculated based on a dictionary. The filter 6200 generated by the filter generator 6100 may be stored in the database 40 . The job recommendation unit 6300 may recommend a job suitable for the target based on the target-related data and the filter 6200 generated by the filter generator 6100 .

In an embodiment, a dictionary including a plurality of job competency units includes documents related to competency units according to job classifications defined according to National Competency Standards (NCS) classification, and words extracted from these documents. It may include a collection of words. The dictionary is composed of a plurality of job competency units. The definition of job competency unit does not simply mean the job competency itself, but should be understood as a concept of a data set that includes data related to the capabilities required by each job competency. It should be interpreted as a concept including a glossary or a job description defined as a sentence containing a plurality of words for each ability, a job description, and their index.

In an embodiment, the filter generating unit 6100 extracts data according to each job competency unit from the dictionary of each job competency unit, extracts words from the extracted data according to each job competency unit, and then each job competency A filter can be created by calculating the degree of relevance between units and words or the degree of relevance between each job competency unit. For example, the data for each job competency unit includes 'classification number', 'name', 'definition', 'evaluation guidelines', 'basic job competency', 'evaluation items', and job competency belonging to the job competency unit. It may be a data-formatted document including information such as 'name', 'performance standard', 'knowledge', 'skill', 'attitude' for each unit element.

18 is a block diagram of a filter generator constituting a job recommendation apparatus according to an embodiment of the present invention. 19 is a configuration diagram of a job recommendation unit constituting a job recommendation device according to an embodiment of the present invention. 18 and 19 , the filter generator 6100 may include a first filter generator 6120 , a second filter generator 6140 , and a filter updater 6160 . The filter 6200 may include a first filter 6220 and a second filter 6240 . The job recommendation unit 6300 may include a first job recommendation unit 6320 and a second job recommendation unit 6340 .

The first filter generating unit 6120 may generate a first filter 6220 of a two-dimensional matrix representing a relationship between each job competency unit and words extracted from data according to each job competency unit. The second filter generator 6120 may generate a second filter 6240 of a two-dimensional matrix indicating a degree of association between each job competency unit.

The first job recommendation unit 6320 may recommend a job to the target based on the first filter 6220 generated by the first filter generation unit 6120 . The second job recommendation unit 6340 may recommend a job to the target based on the second filter 6240 generated by the second filter generation unit 6140 . Hereinafter, the first filter 6220 will be described first, and then the second filter 6240 will be described.

20 is a block diagram of a first filter generating unit constituting the job recommendation apparatus according to an embodiment of the present invention. 18 and 20 , the first filter generating unit 6120 includes a dictionary collection building unit 6122 , a word extracting unit 6126 , a relevance analyzing unit 6130 , and a normalizing unit 6132 . may include The dictionary collection building unit 6122 may construct the dictionary collection 6125 by dividing and extracting data according to job competency units from the job competency-related data 6400 and integrating the extracted data according to the job competency units. In an embodiment, the dictionary collection building unit 6122 may extract pdf documents defining a job competency unit for each of the 24 NCS major categories. The number n of job capability units 6124 may be different, and in an embodiment, n represents the number of job capability units 6124 belonging to a corresponding classification. The pdf documents according to the n job competency units 6124 belonging to the same classification can be utilized to calculate the first filter 6220 for the corresponding classification, and as many as the number of classifications (for example, According to the large classification, 24) first filters 6220 may be calculated.

21A and 21B are diagrams illustrating a document defining the job competency units 6124 of the NCS as an example of a dictionary including a plurality of job competency units. The dictionary building unit 6122 provides 'classification number', 'title', 'level', 'definition', 'basic job competency', 'evaluation matters', 'performance criteria' for each job competency unit element, Data related to job competency units can be extracted from documents containing information such as 'knowledge', 'skill', and 'attitude'. The word extraction unit 6126 may extract words 6128 from all n job competency units. In an embodiment, the word extractor 6126 may include a morpheme analyzer for dividing sentences in a document defining the job competency units 6124 into words of a morpheme unit and tagging the part-of-speech. In an embodiment, the word extraction unit 6126 excludes stop words from sentences, and N-grams such as Uni-grams and/or bi-grams (N is an integer greater than or equal to 2) ) to extract words.

The relevance analysis unit 6130 may analyze the degree of association between the job capability units 6124 defined in the dictionary collection 6125 and the words 6128 extracted from the job capability units 6124 . In an embodiment, the relevance analysis unit 6130 is based on Term Frequency Inverse Document Frequency (TF-IDF) n number of job competency units 6124 and m words extracted from the n number of job competency units 6124 . The degree of association between the 6128 may be analyzed. The degree of association between the job competency units 6124 and the words 6128 may be calculated according to, for example, Equations 1 to 3, or Equations 1, 2, and 4 below.

[Formula 1]

[Formula 2]

[Equation 3]

[Equation 4]

In Equations 1 to 4, 'tfidf(t,d,D)' is a word frequency inverse document frequency, 't' is a word, 'd' is a document, D is a set of all documents, and tf(t,d) is a word frequency, idf(t,D) is the inverse document frequency, 'ft,d' is the frequency of the word t in document d, and 'df(t,d)' is the number of documents including the word t among all documents indicates

Relevance analysis unit 130 applies not only TF-IDF, but also word embedding technology such as Non-negative Matrix Factorization (NMF), Word2Vec/Doc2Vec, and job competency unit documents. It is also possible to analyze the degree of association between words extracted from the job competency unit documents.

When the number of words 6128 extracted from the n job competency units 6124 is m, the associations between the job competency units 6124 and the words 6128 can be represented by a matrix of size n×m. there is. The normalizer 6132 may generate a normalized matrix by dividing each component of the n×m matrix by a set value so that the size of the n×m matrix is normalized to 1. The set value may be different according to each component of the n×m matrix. Also, the set value may be different according to a normalization method. Meanwhile, in the present embodiment, the size of the matrix is normalized to be 1, but it may be transformed to an appropriate size such as 10 or 100. Accordingly, the normalized first filter 6220 may be generated for each major classification. By normalizing the first filter 6220, it is possible to prevent the priority of recommended tasks belonging to different large classifications from being influenced by different matrix sizes between the large classifications, and recommended tasks as a unit of competency within a specific large classification (a large classification with a large matrix size) It is possible to prevent the concentration of jobs (competency units) suitable for job seekers not only in large classifications with large matrix sizes but also in large classifications with small matrix sizes.

22 is an exemplary view of a first filter according to the first embodiment of the present invention. 20 and 22 , the first filter 6220 includes job competency units 6124 (a1, a2, ..., an-1, an) in a row unit (a first unit). Words 6128 (v1, v2, ..., vm-1, vm) extracted from job competency units 6124 of the dictionary collection 6125 in a column unit (second unit) This can be expressed as a two-dimensional matrix that is arranged. Each component value of the 2D matrix of the first filter 6220 may be a value obtained by dividing a TF-IDF value of a word by a matrix size and normalizing the value. The first filter 6220 may be a set of row vectors having a size of 1×m of each of the n job unit capabilities. The number m of words 128 may be approximately tens to hundreds of thousands of words.

In the above, with respect to the embodiment of the first filter 6220 in which job competency units are sequentially arranged in a row unit (first unit), and words extracted from job competency unit documents are sequentially arranged in a column unit (second unit) Although described, in the first filter 6220, job competency units may be arranged in a column unit, and words extracted from the job competence units may be arranged in a row unit. In this case, each job competency unit vector may be provided as a column vector.

Referring back to FIG. 18 , the filter updater 6160 collects update information of a dictionary including a plurality of job competency units through a periodic crawling technique, and collects the dictionary collection including a plurality of job competency units. The filter can be updated based on the update information of . The dictionary collection including a plurality of job competency units can be changed and updated to reflect future social changes and the demands of industrial sites, and when new or disappearing jobs exist, job competency units can be added or deleted. In addition, words or required abilities included in the job competency unit may be changed or updated to include more specific terms. For example, when the word 'powerpoint', which is a program name used in an industrial field, is collected from a self-introduction letter, which is an example of job competency-related data 6400 , when powerpoint is a word that is not included in the job competency unit, the job competency unit is The including dictionary collection may be updated to include it. The filter update unit 160 may update the filter when a dictionary including a plurality of job competency units is updated.

For example, the filter updater 6160 may detect a job competency unit having a new classification number or a new name in a dictionary including a plurality of job competency units collected through crawling, or use classification numbers of existing job competency units or If there is a classification number or name that has been deleted among the names, or by crawling technology, 'job basic competency', 'evaluation items' of job competency units, 'performance standards', 'knowledge' for each job competency unit element When it is determined that information such as , 'skill', 'attitude' has been changed, it is determined that the dictionary book including a plurality of job competency units has been changed or updated, and the filter may be updated.

23 is a configuration diagram of a first job recommendation unit constituting a job recommendation apparatus according to the first embodiment of the present invention. Referring to FIGS. 18, 22 and 23 , the first job recommendation unit 6320 provides one or more job recommendations to the subject based on the subject's input job ability 6500 , for example, the required job ability, and the first filter 6220 . You can recommend a job. In an embodiment, the input job ability 6500 is data collected by a chatbot that collects education information, qualification information, portfolio, resume, self-introduction, SNS data, and history information in the form of conversation with the subject, the subject's voice The data collected through the target may include digitized personal information such as social relationship information, SNS interests, content viewing records, and search information, military service specialties, military service portfolio, missions performed during military service, and the like. The input job ability 6500 may be collected from the target terminal, or collected from the target's SNS or email, a job search relay server, a school or national administrative agency server that stores and manages information about the target. The first job recommendation unit 6320 may include a word extraction unit 6322 , a vectorization unit 6324 , a similarity calculation unit 6326 , and a similar job ability determination unit 6328 .

The word extraction unit may extract competency-related words from the input job capability 6500 of the subject. The word extractor 6322 may include a morpheme analyzer for dividing sentences in the input job competency 6500 into words of a morpheme unit and tagging the part-of-speech. In an embodiment, the word extraction unit 6322 may extract a noun-signing morpheme after morpheme analysis, remove stop words, and then apply N-grams such as bi-grams in the case of a noun continuation. . The word extraction unit 6322 may extract only words corresponding to the words 6128 defined in the first filter 6220 from among the inputted words of the job ability 6500 .

The vectorization unit 6324 may vectorize the words extracted from the job ability 6500 input by the word extraction unit 6322 according to the arrangement order of the words 6128 defined in the first filter 6220 . In the first filter 6220, the order of the words 6128 is arranged in a column unit. According to the arrangement order of the words 6128 defined in the first filter 6220, the vectorization unit 6324 extracts k (k is an integer greater than or equal to 0 and less than or equal to m) extracted from the input job capability 6500. Words can be converted into row vectors of size 1×m. In this case, in the row vector transformed from the words extracted from the input job capability 6500, k components may have a size greater than 0, and the remaining (m-k) components may have a value of 0. The component value of each of the k words may be determined as a value corresponding to a term frequency (TF) of the input job capability 6500, and a value irrespective of the frequency by applying a binary score. may be determined as When the word frequency (TF) is used, the normalized term frequency may be used to calculate similarity (eg, cosine similarity). When using a binary score, it can be applied by taking the square and then dot product.

For example, assuming that the word 'architectural design' is included twice in the input job competency 6500, and the word corresponding to 'architecture design' in the first filter 6220 is defined as the 12145th column, the word The 12145th component value of the primary vector generated by the vectorization unit 6324 is determined to be '1' when the frequency of the word is not considered, and the primary generated by the vectorization unit 6324 when considering the frequency of words The value of the 12145th component of the vector may be determined to be '2'. Thereafter, a normalized row vector (feature vector) may be generated by a vector normalization operation. The similarity calculator 6326 compares the row vector (feature vector) generated from the job ability 6500 input by the vectorization unit 6324 and the job seeker's feature vector and the capability units based on the first filter 6220 . similarity can be calculated. In an embodiment, the similarity calculator 6326 may calculate a similarity (eg, cosine similarity) between the job seeker's feature vector and a row vector (a competency unit vector) of each of the capability units.

In an embodiment, the cosine similarity may be calculated as a value obtained by dividing the feature vector of the subject and the row vector of each of m job competency units by the size of the feature vector and the row vector size of the job competency unit after dot product. When the feature vector is normalized, the operation of dividing by the size of the feature vector may be omitted. The similarity calculating unit 6326 may calculate the similarity with the feature vector of the subject for each of n job competency units belonging to one major category. The similar job ability determination unit 6328 determines the priority of job competency units in the order of the highest similarity with the target's feature vector among n similarity values between the target's feature vector and the job competency units, and recommends the job to be recommended to the target. can be decided In addition, it is possible to provide certification information required for the recommended job or training information necessary to improve competency.

In an embodiment, the similar job capability determining unit 6328 may determine a recommended job from one or a plurality of major categories in an order of high similarity between the feature vector of the subject and the job capability units for each major classification. Alternatively, when the subject makes an input for selecting a specific large classification using the subject interface, a job (job competency unit) belonging to the large classification may be recommended based on the filter only for the selected large classification. In the above, the case where various filters are created for each major category and job recommendation is described as an example, and it is also possible to recommend a job by creating one filter for all job competency unit documents belonging to all major categories.

Hereinafter, an embodiment of creating the second filter 240 to recommend a job will be described. 24 is a block diagram of a second filter generating unit constituting the job recommendation apparatus according to an embodiment of the present invention. Referring to FIGS. 18 and 24 , the second filter generating unit 6140 generates the second filter of a two-dimensional matrix representing the degree of association between job competency units 6124 extracted from a dictionary 6125 including a plurality of job competency units. Two filters 6240 may be created.

The second filter generating unit 6140 may include a dictionary collection building unit 6122 , a word extracting unit 6146 , and a relationship analysis unit 6148 between classifications. The dictionary collection building unit 6122 may extract job capability units 6124 from the job capability related data 6400 . In an embodiment, the dictionary collection building unit 6122 may extract job capability units 6124 from job capability related data. The number of extracted job competency units 6124 may be n. The n job competency units 6124 may be utilized to calculate the second filter.

The word extraction unit 6146 may extract words from all of the n job competency units 6124 . The word extractor 6146 may include a morpheme analyzer that divides the sentences in the job competency units 6124 into words of the morpheme unit and tags the part-of-speech. In an embodiment, the word extraction unit 6146 may extract a noun-signing morpheme after morpheme analysis, remove stop words, and then apply N-grams such as bi-grams in the case of a noun continuation. .

The relationship between classification analysis unit 6148 may analyze the degree of correlation between the job competency units 6124 based on the similarity of the words extracted from the job competency units 6124 . In an embodiment, the relationship analysis unit 6148 between classifications may analyze the correlation between the n job competency units 6124 based on a Term Frequency Inverse Document Frequency (TF-IDF).

In the embodiment, when m words are extracted from all job competency units 6124 by the word extracting unit 6146, the inter-class relationship analysis unit 6148 determines that the order of all words is mapped to the column order. A row vector of size ×m can be created. The classification relationship analysis unit 6148 generates a row vector for each job competency unit 6124 from a set of words extracted from each job competency unit 6124, As many (n) number of row vectors can be generated. Then, for all n × n combinations between the n job competency units 124, for example, the cosine similarity between the row vectors of the two job competency units 6124 is calculated according to Equations 1 to 4 above. By calculating, the relationship between the job competency unit documents 6124 may be analyzed. Relationship values between the job competency unit documents 6124 may be represented by a two-dimensional matrix of n×n size, and this two-dimensional matrix may be stored as the second filter 240 .

25 is an exemplary diagram of a second filter according to the first embodiment of the present invention. 24 and 25 , the second filter 6240 includes job competency units 6124 (a1, a2, ..., an-1, an) in a row unit (a first unit). It may be arranged and expressed as a two-dimensional matrix in which job competency units 6124 (a1, a2, ..., an-1, an) are arranged in a column unit (a second unit). The job competency units 6124 (a1, a2, ..., an-1, an) may be defined in the same arrangement order in a row unit and a column unit. Each component value of the two-dimensional matrix of the second filter 6240 may be a similarity value between a plurality of row vectors generated from a plurality of word sets each extracted from a plurality of job competency units. Since the diagonal component of the second filter 6240 is a similarity value between a plurality of same row vectors extracted from the same job competency unit, the component value becomes 1. The second filter 6240 may be a set of row vectors having a size of 1×n of each of the n job unit capability units. A large component value in the second filter 6240 means that the similarity between the corresponding two job competency units is large, and in the opposite case, the similarity between the two job competency units is small.

26 is a configuration diagram of a second job recommendation unit according to the first embodiment constituting the job recommendation apparatus according to the embodiment of the present invention. 19, 25 and 26 , the second job recommendation unit 6340 may recommend one or more jobs to the target based on the target's input job ability 6500 and the second filter 6240. . The second job recommendation unit 6340 may include a word extraction unit 6342 , a job ability analysis unit 6344 , and a similar job ability determination unit 6346 .

The word extraction unit 6342 may extract competency-related words from the input job capability 6500 of the subject. The word extractor 6342 may include a morpheme analyzer for dividing sentences in the input job competency 6500 into words of a morpheme unit and tagging the part-of-speech. In an embodiment, the word extraction unit 342 extracts noun-signing morphemes after morpheme analysis, removes stop words, and applies N-grams such as bi-grams in the case of noun continuation, and the subject Words related to job competency units can be extracted.

The job capability analysis unit 6344 determines one or more first job capability units related to the subject from the words extracted from the input job capability 6500, and based on the second filter 6240, the first job capability unit and One or more second job competency units having a high degree of similarity may be extracted. As another example, when a level score for the first job competency unit is input by the subject, or completion information such as career or educational program related to the first job competency unit is input, the job competency analysis unit 6344 performs the second filter Based on the similarity matrix relationship between job competency units in (6240), the level score or completion information for the first job competency unit may be reflected as the level score or completion information for other job competency units.

For example, in the second filter 6240 shown in FIG. 25 , the degree of relevance of the anth job competency unit (the second job competency unit) to the an-1st job competency unit (the first job competency unit) is 0.30. , if the subject's level score related to the 1st job competency unit is entered as 90 points or measured/evaluated or analyzed, the subject's level score can be reflected as 27 points also for the anth job competency unit. Accordingly, based on the second filter 6240, the job search-related history for the first job competency unit of the subject can be extended to other job competency units, and various job competency units suitable for the subject can be recommended to the subject. .

At least some of the methods according to an embodiment of the present invention can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. Computer-readable recording media include volatile memory such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), ROM (Read Only Memory), PROM (Programmable ROM), EPROM (Electrically Programmable ROM), Nonvolatile memory such as Electrically Erasable and Programmable ROM (EEPROM), flash memory device, phase-change RAM (PRAM), magnetic RAM (MRAM), resistive RAM (RRAM), ferroelectric RAM (FRAM), floppy disk, hard disk, or The optical reading medium may be, for example, a storage medium such as a CD-ROM or DVD, but is not limited thereto.

At least some of the methods according to an embodiment of the present invention may be implemented as software (eg, a program) including instructions stored in a computer-readable storage medium. At least some of the job recommendation method according to an embodiment of the present invention may be implemented by calling a stored instruction from a storage medium and executing a process according to the called instruction. Instructions may include code generated or executed by a compiler or interpreter. The storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' means that the storage medium does not include a signal and is tangible, and does not distinguish that data is semi-permanently or temporarily stored in the storage medium.

The description of the present invention described above is for illustration, and those of ordinary skill in the art to which the present invention pertains can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. For example, each component described as a single type may be implemented in a dispersed form, and likewise components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

Claims (3)

A method of recommending a new business to a company in an electronic device comprising a processor and a memory for storing instructions executed by the processor, the method comprising:
receiving, to a processing unit, the competency of one or more employees from a competency measurement tool consisting of a required job competency and one or more similar job capabilities similar to the required job competency;
calculating, by the processing unit, a competency of an enterprise from the competency of the one or more employees;
comparing, by the processing unit, the capabilities of one or more other enterprises stored;
deriving, by the processing unit, a similar capability enterprise having a capability similar to that of the enterprise among the one or more other enterprises;
A new business recommendation method for providing, by the processing unit, business information of the derived similar capability company to the company. A method of recommending a company, job or job to a subject in an electronic device including a processor and a memory for storing instructions executed by the processor, the method comprising:
receiving, to a processing unit, a competency of the first measured subject from a capability measurement tool consisting of a required job capability and one or more similar job capabilities similar to the required job capability;
comparing, by the processing unit, the capabilities of the first to-be-measured and the capabilities of the stored one or more measured persons;
extracting, by the processing unit, a second subject having similar capabilities to that of the first subject from among the one or more stored subjects;
A company, job, or job recommendation method for recommending, by the processing unit, any one or more of a company, a job, or a job to the first measured person from any one or more of company information, job information, and job information of the second target. A method of deriving a recommended curriculum in an electronic device comprising a processor and a memory storing instructions executed by the processor, the method comprising:
receiving input from the terminal to the processing unit;
deriving, by the processing unit, at least one similar job competency similar to the required job competency;
providing, by the processing unit, the one or more similar job capabilities to the terminal;
receiving, by the processing unit, one or more additional job capabilities selected from the one or more similar job capabilities from the terminal;
deriving, by the processing unit, the required job capability of the company from the required job capability and the additional job capability;
and deriving a recommended curriculum for the educational institution by comparing, by the processing unit, the required job capability of the company and the job capability according to the curriculum of the educational institution.

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