KR20200129028A - How to create a job description, how to create a competency measurement tool, how to recommend a new business, and how to recommend a curriculum - Google Patents

Abstract

The present invention discloses a method for creating the job description, a method for creating a capacity measurement tool, a method for recommending a new business, and a method for recommending curriculum. According to one embodiment of the present invention, the method for creating the job description comprises the steps of: receiving job performance required from a terminal; deriving one or more pieces of similar job performance similar to the required job performance; and creating the job description from the required job performance and the similar job performance.

Description

Job description generation method, competency measurement tool generation method, new business recommendation method, and curriculum recommendation method{omit}

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.

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 regardless of job duties, and in the hiring process, there are many cases where sufficient job descriptions or clear recruitment standards have not been presented. In the hiring process of such companies, there are many cases in which personnel who are not suitable for the job are hired, and there is a problem that a lot of time and money are incurred for job training before the recruited personnel are put into practice.

In particular, there are many cases in which human resources who have been put into practice do not adapt to their jobs and resign or change jobs early, which can cause further damage to companies and human resources.

Meanwhile, the government introduced the National Competency Standards (NCS) in order to check the knowledge, skills, and abilities required for each job in the industrial field and use it as an important standard when hiring talent. It aims to spread to private enterprises as well as institutions, public institutions, and educational institutions.

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

In particular, it is pointed out that there is a gap between the actual jobs experienced or experienced by workers or retirees with a history of working in companies and departments and job classification through NCS.

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

The present invention provides a method to solve the problem of job mismatching.

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

The present invention provides a method of providing a reasonable basis for salary negotiation.

The present invention provides a method by which an educational institution can reflect the actual needs of companies in setting a curriculum.

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

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

A method of generating a job description according to another viewpoint includes the steps of receiving an input of a job capability required from a terminal; Deriving one or more similar job abilities similar to the required job abilities; Providing the one or more similar job capabilities to the terminal; Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal; And generating a job description from the required job competency and the additional job competency.

In an embodiment, after the step of receiving 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; It 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 on the company.

In one embodiment, the step of deriving the similar job competency includes: recommending the similar job competency based on the input job competency and a filter, and the filter is composed of data related to required competence according to the job. Generated based on a dictionary including a plurality of job competency units, at least one of a degree of association between the job competency units and words extracted from the data constituting the job competency units and a degree of correlation between the job competency units It may include.

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

In an embodiment, the filter includes a two-dimensional matrix in which job competency units are arranged in a first unit of a row unit and a column unit, and the job competency units are arranged in another second unit, and , The two-dimensional matrix has a degree of relationship between a first job competency unit associated with each job competency unit of the first unit and a second job competency unit associated with each job competency unit of the second unit as each component value. The present invention provides a method of generating a capability measurement tool. In an embodiment, the step of receiving an input of a job capability required from a terminal; Deriving one or more similar job abilities similar to the required job abilities; And generating a competency measurement tool from the required job competency and the similar job competency.

A method for generating a capability measurement tool according to another viewpoint includes the steps of receiving a job capability required from a terminal; Deriving one or more similar job abilities similar to the required job abilities; Providing the one or more similar job capabilities to the terminal; Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal; And generating a competency measurement tool from the required job competency and the additional job competency.

In an embodiment, after the step of receiving 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; It 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 an embodiment, the step of receiving an input of a job capability required from a terminal; Deriving one or more similar job abilities similar to the required job abilities; Generating a competency measurement tool from the required job competency and the similar job competency; Receiving competencies of one or more employees from the competency measurement tool; Calculating the capabilities of the enterprise from the capabilities of the one or more employees; Comparing the stored capabilities of one or more different enterprises; Deriving a similar competency company having a similar capability to the company from among the one or more other companies; The business information of the similar competency company derived above is provided to the company.

The present invention provides a method of recommending a company, job or job. In an embodiment, the method further comprises: receiving a job capability required from a terminal; deriving one or more similar job capabilities similar to the requested job capability; Generating a competency measurement tool from the required job competency and the similar job competency; Receiving a capability of a first subject from the capability measurement tool; Comparing the capabilities of the first testee with the capabilities of the stored at least one subject; Extracting a second subject whose capabilities are similar to that of the first subject from among the stored at least one subject; Any one or more of a company, a job, or a job is recommended to the first subject from any one or more of company information, job information, or job information of the second subject.

The present invention provides a curriculum recommendation method. In an embodiment, the step of receiving an input of a job capability required from a terminal; Deriving one or more similar job abilities similar to the required job abilities; Providing the one or more similar job capabilities to the terminal; Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal; Deriving a required job competency of the company from the required job competency and the additional job competency; And comparing the required job ability of the company with the job ability according to the curriculum of the educational institution and recommending a curriculum for the educational institution.

According to an embodiment of the present invention, a job mismatch problem may 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 can write a detailed and specific job description for the job in the hiring field.

According to an embodiment of the present invention, in negotiating an annual salary, a reasonable basis may be provided.

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

The 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 from the present specification and the accompanying drawings.

1 is a diagram showing an apparatus according to an embodiment of the present invention.
FIG. 2 is a flow chart illustrating a process of recommending a similar job competency by a processing unit and deriving a required job competency from an input requested job competency according to an exemplary embodiment.
FIG. 3 is a flowchart illustrating a process unit generating a job description of a company from an input request capability according to an embodiment and connecting it to a job posting.
FIG. 4 is an exemplary screen of a user terminal showing an extension from a job competency input from a user to a similar job competency from the similar job recommendation unit 31 according to an exemplary embodiment.
FIG. 5 illustrates a method of creating a competency measurement tool from input job competency according to an embodiment and negotiating an annual salary using it, recommending a new business to a company, or recommending an employee, and a new company or a new job or a new job to an employee. It is a flow chart showing how to recommend.
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 showing an example of comparing company capabilities in recommending a new business to 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.
12 is a graph showing an example of recommending a new company or a new job or a new job to a worker 1;
13 is a flow chart illustrating a method of recommending an education curriculum to an educational institution according to an embodiment.
14 is a flow chart illustrating a method of recommending an education curriculum to an educational institution according to another embodiment.
FIG. 15 is a block diagram showing a database that holds information on required job competency of a company and curriculum of an educational institution.
16 is a graph showing an example of recommending a curriculum for educational institutions.
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 generating unit constituting a job recommendation device 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 configuration diagram of a first filter generator constituting a job recommendation device according to an embodiment of the present invention.
21A and 21B are diagrams illustrating a document defining the job competency units 6112 of the NCS as an example of a dictionary including a plurality of job competency units.
22 is an exemplary diagram of a first filter according to the first embodiment of the present invention.
23 is a block diagram of a first job recommendation unit constituting a job recommendation device according to the first embodiment of the present invention.
24 is a configuration diagram of a second filter generator constituting a job recommendation device 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 that configures a job recommendation device according to an 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 embodiments to be described later in detail together 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. Even if not defined, all terms (including technical or scientific terms) used herein have the same meaning as commonly accepted by universal technology in the prior art to which this invention belongs. General descriptions of known configurations may be omitted so as not to obscure the subject matter of the present invention. In the drawings of the present invention, the same reference numerals are used as much as possible for the same or corresponding configurations.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present application, terms such as "comprise", "have" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or a combination thereof described in the specification. It is to be understood that the possibility of the presence or addition of other features, numbers, steps, actions, components, parts, or combinations thereof, or any further features, is not excluded in advance.

The'~ unit' used throughout this specification is a unit that processes at least one function or operation, and may mean, for example, a hardware component such as software, FPGA, or ASIC. However,'~ part' is not limited to software or hardware. The'~ unit' may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors. As an example,'~ unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, and subs. Routines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The components and functions provided by the'~ unit' may be performed separately by a plurality of elements and the'~ units', or may be integrated with other additional elements.

In the present invention, the term'job ability' is used. The term'job competency' used in the present invention refers to the knowledge, skills, and attitudes required for job performance in the industrial field, for example, the National Competency Standard (NCS) or the Korea Vocational Competency Development Institute. Standards) can be defined as a unit of competency according to the concept structured. Specifically, information on NCS can be obtained from https://www.ncs.go.kr/, and the major classification (24) classified by referring to the Korean Employment Classification of Occupations (KECO) → Sub-classification (79) → Sub-classification (253) → According to the sub-classification (1,001), knowledge and skills of the elements of the competency unit provided in the NCS competency units (reports) included in each NCS competency unit included in the NCS competency units provided in each subclass , Attitude can be. On the other hand, job competency should not be interpreted as a concept limited to the aforementioned NCS competency unit or competency unit element. Job competency may mean all the items to be stated, such as the name, definition, basic job competency, evaluation items, performance standards for each competency unit element defined in the NCS. In addition, job competency includes knowledge, skills, attitudes, etc. required for job performance in an industrial site not described above, and may be a concept classified according to certain criteria.

1 is a diagram showing a device 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 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, laptop PC, netbook computer, workstation, server, personal digital assistant (PDA), portable multimedia player (PMP) ), an MP3 player, a mobile medical device, a camera, or a wearable device. In addition, although not described, another terminal may be provided that is provided to the user to receive information from the user.

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. , An employee recommendation unit 37, a company/job/job recommendation unit 38, and an education 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 to 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 IrDA (infrared data association)) or long-distance communication (eg, cellular It may be a network, the Internet, or a computer network (eg, LAN or WAN), but is not limited thereto. Wired/wireless network interfaces communicate according to various communication standards such as IEEE, zigbee, 3rd Generation (3G), 3rd Generation Partnership Project (3GPP), Long Term Evoloution (LTE), 5th Generation (5G), etc. It may be configured as a network interface that performs, but is not limited thereto.

FIG. 2 is a flow chart illustrating a process of recommending a similar job competency by a processing unit and deriving a required job competency from an input requested job competency according to an embodiment. FIG. 3 is a flow chart illustrating a process unit generating a job description of a company from an input job capability according to an embodiment and connecting it to a job posting. The use of the same reference numerals as in FIG. 2 in FIG. 3 is the same as the description of FIG. 2. It can be replaced by the description of FIG. 2. With reference to FIG. 3, the similar job recommendation unit 31, the company information update unit 32, and the job description generation unit 33 will be described.

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

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

First, the required job capability is input from the terminal 20 (1110). for example. The first job competency 118 is input from the terminal 20 (if there are a plurality of required job competencies, a plurality of job competencies will be inputted). The similar job recommendation unit 31 derives a similar job competency from the inputted first job competency 118 (1210). The similar job recommendation unit 31 generates a first similar job competency and a second similar job competency similar to the first job competency (if there are dozens or more of similar job competencies, the number may generate dozens or more of similar job competencies. will be). In the present invention, the term “similar job competency” is used, which is a similar job recommended by the similar job recommendation unit 31, and for convenience, the term “similar job competency” is used, and is substantially the same concept as job competency.

The similar job recommendation unit 31 generates a similar job capability list 125 using this (1220), and provides it to the terminal 20 again (1230). The terminal 20 user is a similar job competency list 125 including a first similar job competency 126 and a second similar job competency 127 expanded from the first job competency 118 input to the terminal 20 Will be provided. The similar job competency list 125 is a list of similar job competencies similar to the input job competencies, and may be, for example, a list of first and second similar job competencies.

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

It is very important for companies to expand their required job skills. For example, if the job performed through the first job competency accounts for 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 job position, so it is not clearly understood at the time of writing the job description. This correction has serious consequences, especially for small and medium-sized workforce. For example, B, who joined Company A, understands that only the first job that requires only the first job ability will be his job in a specific job description written by Company A, and after joining Company A, Company A 2 It is a difficult situation to keep attending Company A due to reasons such as demanding job skills and requesting a second job in addition to the first job, and if Company A was unable to do the second job, then another Such situations as hiring occur frequently in small and medium-sized enterprises that lack manpower.

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

The processing unit 30 generates a draft job description from the input required job competency and the weight for each required job competency. The draft job description may be generated from detailed data (e.g., detailed description of knowledge, skills, attitudes, etc.) derived from data stored according to each job competency and previously entered data such as a previously entered job description of a company.

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 is provided with a draft job description from the terminal 20, and may appropriately modify the draft job description as necessary (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 created through the above-described method extends to job abilities that companies are not aware of, so that the job description can contain more accurate required job abilities, and by providing a draft job description, hiring managers with insufficient writing ability By providing help to people, the job description can be written more precisely and accurately. As a result, communication between job seekers and recruiters can be facilitated and job mismatch problems can be resolved.

FIG. 5 illustrates a method of creating a competency measurement tool from input job competency according to an embodiment and negotiating an annual salary using it, recommending a new business to a company, or recommending an employee, and a new company or a new job or a new job to an employee. It is a flow chart showing how to recommend. The use of the same reference numerals as in FIG. 2 in FIG. 5 is the same as the description of FIG. 2. It can be replaced by the description of FIG. 2. An example will be described. In one embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown in the user line 100, and the items processed by the processing unit 100 are displayed in the processing unit line 200. It is illustrated, and the information received from the employee through the terminal 20 or another terminal is shown in the employee line 300. An annual salary negotiation method, a new business recommendation method, an employee recommendation, and a company/job/job recommendation method for an employee according to an embodiment will be described with reference to FIG. 5. In addition, with reference to FIG. 5, the competency sheet generation 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 selecting the required job competency from the similar job competency recommended from the same method as the description using FIG. 2 to expand the required job competency and selecting the required job competency (1000), the competency sheet generation unit 34 inputs A draft competency measurement tool is generated from the received required job competency and the weight by required job competency (30). The competency measurement tool is a tool to measure an employee's job competency, and may include several questionnaires. For example, the questionnaire may be a question extracted from the performance criterion included in the NCS capability unit (report) (see FIG. 21). The data included in the classification according to each job competency may include evaluation elements of job competency, and these evaluation elements can be automatically extracted to generate a draft competency measurement tool. For example, a draft can be created as shown in [Table 1] below. The draft competency measurement tool may be provided by adjusting the selection of evaluation elements according to the difficulty of each evaluation element, taking into account weights for each required job competency.

Draft exemplary competency measurement tool Job ability Evaluation factor Select Detail selection Analyzing the development environment of partner countries It is possible to analyze the feasibility of promoting public development assistance by reviewing the policies, systems, and laws of partner countries. The comparative advantage of Korea can be analyzed by grasping the support characteristics, operational strategies, and implementation plans of other donors who support partner countries. By analyzing the stakeholders of Korea and the partner countries, it is possible to suggest a plan for smooth cooperation. It is possible to analyze the specific matters of project execution by investigating the domestic and foreign environment and other risk factors of the partner country for official development assistance. Analyzing 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 outside the organization. Assumptions and prerequisites can be identified through project environment analysis. Project work documents and contracts, the main inputs of the project site, can be reviewed in advance. Investigating 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. In order to select an effective project, it is possible to define the size, complexity, list, and analysis target, and establish an effective analysis plan. A feasibility study report can be prepared based on the results of the project feasibility study.

In the example according to [Table 1] above,'Analyzing the partner country's development environment' corresponds to the first job competency,'Analyzing the project environment' corresponds to the first similar job ability, and'Investigating the project feasibility' is the first. 2 May correspond to similar job capabilities.

According to an embodiment, the evaluation elements are exemplified to include only evaluation elements according to the performance criteria, but from the information in the [Knowledge] column,'We know the development policy and support status for partner countries such as international organizations bilateral aid organizations and NGOs. Questions such as'I have the ability to communicate in foreign languages' from the items in the [Skill] column, or items such as'I have the will to think objectively and logically' from the items in the [Attitude] column. You can also derive.

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

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

Competency measurement tool Job ability Evaluation factor Yes usually no Analyzing the development environment of partner countries It is possible to analyze the feasibility of promoting public development assistance by reviewing the policies, systems, and laws of partner countries. The comparative advantage of Korea can be analyzed by grasping the support characteristics, operational strategies, and implementation plans of other donors who support partner countries. By analyzing the stakeholders of Korea and the partner countries, it is possible to suggest a plan for smooth cooperation. It is possible to analyze the specific matters of project execution by investigating the domestic and foreign environment and other risk factors of the partner country for official development assistance. Analyzing 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 outside the organization. Assumptions and prerequisites can be identified through project environment analysis. Project work documents and contracts, 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 his or her competence (3310). For example, an employee may be provided with a competency measurement tool in the year of employment to measure competency, and then the competency improvement may be measured by measuring the competency again from the competency measurement tool for salary negotiation in the next year. This can serve as a basis for negotiating an annual salary (3500). In addition, the competencies measured by the employee themselves using the competency measurement tool may lack objectivity and can be modified by the manager. The processing unit 30 may provide information to an administrator by providing a notification on an evaluation result indicating an excessive increase.

From the results of measuring employee competency, the new business recommendation department (35) recommends a new business to the company (3600). In addition, the employee recommendation unit 37 recommends employees to the company from the measured capabilities (3700). In addition, the company/job/job recommendation unit 38 recommends a company or job or job to the employee from the measured competency (4000). 6 shows a matter of integrating data in order to derive an employee's job competency from the employee's job competency, and FIGS. 7 and 8 show a new business recommendation unit 35 recommending a new business or an employee recommendation unit 37 Shows where data is being incorporated to recommend staff. 5 will be described with further reference to FIGS. 6, 7, and 8.

The data integration unit 36 may integrate data. For example, by integrating the job capability 1, job capability 2, and job capability 3 of employee 1 measured from employee 1 through the data integration unit 36, the job competency of employee 1 may be derived. In addition, by integrating the data for employee 2, employee 3, and employee 4 in the same way as employee 1, the job competency of employee 2, job competency of employee 3, and job competency of employee 4 can be obtained. The job competency of a company can be derived by integrating the job competencies of employees belonging to the company. For example, the job competency of the company 1 having employees 1 and 2 is as shown in FIG. 8. It can be derived in the form of integrating employee 1's job competency and employee 2's job competency. Meanwhile, whether or not a company has a specific machine may be corrected as the company's job capability data.

The job competency of Company 1 thus derived is stored in the database 40. In addition, 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 Firm 1, it is possible to propose a new business to Firm 1 by providing business information of the company with the most similar job competency through comparison with Firm 2, Firm 3, ... Firm n. I can.

As an example, FIG. 10 is a graph showing an example of comparing company capabilities in recommending a new business for company 1. FIG. As referenced through FIG. 10, Firm 1 has similar job capabilities to Firm 3, and by providing Firm 3's business field to Firm 1, a new business can be recommended for Firm 1. Conversely, by providing Firm 1's business field to Firm 3, a new business can be recommended to Firm 3. In addition, if Firm 1 is engaged in a business field similar to Firm 2, whereas Firm 1's job capability 5 is relatively weak compared to Firm 2, then the employee with excellent job capability 5 compared to Firm 1 (applicants or other companies Staff, etc.).

11 is a block diagram illustrating an example in which a database stores job competency data of a professional. 12 is a graph showing an example of recommending a new company or a new job or a new job to a worker 1; Referring to FIGS. 11 and 12, the company/job/job recommendation unit 38 will be described. As referenced through FIG. 11, information of individual employees stored through the above-described examples may be defined and stored as a professional person. When an employee belongs to a company, it is an employee, but when viewed from a social standpoint, it corresponds to a professional person. Therefore, in the description of the present invention, an employee is viewed from a position outside the company and defined as a professional person. For example, the database 40 may store job competency data of a professional 1, a professional 2, a professional 3, ... professional n. Each professional may belong to the same company, may belong to different companies, may be freelancers, business owners or self-employed persons, or may have other undefined jobs. As referenced through FIG. 12, when comparing the job competencies of professional 1, 2, and 3, professional 1 has similar job competency as professional 2, and provides information on the company or job or job of professional 2 to professional 1 By doing so, it is possible to recommend a company or a job or job to a professional person 1. Conversely, by providing the company or job or job information of the employee 1 to the employee 2, it is possible to recommend the company or job or job information to the employee 2.

13 is a flow chart illustrating a method of recommending an education curriculum to an educational institution according to an embodiment. In FIG. 13, the use of the same reference numerals as in FIG. 2 is the same as the description of FIG. 2, and therefore, the description of FIG. 2 may be substituted. In one embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown in the user line 100, and the items processed by the processing unit 100 are displayed in the processing unit line 200. It is shown, and the information received by the educational institution through the terminal 20 or another terminal is shown in the educational institution line 400. Referring to FIG. 13, a method of recommending an education curriculum to the education curriculum recommendation unit 39 and educational institutions will be described.

In one embodiment, in addition to creating the job description described through FIG. 2 and connecting it to the employment announcement, the education curriculum recommendation unit 39 may recommend the education curriculum based on the necessary job skills required by the company. Yes (5000).

14 is a flow chart illustrating a method of recommending an education curriculum to an educational institution according to another embodiment. In FIG. 14, the use of the same reference numerals as in FIG. 4 is the same as the description of FIG. 4, and thus, the description of FIG. 4 may be substituted. In one embodiment, information received from a user (eg, a hiring manager of a company) through the terminal 20 is shown in the user line 100, and the items processed by the processing unit 100 are displayed in the processing unit line 200. It is shown, and the information received by the educational institution through the terminal 20 or another terminal is shown in the educational institution line 400. Referring to FIG. 14, a method of recommending an education curriculum to the education curriculum recommendation unit 39 and educational institutions will be described.

In one embodiment, by creating 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, In addition to recommending jobs/jobs), the education curriculum recommendation unit 39 may recommend an education curriculum based on the required job competency required by the company and the job competency of the company in the actual industrial field (5000).

Fig. 15 is a block diagram showing a database holding the required job competency of a company and curriculum information of an educational institution, and Fig. 16 is a graph showing 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 holds job competencies required by companies and job competencies according to the curriculum of educational institutions. 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. As shown in FIG. 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, while companies strongly demand job competency 1, job competency 3, and job competency 5, the curriculum of educational institutions rarely provides training for job competency 5, whereas companies require it. You may be educating about job skills 2 that you do not do. The educational curriculum recommendation unit 39 according to an embodiment of the present invention may propose a curriculum that increases the proportion of job competency 5 to an educational institution, and proposes a curriculum that reduces the proportion of job competency 2.

Hereinafter, a method of extracting a similar job competency from the input job competency 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 generation unit 6100, a filter 6200, and a job recommendation unit 6300.

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

The dictionary collection (hereinafter referred to as the dictionary collection) that includes a plurality of job competency units includes job competency extracted from job-related specific data (e.g., job seeker's self-introduction, job description, classification data according to NCS, etc.) It refers to a set of data that is organized by classifying related data (eg, words, sentences, etc. related to job competency) 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 to the subject based on the subject-related data and the filter 6200 generated by the filter generating unit 6100.

In an embodiment, a dictionary including a plurality of job competency units includes documents related to competency units according to job classification defined according to the National Competency Standards (NCS) classification, and words extracted from these documents. May contain a collection of words. The dictionary is composed of a plurality of job competency units. The definition of a job competency unit does not simply mean job competence itself, but should be understood as a concept of a data set that includes data related to the competency required by each job competency. It is not simply defined in words, but each job It should be interpreted as a concept including a vocabulary including a plurality of words for each ability or a job description, job description, etc. defined as sentences or their indexes.

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

18 is a block diagram of a filter generating unit constituting a job recommendation device 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 generator 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 2D matrix indicating a degree of association between each job competency unit.

The first job recommendation unit 6320 may recommend a job to a target person 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 a target person 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 configuration diagram of a first filter generator constituting a job recommendation device according to an embodiment of the present invention. 18 and 20, the first filter generation unit 6120 includes a dictionary construction unit 6122, a word extraction unit 6126, a correlation analysis unit 6130, and a normalization unit 6132. Can include. The dictionary collection construction unit 6122 may construct a dictionary collection 6125 by dividing and extracting data according to job competency units from job competency-related data 6400 and integrating the extracted data according to job competency units. In an embodiment, the dictionary collection construction unit 6122 may extract pdf documents defining job competency units for each of 24 NCS major categories. The number n of the job competency units 6112 may be different, and in one embodiment, n denotes the number of job competency units 6112 belonging to the corresponding classification. PDF documents according to n job competency units 6112 belonging to the same classification can be used to calculate the first filter 6220 for the corresponding classification, and as many as the number of classifications (for example, the NCS classification system According to the large classification, 24) of first filters 6220 may be calculated.

21A and 21B are diagrams illustrating a document defining the job competency units 6112 of the NCS as an example of a dictionary including a plurality of job competency units. For each job competency unit,'classification number','name','level','definition','basic vocational competence','evaluation items','performance standards' 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 extractor 6126 may extract words 6128 from all of the n job competency units. In an embodiment, the word extracting unit 6126 may include a morpheme analyzer that divides sentences in a document defining the job competency units 6112 into words of a morpheme unit and tagging parts of speech. In an embodiment, the word extracting unit 6126 excludes stop words from sentences, and N-grams such as uni-gram and/or bi-gram (N is an integer of 2 or more). ) To extract words.

The association analysis unit 6130 may analyze a degree of association between the job competency units 6112 defined in the dictionary 6125 and the words 6128 extracted from the job competency units 6112. In an embodiment, the correlation analysis unit 6130 is based on TF-IDF (Term Frequency Inverse Document Frequency), based on n job competency units 6112 and m words extracted from n job competency units 6112 It is possible to analyze the degree of association between the fields 6128. The degree of association between the job competency units 6112 and the words 6128 may be calculated according to Equations 1 to 3 below, or may be calculated according to Equations 1, 2, and 4 below.

[Equation 1]

[Equation 2]

[Equation 3]

[Equation 4]

In Equations 1 to 4,'tfidf(t,d,D)' is the 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 the document d, and'df(t,d)' is the number of documents containing the word t among all documents Represents.

The relevance analysis unit 130 applies not only TF-IDF, but also non-negative matrix factorization (NMF) and word embedding technologies such as Word2Vec/Doc2Vec, and documents job competency units. And, it is also possible to analyze the degree of association between words extracted from job competency unit documents.

When the number of words 6128 extracted from n job competency units 6112 is m, the associations between job competency units 6112 and words 6128 can be expressed in a matrix of size n×m. have. The normalization unit 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 matrix of size n×m is normalized to 1. The set value may be different depending on each component of the n×m matrix. Also, the set value may be different depending on the normalization method. On the other hand, in the present embodiment, the matrix is normalized to have a size of 1, but it may be modified to an appropriate size such as 10 or 100. Accordingly, a normalized first filter 6220 may be generated for each major classification. By normalizing the first filter 6220, it is possible to prevent the priorities of recommended jobs belonging to different major categories from being influenced by different matrix sizes between major categories, and recommended jobs as competency units within a specific major category (large category with a large matrix size) It is possible to prevent the concentration of people and recommend a job (competency unit) suitable for job seekers not only in the large category with a large matrix size, but also in the large category with a small matrix size.

22 is an exemplary diagram of a first filter according to the first embodiment of the present invention. 20 and 22, the first filter 6220 includes job competency units 6112 (a1, a2,...,an-1,an) in a row unit (first unit). Words (6128) (v1,v2,...,vm-1,vm) that are arranged and extracted from the job competency units (6124) of the dictionary (6125) in a column unit (second unit) This can be expressed as an arrayed two-dimensional matrix. Each component value of the 2D matrix of the first filter 6220 may be a value obtained by dividing the TF-IDF value of a word by the matrix size and normalized. The first filter 6220 may be regarded as a set of row vectors having a size of 1×m for each of n job unit capabilities. The number m of words 128 may be approximately tens of thousands to hundreds of thousands of words.

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

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

For example, the filter update unit 6160 finds a new classification number or a job competency unit having a new name in a dictionary including a plurality of job competency units collected by crawling, or the classification numbers of the existing job competency units or If there is a deleted classification number or name among the names, or by a crawling technique, the job competency unit's'vocational basic competency','evaluation items','performance criteria','knowledge' by job competency unit element When it is determined that information such as,'technology','attitude', etc. has been changed, it is determined that the dictionary including a plurality of job competency units has been changed or updated, and the filter may be updated.

23 is a block diagram of a first job recommendation unit constituting a job recommendation device according to the first embodiment of the present invention. 18, 22, and 23, the first job recommendation unit 6320 provides at least one job to the subject based on the subject’s input job capability 6500, for example, the required job capability, and the first filter 6220. Can recommend a job. In an embodiment, the input job competency 6500 is data collected by a chatbot that collects education information, possession certificate information, portfolio, resume, self-introduction, SNS data, and history information in the form of conversation with the subject, the subject’s voice The collected data may include digitized personal information such as social relations information of the target person, interests of SNS, content viewing records, search information, etc., military service main specialty, military job portfolio, and missions performed during military service. The input job capability 6500 may be collected from the target person's terminal, or may be collected from the target person's SNS or e-mail, a job search relay server, a school or a national administrative agency server that stores and manages information on the target person. 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 capability determination unit 6328.

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

The vectorization unit 6324 may vectorize words extracted from the job capability 6500 input by the word extraction unit 6322 according to the arrangement order of words 6128 defined in the first filter 6220. In the first filter 6220, the order of words 6128 is arranged in units of columns. The vectorization unit 6324 includes k (k is an integer of 0 or more and m or less) extracted from the input job competency 6500 according to the arrangement order of words 6228 defined in the first filter 6220. Words can be converted into 1×m row vectors. In this case, in the row vector converted from words extracted from the input job competency 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 the Term Frequency (TF) in the input job capability 6500, or a value irrespective of the frequency by applying a binary score. It can also be determined. When a word frequency (TF) is used, a normalized term frequency can be used to calculate the similarity (eg, cosine similarity). In the case of 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'architectural design' in the first filter 6220 is defined in the 12145th column, the word When the frequency of the vectorization unit 6324 is not considered, the 12145th component value of the primary vector generated by the vectorization unit 6324 is determined as '1', and when the frequency of the word is considered, the first order generated by the vectorization unit 6324 The value of the 12145th component of the vector may be determined as '2'. Then, a normalized row vector (feature vector) can be generated by a vector normalization operation. The similarity calculation unit 6326 is based on the row vector (feature vector) generated from the job competency 6500 input by the vectorization unit 6324 and the first filter 6220 between the job seeker's feature vector and the competency units. Similarity can be calculated. In an embodiment, the similarity calculation unit 6326 may calculate a similarity (eg, cosine similarity) between the job seeker's feature vector and the row vectors (capacity unit vectors) of each of the job seekers.

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 the m job competency units by the size of the feature vector and the row vector size of the job competency unit. When the feature vector is normalized, the operation of dividing by the size of the feature vector may be omitted. The similarity calculation unit 6326 may calculate a similarity with a feature vector of a subject for each of n job competency units belonging to one major classification. The similar job competency determination unit 6328 determines the priority of the job competency units in the order of the highest similarity to the subject's feature vector among n similarity values between the subject's feature vector and the job competency units, and recommends the job to be recommended to the subject. Can be determined. In addition, certification information required for the recommended job or training information necessary to improve competency can be provided.

In an embodiment, the similar job competency determination unit 6328 may determine a recommended job from one or a plurality of major categories, in an order of a high degree of similarity between the subject's feature vector and the job competency units, for each major category. Alternatively, when the target person inputs selecting a specific major category using the target person interface, a job (job competency unit) belonging to the major category may be recommended based on a filter for only the selected major category. In the above, the case of recommending a job by creating various filters for each major category has been described as an example, and it is also possible to create a single filter for all job competency unit documents belonging to all major categories to recommend a job.

Hereinafter, an embodiment in which the second filter 240 is generated to recommend a job will be described. 24 is a configuration diagram of a second filter generator constituting a job recommendation device according to an embodiment of the present invention. 18 and 24, the second filter generator 6140 is a second filter generating unit 6140 of a two-dimensional matrix indicating the degree of association between the job competency units 6112 extracted from a dictionary 6125 including a plurality of job competency units. 2 Filter 6240 can be created.

The second filter generation unit 6140 may include a dictionary construction unit 6122, a word extraction unit 6146, and a relationship analysis unit 6148 between classifications. The dictionary collection construction unit 6122 may extract job competency units 6112 from job competency-related data 6400. In an embodiment, the dictionary collection building unit 6122 may extract job competency units 6112 from job competency-related data. The number of extracted job competency units 6112 may be n. The n number of job competency units 6114 may be used to calculate the second filter.

The word extraction unit 6146 may extract words from all of the n job competency units 6112. The word extracting unit 6146 may include a morpheme analyzer for dividing sentences in the job competency units 6112 into words in morpheme units and tagging parts of speech. In an embodiment, the word extractor 6146 may extract a noun tagging morpheme after morpheme analysis, remove stop words, and then apply an N-gram such as a bi-gram in the case of noun continuation. .

The relationship between classifications analysis unit 6148 may analyze the degree of correlation between the job competency units 6114 based on the similarity of words extracted from the job competency units 6112. In an embodiment, the relationship analysis unit 6148 between classifications may analyze the degree of correlation between the n job competency units 6114 based on TF-IDF (Term Frequency Inverse Document Frequency).

In an embodiment, when m words are extracted from the total job competency units 6112 by the word extracting unit 6146, the interclassification relationship analysis unit 6148 is 1 in which the order of all words is mapped to the column order. You can create a row vector of size ×m. The relationship between classifications analysis unit 6148 generates a row vector for each job competency unit 6124 from a set of words extracted from each job competency unit 6112, As many as n number of row vectors can be created. Thereafter, for all n×n combinations of the n job competency units 124, for example, the cosine similarity between the row vectors of the two job competency units 6112 is calculated according to Equations 1 to 4 above. By calculating, it is possible to analyze the relationship between the job competency unit documents 6112. The relationship diagram values between the job competency unit documents 6112 may be represented by a two-dimensional matrix having a size of n×n, and the 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. Referring to FIGS. 24 and 25, the second filter 6240 includes job competency units 6124 (a1, a2,...,an-1,an) in a row unit (first unit). It is arranged and may be expressed as a two-dimensional matrix in which job competency units 6112 (a1, a2,...,an-1,an) are arranged in a column unit (second unit). The job competency units 6112 (a1, a2,...,an-1,an) may be defined in the same order as the row unit and the column unit. Each component value of the 2D 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 identical row vectors extracted from the same job capability unit, the component value is 1. The second filter 6240 may be referred to as a set of row vectors having a size of 1×n for each of the n job unit capability units. A large component value in the second filter 6240 means that the similarity between the two corresponding 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 that configures a job recommendation device according to an embodiment of the present invention. 19, 25, and 26, the second job recommendation unit 6340 may recommend one or more jobs to the subject based on the job capability 6500 and the second filter 6240 input by the subject. . The second job recommendation unit 6340 may include a word extraction unit 6432, a job capability analysis unit 6344, and a similar job capability determination unit 6346.

The word extractor 6432 may extract competency-related words from the input job competency 6500 of the subject. The word extracting unit 6432 may include a morpheme analyzer that divides the inputted sentences in the job capability 6500 into words in morpheme units and tags the parts of speech. In an embodiment, the word extracting unit 342 extracts a noun tagging morpheme after morpheme analysis, removes stop words, and applies N-grams such as bi-grams in the case of noun continuation, and Words related to the unit of job competency can be extracted.

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

For example, in the second filter 6240 shown in FIG. 25, the relationship between the an-1th job competency unit (the first job competency unit) and the an-1 job competency unit (the second job competency unit) is 0.30. , If the subject's level score related to the an-1st job competency unit is input as 90 points, measured/evaluated or analyzed, the subject's level score may be reflected as 27 points for the anth job competency unit as well. Accordingly, based on the second filter 6240, the job search-related history of the subject's first job competency unit can be expanded to other job competency units, so that 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 may be written as a program that can be executed on a computer, and may be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium. Computer-readable recording media include volatile memories such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), 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 in the form of a CD-ROM or a 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 methods according to an embodiment of the present invention may be implemented by calling a stored command from a storage medium and executing a process according to the called command. 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-transient' means that the storage medium does not contain a signal and is tangible, but does not distinguish between semi-permanent or temporary storage of data in the storage medium.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to 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 limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms 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 (13)

Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
A job description generation method comprising the step of generating a job description from the required job ability and the similar job ability. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
Providing the one or more similar job capabilities to the terminal;
Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal;
A job description generating method comprising the step of generating a job description from the required job ability and the additional job ability. The method of claim 2,
After the step of receiving input from the terminal one or more additional job capabilities selected from the one or more similar job capabilities:
Providing the required job capability and the additional job capability to the terminal;
The method of generating a job description further comprising the step of receiving a weight for the required job competency and the additional job competency from the terminal. The method of claim 3,
The required job capability, the additional job capability, and the weight are stored as information on a company. The method of claim 1 or 2,
The step of deriving the similar job competency is:
Including the step of recommending the similar job ability based on the input job ability and filter,
The filter is generated based on a dictionary including a plurality of job competency units composed of data related to required competence according to the job, and the degree of association between the job competency units and words extracted from the data constituting the job competency units And a job description generating method including at least one of a degree of relation between the job competency units. The method of claim 5,
The filter is a two-dimensional matrix in which job competency units are arranged in a first unit of a row unit and a column unit, and words extracted from data constituting the job competency units are arranged in another second unit. Including,
The two-dimensional matrix is a method for generating a job description having a degree of association between each job competency unit of the first unit and each word of the second unit as respective component values. The method of claim 5,
The filter includes a two-dimensional matrix in which job competency units are arranged in a first unit of a row unit and a column unit, and the job competency units are arranged in another second unit,
The two-dimensional matrix is a job having a degree of relationship between a first job competency unit associated with each job competency unit of the first unit and a second job competency unit associated with each job competency unit of the second unit as component values How to create a description. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
And generating a competency measurement tool from the required job competency and the similar job competency. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
Providing the one or more similar job capabilities to the terminal;
Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal;
And generating a competency measurement tool from the required job competency and the additional job competency. The method of claim 9,
After the step of receiving input from the terminal one or more additional job capabilities selected from the one or more similar job capabilities:
Providing the required job capability and the additional job capability to the terminal;
And receiving a weight for the required job competency and the additional job competency from the terminal. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
Generating a competency measurement tool from the required job competency and the similar job competency;
Receiving competencies of one or more employees from the competency measurement tool;
Calculating the capabilities of the enterprise from the capabilities of the one or more employees;
Comparing the stored capabilities of one or more different enterprises;
Deriving a similar competency company having a similar capability to the company from among the one or more other companies;
A new business recommendation method for providing the derived business information of the similar competency company to the company. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
Generating a competency measurement tool from the required job competency and the similar job competency;
Receiving a capability of a first subject from the capability measurement tool;
Comparing the capabilities of the first testee with the capabilities of the stored at least one subject;
Extracting a second subject whose capabilities are similar to that of the first subject from among the stored at least one subject;
A company, job, or job recommendation method for recommending any one or more of a company, job, or job to the first subject from any one or more of company information, job information, or job information of the second subject. Receiving a job capability required from a terminal;
Deriving one or more similar job abilities similar to the required job abilities;
Providing the one or more similar job capabilities to the terminal;
Receiving one or more additional job abilities selected from the one or more similar job abilities from the terminal;
Deriving a required job competency of the company from the required job competency and the additional job competency;
And recommending a curriculum to the educational institution by comparing the required job competency of the company with the job competency according to the curriculum of the educational institution.

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