JP7228072B1 - Recruitment Support Device and Recruitment Support Method - Google Patents

Abstract

A recruitment support device and a recruitment support method capable of appropriately hiring a job seeker are provided. SOLUTION: A recruitment support device includes a management unit that manages profile information of a job seeker, and a control unit that generates a scout document reflecting a word group including one or more words extracted from the profile information. wherein the control unit generates a learning model by learning a correlation between a first parameter related to employment of the job seeker and the word group, and extracts the word group based on the generated learning model. do. [Selection diagram] Fig. 2

Description

The present invention relates to a recruitment support device and a recruitment support method.

In recent years, matching services between job seekers and recruits have attracted attention. In such a matching service, job seekers register their profile information in a database, and recruiters send documents related to recruitment (hereinafter referred to as scout documents) to job seekers based on the profile information of job seekers. .

Furthermore, since creating a scout document is complicated, a technique of utilizing a scout document template has also been proposed (eg, Patent Document 1).

JP 2013-246453 A

However, since the scout document template described above is a mediocre document for a job seeker, the interest of the job seeker in the scout document is reduced. As a result, the response rate to the scout documents may decrease, and job seekers may not be properly recruited.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide a recruitment support device and a recruitment support method capable of appropriately hiring job seekers.

One aspect of the disclosure includes a management unit that manages profile information of a job seeker, and a control unit that generates a scout document that reflects a word group including one or more words extracted from the profile information, The control unit generates a learning model by learning a correlation between a first parameter related to employment of the job seeker and the word group, and extracts the word group based on the generated learning model. It is a support device.

One aspect of the disclosure is a step A of managing profile information of a job seeker, a step B of generating a scout document reflecting a word group including one or more words extracted from the profile information, and a step B of the job seeker a step C of generating a learning model by learning a correlation between a first parameter related to recruitment and the word group, and extracting the word group based on the generated learning model; is.

According to the present invention, it is possible to provide a recruitment support device and a recruitment support method that can appropriately recruit job seekers.

FIG. 1 is a diagram showing a recruitment support system 100 according to an embodiment. FIG. 2 is a diagram showing the recruitment support device 30 according to the embodiment. FIG. 3 is a diagram showing an example of profile information according to the embodiment. FIG. 4 is a diagram illustrating an example of a scout document according to the embodiment; FIG. 5 is a diagram showing a learning method according to the embodiment. FIG. 6 is a diagram showing a learning method according to Modification 1. As shown in FIG. FIG. 7 is a diagram showing a recruitment support system 100 according to Modification 2. As shown in FIG. FIG. 8 is a diagram showing an example of a scout document according to Modification 2. As shown in FIG. FIG. 9 is a diagram showing a learning method according to Modification 2. In FIG.

Embodiments will be described below with reference to the drawings. In addition, in the following description of the drawings, the same or similar reference numerals are given to the same or similar parts.

However, it should be noted that the drawings are schematic, and the ratio of each dimension may differ from the actual one. Therefore, specific dimensions should be determined with reference to the following description. In addition, it is needless to say that the drawings may include portions having different dimensional relationships or ratios.

[Summary of Disclosure]
A recruitment support device according to an overview of the disclosure includes a management unit that manages profile information of a job seeker, a control unit that generates a scout document that reflects a word group including one or more words extracted from the profile information, wherein the control unit generates a learning model by learning a correlation between a first parameter related to employment of the job seeker and the word group, and determines the word group based on the generated learning model Extract.

A recruitment support method according to the outline of the disclosure includes step A of managing profile information of a job seeker; step B of generating a scout document reflecting a word group including one or more words extracted from the profile information; generating a learning model by learning a correlation between the word group and a first parameter related to employment of the job seeker, and extracting the word group based on the generated learning model. .

In an outline of the disclosure, a recruitment support device generates a learning model by learning a correlation between a first parameter related to hiring a job seeker and a word group, and extracts words based on the generated learning model. Generate a scout document reflecting the flock. According to such a configuration, it is possible to extract appropriate word groups from the profile information, and it is possible to increase the success rate of hiring job seekers using scout documents.

Although not particularly limited, it may be considered that the success rate of recruiting job seekers increases by improving the response rate to scout documents.

[Embodiment]
(Recruitment support system)
A recruitment support system according to the embodiment will be described below. FIG. 1 is a diagram showing a recruitment support system 100 according to an embodiment.

As shown in FIG. 1, the recruitment support system 100 has a first terminal 10, a second terminal 20, and a recruitment support device 30. FIG. The first terminal 10, the second terminal 20, and the recruitment support device 30 are connected by a network 200. FIG. Although not particularly limited, the network 200 may be configured by the Internet network. Network 200 may include a local area network, a mobile communication network, or a VPN (Virtual Private Network).

The first terminal 10 is a terminal used by job seekers. For example, the first terminal 10 may be a personal computer, a smart phone, or a tablet terminal. Although not particularly limited, the job seeker may use the first terminal 10 to input the job seeker's profile information.

The second terminal 20 is a terminal used by the recruiter. The second terminal 20 may be a personal computer, a smart phone, or a tablet terminal. Although not particularly limited, the recruiter may use the second terminal 20 to browse the profile information of the job seeker.

The recruitment support device 30 is a device that supports recruitment of human resources. The recruitment support device 30 provides a matching service between job seekers and recruiters. In the embodiment, the recruitment support device 30 generates scout documents for job seekers. The details of the employment support device 30 will be described later.

(Recruitment support device)
A recruitment support device according to an embodiment will be described below. FIG. 2 is a diagram showing the recruitment support device 30 according to the embodiment. As shown in FIG. 2 , the recruitment support device 30 has a communication section 31 , a management section 32 and a control section 33 .

The communication unit 31 is configured by a communication module. The communication module can be a wireless communication module conforming to standards such as IEEE802.11a/b/g/n/ac/ax, LTE, 5G, 6G, etc., and a wired communication module conforming to standards such as IEEE802.3 may be

In the embodiment, the communication unit 31 may constitute a transmission unit that transmits scout documents. For example, the communication unit 31 may transmit the scout document to the first terminal 10. FIG. The communication unit 31 may transmit the scout document to the second terminal 20 for the recruiter to view. The scout documents may be sent to the second terminal 20 before being sent to the first terminal 10. FIG.

The communication unit 31 may receive the job seeker's profile information from the first terminal 10 . The communication unit 31 may transmit the job seeker's profile information to the second terminal 20 for the recruiter to view.

The management unit 32 is composed of a storage medium such as an SSD (Solid State Drive) or HDD (Hard Disk Drive), and stores various information.

In the embodiment, the management unit 32 constitutes a management unit that manages profile information of job seekers. As noted above, profile information may be entered by the job seeker using the first terminal 10 .

Although not particularly limited, the profile information may include the information shown in FIG. As shown in FIG. 3, profile information may include desired conditions, current industry, current occupation, career history, and the like.

Desired conditions are information on conditions desired by job seekers. Desired conditions may include the area in which the job seeker wishes to work (eg, Tokyo, Osaka, nationwide, overseas, etc.). The desired conditions may include the annual income desired by the job seeker (for example, XX yen or more, XX yen to XX yen, etc.). Desired conditions may include the type of industry desired by the job seeker (for example, manufacturing industry, information and communication industry, financial industry, service industry, etc.). Desired conditions may include job types desired by job seekers (for example, sales, clerical work, sales, accounting, legal affairs, etc.). Although not particularly limited, the desired conditions may include items other than the items described above. For example, the desired conditions may include a position desired by the job seeker (eg, managerial position, general manager position, section manager position, responsible position, etc.).

The current industry may be information about the industry that the job seeker is currently working on. If the job seeker is currently unemployed, the current industry may be information about the industry related to the most recent job in which the job seeker is engaged.

The current job title may be information about the job job that the job seeker is currently working on. If the job seeker is currently unemployed, the current occupation may be information about the occupation related to the most recent job the job seeker is engaged in.

Biography is information about the career of a job seeker. A biography may include information about a job seeker's educational background. A biography may include information about a job seeker's work history. A biography may include information about the job seeker's qualifications. A biography may include information about a job seeker's skills. The biography may include, but is not limited to, items other than those listed above. For example, a biography may include information about a job seeker's selling points (eg, achievements in service awards, personality, etc.).

Control unit 33 may include at least one processor. At least one processor, CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), one or more Integrated Circuit, one or more Discrete Circuit, and may be configured by a combination thereof .

In the embodiment, the control unit 33 constitutes a control unit that generates a scout document reflecting a word group including one or more words extracted from profile information. Here, the word group may mean a clause containing one or more extracted words, or a sentence containing one or more extracted words. The phrases and sentences may be extracted from the profile information and may be automatically generated based on one or more extracted words.

The scout document may include, but is not limited to, the information shown in FIG. As shown in FIG. 4, the scout document may include a preface, attention/evaluation points, company introduction, business description, summary, and the like.

The first part is information about a standard sentence such as a greeting sentence. In the former stage, the word group extracted from the profile information may not be reflected.

The attention/evaluation points are information about the points that focused on the job seeker and the points that evaluate the job seeker. Attention/evaluation points may reflect word groups extracted from profile information. For example, a group of words related to the abilities required of a job seeker may be reflected in the attention/evaluation points. A word group related to ability may include one or more words extracted from current industry, current occupation, and career. A group of words related to achievements desired of a job seeker may be reflected in attention/evaluation points. A word group related to achievements may include one or more words extracted from current industry, current occupation, and career.

For example, an attention/evaluation point may include a sentence such as "I contacted you because I saw your career, XXX, and thought that you were good not only at YYYY but also at ZZZZ." In such cases, YYYY and ZZZZ may be examples of word groups extracted from profile information. Alternatively, the sentences themselves may be automatically generated based on one or more words extracted from the profile information. XXX is the name of the job seeker.

The company introduction is information related to introduction of the company. The company introduction may include content related to the company's business, and may include the circumstances surrounding the company's business. Although not particularly limited, the company introduction may be input by the second terminal 20 .

The business content is information about the business employed in the scout document. The business content may include the details of the content of the business, the capabilities expected for the business, the tasks related to the business, and the like. Although not particularly limited, the company introduction may be input by the second terminal 20 .

The summary may be attention/evaluation points based on company introduction and business content. The summary may reflect a group of words extracted from the profile information. For example, a group of words related to the ability required of a job seeker based on company introduction and job content may be reflected in the attention/evaluation points. A word group related to ability may include a word group extracted from the current industry, current occupation, and career. A group of words related to achievements required of a job seeker based on company introduction and business content may be reflected in attention/evaluation points. The word group related to achievements may include a word group extracted from the current industry, current occupation, and career.

For example, an attention/evaluation point might be, "We are looking to expand our organization in the future, and we are looking for people who have experienced ZZZZ like XXX. I think this is the phase where XXX can demonstrate their strengths. ." may be included. In such cases, ZZZZ may be an example of a group of words extracted from profile information. Alternatively, the sentences themselves may be automatically generated based on one or more words extracted from the profile information. XXX is the name of the job seeker.

Under such a premise, in the embodiment, the control unit 33 generates a learning model by learning the correlation between the first parameter related to employment of job seekers and word groups. That is, the control unit 33 generates a learning model for the purpose of improving the accuracy of extracting words (or word groups) such as YYYY and ZZZZ described above.

Specifically, the control unit 33 acquires a previously transmitted scout document. A scout document is a document reflecting word groups extracted from profile information. Control unit 33 generates a learning model by learning the correlation between the word group and the first parameter. The learning model is a model that, when generating a new scout document, takes as input a word group extracted from profile information and outputs a word group that has had a positive effect on hiring a job seeker.

Here, the first parameter is a parameter corresponding to a scout document that has been sent in the past, and is a parameter for determining whether or not the word group has had a positive effect on hiring a job seeker. The following parameters can be used as the first parameter.

First, the first parameter may be a parameter regarding whether or not there is a reply to the scout document. Specifically, the control unit 33 generates a learning model by learning the correlation between word groups and whether or not there is a reply to the scout document. In other words, the control unit 33 generates a learning model so that the weight of the word group included in the scout documents to which the job seeker has replied is relatively high, and the word group is included in the scout documents to which the job seeker has not replied. A learning model may be generated such that the weighting of word groups is relatively low. Alternatively, the control unit 33 may use, as learning parameters, word groups included in scout documents to which job seekers have replied, instead of using word groups included in scout documents to which job seekers have not replied as learning parameters. good. The fact that there was a reply to the scout document means that the word group had a positive impact on the recruitment of job seekers.

The first parameter may include a parameter relating to the time from sending the scout document to replying to the scout document (hereinafter referred to as lead time). Specifically, the control unit 33 generates a learning model by learning the correlation between word groups and lead times. For example, the control unit 33 generates a learning model so that the weight of the word group included in the scout document that has a reply with a relatively short lead time is relatively high, A learning model may be generated so that the weighting of word groups contained in scout documents is relatively low. Alternatively, the control unit 33 does not use, as a learning parameter, a group of words included in scout documents that reply with a lead time longer than the threshold, and instead selects words contained in scout documents that reply with a lead time shorter than the threshold. A group may be used as a learning parameter. A shorter lead time could mean that the word group had a better influence on the hiring of job seekers.

Second, the first parameter may be a parameter related to the hiring success or failure of the job seeker. Specifically, the control unit 33 generates a learning model by learning the correlation between the word group and the hiring success or failure of the job seeker. In other words, the control unit 33 generates the learning model so that the weight of the word group contained in the scout document used in the case where the job seeker was hired is relatively high, and the job seeker was not hired. A learning model is generated so that the word groups contained in the scout documents used in the cases are given relatively low weights. Alternatively, the control unit 33 does not use the word group included in the scout document used in the case where the job seeker was not hired as a learning parameter, but instead uses the words included in the scout document used in the case where the job seeker was hired. Word groups may be used as learning parameters. The fact that job seekers were hired means that word groups had a positive effect on job seekers' hiring.

Third, the first parameter may include parameters relating to both the presence or absence of a reply to the scout document and the success or failure of hiring the job seeker.

When transmitting a new scout document, the control unit 33 extracts a word group based on the generated learning model. The control unit 33 generates a scout document reflecting the extracted word group.

(learning method)
A learning method according to the embodiment will be described below. Note that learning is performed before sending new scout documents.

As shown in FIG. 5, in step S10, the recruitment support device 30 acquires word groups. The word group is a word group reflected in a scout document that has been sent in the past, and is a word group extracted from profile information.

In step S12, the recruitment support device 30 acquires a first parameter corresponding to the previously transmitted scout document. The first parameter may include a parameter relating to at least one of whether or not there is a reply to the scout document and whether or not the job seeker has been hired.

In step S14, the recruitment support device 30 generates a learning model by learning the correlation between the word group and the first parameter. The learning model is a model that, when generating a new scout document, takes as input a word group extracted from profile information and outputs a word group that has had a positive effect on hiring a job seeker.

(Action and effect)
In the embodiment, the recruitment support device 30 generates a learning model by learning a correlation between a first parameter related to hiring a job seeker and a word group, and extracts words based on the generated learning model. Generate a scout document reflecting the flock. According to such a configuration, it is possible to extract appropriate word groups from the profile information, and it is possible to increase the success rate of hiring job seekers using scout documents.

[Modification 1]
Modification 1 of the embodiment will be described below. Differences from the first embodiment will be mainly described below.

In Modification 1, a learning model is generated using a second parameter related to job seeker attributes. The second parameter includes one or more attributes selected from the job seeker's desired conditions, the job seeker's current industry, the job seeker's current occupation, the job seeker's career, and the job seeker's behavior.

Job seeker's desired conditions can be specified from the profile information. For example, a case can be assumed in which a word group that has a positive effect on a job seeker with a relatively high desired annual income is different from a word group that has a positive effect on a job seeker with a relatively low desired annual income.

A job seeker's current industry can be identified from profile information. For example, it is conceivable that each industry has a different set of values and a different set of words that have a positive effect on job seekers.

A job seeker's current occupation is identifiable from profile information. For example, a case can be assumed in which the sense of values differs for each job type, and the word group that has a positive effect on the job seeker differs for each job type.

A job seeker's background is identifiable from profile information. For example, a case can be assumed in which a word group that has a positive influence on a job seeker with experience of a position is different from a word group that has a positive influence on a job seeker without experience of a position.

The job seeker's actions include the viewing status of job offers, the status of applying for job offers, the status of replying to scout documents, and the like. These pieces of information may be managed by the management unit 32 of the recruitment support device 30. FIG. For example, a job seeker's behavior can serve as a basis for determining whether or not he or she is currently considering changing jobs. For example, it is assumed that the group of words that have a positive effect on job seekers who are currently considering changing jobs is different from the group of words that have a positive effect on job seekers who are not currently considering changing jobs. obtain.

Under this premise, the recruitment support device 30 generates a learning model using the second parameters related to job seeker attributes.

For example, the second parameter may be input to the learning model as a learning parameter. That is, the recruitment support device 30 may generate a learning model by learning the correlation between word groups, first parameters, and second parameters.

Alternatively, the recruitment support device 30 may classify job seekers into two or more categories based on the second parameter. The recruitment support device 30 may generate a learning model for each category.

(learning method)
The learning method according to Modification 1 will be described below. Note that learning is performed before sending new scout documents.

As shown in FIG. 6, in step S20, the recruitment support device 30 acquires word groups. The word group is a word group reflected in a scout document that has been sent in the past, and is a word group extracted from profile information.

In step S22, the recruitment support device 30 acquires the first parameter corresponding to the previously transmitted scout document. The first parameter may include a parameter relating to at least one of whether or not there is a reply to the scout document and whether or not the job seeker has been hired.

In step S24, the recruitment support device 30 acquires a second parameter regarding attributes of the job seeker.

In step S26, the recruitment support device 30 generates a learning model by learning the correlation between the word group and the first parameter. In Modification 1, recruitment support device 30 generates a learning model using the second parameter.

(Action and effect)
In Modification 1, the recruitment support device 30 generates a learning model using a second parameter related to job seeker attributes. According to such a configuration, it is possible to appropriately generate a learning model even when it is assumed that word groups that have a positive influence on a job seeker are different for each attribute of the job seeker. As a result, it is possible to extract appropriate word groups from the profile information, and the success rate of hiring job seekers using scout documents can be increased.

[Modification 2]
Modification 2 of the embodiment will be described below. Differences from the first embodiment will be mainly described below.

In Modification 2, as shown in FIG. 7, a case in which a third party 40 intervenes is considered. For example, third party 40 may be a headhunter. The third party 40 may have authority to use at least some functions of the recruitment support device 30 . In Modification 2, a case is assumed in which a job seeker receives a scout document from a third party 40 .

In Modification 2, a learning model is generated using third party information about third party 40 . The third party information includes one or more attributes selected from among the types of jobs handled by the third party 40, job types of jobs handled by the third party 40, and documents of jobs handled by the third party 40. FIG.

The type of job offer handled by the third party 40 may be managed by the management unit 32 of the recruitment support device 30 or may be identified from the job offer document handled by the third party 40 . The type of job offer handled by the third party 40 may be used to generate a learning model in the same way as the above-described second parameter. In other words, the type of job offer handled by the third party 40 may be used as a learning parameter of the learning model, and may be used to specify a category corresponding to the learning model.

Job types for which the third party 40 is recruiting may be managed by the management unit 32 of the recruitment support device 30, or may be specified from the recruitment documents handled by the third party 40. FIG. The job types of job offers handled by the third party 40 may be used to generate a learning model in the same way as the above-described second parameter. In other words, the type of job offer handled by the third party 40 may be used as a learning parameter of the learning model, and may be used to specify a category corresponding to the learning model.

Recruitment documents handled by third parties 40 may include documents that third parties 40 send to job seekers. For example, a job offer document handled by the third party 40 is a document used in headhunting. Recruitment documents handled by the third party 40 may be used to generate a learning model in the same way as the scout documents described in the embodiment. That is, word groups used as learning parameters of the learning model may be extracted from the job offer document handled by the third party 40 . Further, the management unit 32 may manage the first parameters (whether or not there is a reply to the document and whether or not the job seeker has been hired) corresponding to the recruitment document handled by the third party 40 .

Although not so limited, the third party 40 scout document may include the information shown in FIG. As shown in FIG. 8, the scout document may include a preface, attention/evaluation points, self-introduction, job description, summary, and the like. That is, the scout document of the third party 40 includes a self-introduction in place of the company introduction and business contents of the scout document shown in FIG.

The self-introduction is information relating to the introduction of the third party 40 . A self-introduction may be entered by third party 40 . The self-introduction may include information about the job that the third party 40 is currently in charge of, may include information about the performance that the third party 40 has been in charge of in the past, and the area of expertise of the third party 40 ( industry, occupation, etc.) may be included.

Under such a premise, the recruitment support device 30 uses the third party information regarding the third party 40 to generate a learning model.

For example, the industry of the job offer handled by the third party 40, the job type of the job offer handled by the third party 40, and the document of the job offer handled by the third party 40 may be used as learning parameters of the learning model. The type of job postings handled by the third party 40 and the type of job postings handled by the third party 40 may be used to identify categories corresponding to learning models.

(learning method)
The learning method according to Modification 2 will be described below. Note that learning is performed before sending new scout documents.

As shown in FIG. 9, in step S30, the recruitment support device 30 acquires word groups. The word group is a word group reflected in a scout document that has been sent in the past, and is a word group extracted from profile information.

In step S32, the recruitment support device 30 acquires a first parameter corresponding to the previously transmitted scout document. The first parameter may include a parameter relating to at least one of whether or not there is a reply to the scout document and whether or not the job seeker has been hired.

In step S34, the recruitment support device 30 acquires third party information regarding the third party 40. FIG.

In step S36, the recruitment support device 30 generates a learning model by learning the correlation between the word group and the first parameter. In Modification 2, recruitment support device 30 generates a learning model using third party information.

(Action and effect)
In Modification 2, recruitment support device 30 uses third party information about third party 40 to generate a learning model. According to such a configuration, it is possible to expand the population of information used for generating the learning model, and to appropriately generate the learning model. As a result, it is possible to extract appropriate word groups from the profile information, and the success rate of hiring job seekers using scout documents can be increased.

[Other embodiments]
Although the present invention has been described by the above-described embodiments, the statements and drawings forming part of this disclosure should not be construed as limiting the present invention. Various alternative embodiments, implementations and operational techniques will become apparent to those skilled in the art from this disclosure.

Although not specifically mentioned in the above disclosure, Modification 1 and Modification 2 may be combined. That is, the recruitment support device 30 may generate a learning model using the second parameter and the third party information.

Although not specifically mentioned in the above disclosure, the learning may be machine learning or deep learning represented by AI (Artificial Intelligence).

Although not specifically mentioned in the disclosure above, the recruitment support device 30 may extract one or more word groups extracted from the profile information based on the learning model, and then automatically generate the scout document itself. . Scout documents may be generated by existing writing tools. The writing tool may be a tool using AI.

In the above disclosure, the case where the management unit 32 is provided in the recruitment support device 30 has been exemplified. However, the above disclosure is not so limited. The management unit 32 may be included in the same external server that can communicate with the recruitment support device 30 as a separate entity from the recruitment support device 30, or may be included in a separate external server that can communicate with the recruitment support device 30. good too.

In the above disclosure, the case in which the recruitment support device 30 transmits the scout document has been mainly described. However, the above disclosure is not so limited. For example, the recruitment support device 30 may provide only the function of managing (saving) scout documents. In such a case, the recruitment support device 30 may generate a learning model and extract word groups using the learning model. According to such a configuration, provision of word groups to be included in scout documents can help improve the success rate of hiring job seekers using scout documents.

Although not particularly mentioned in the disclosure above, a program that causes a computer to execute each process performed by the recruitment support device 30 may be provided. Also, the program may be recorded on a computer-readable medium. A computer readable medium allows the installation of the program on the computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory recording medium. The non-transitory recording medium is not particularly limited, but may be, for example, a recording medium such as CD-ROM or DVD-ROM.

Alternatively, a chip configured by a memory storing a program for executing each process performed by the recruitment support device 30 and a processor executing the program stored in the memory may be provided.

10... First terminal, 20... Second terminal, 30... Recruitment support device, 31... Communication unit, 32... Management unit, 33... Control unit, 40... Third party, 100... Recruitment support system, 200... Network

Claims (9)

an administration department that manages job seeker profile information;
a control unit that generates a scout document reflecting a word group including one or more words extracted from the profile information;
The control unit extracts the word group based on a correlation between a first parameter related to employment of the job seeker and the word group,
The recruitment support device, wherein the first parameter is a parameter indicating an influence on employment of the job seeker. 2. The recruitment support device according to claim 1, wherein said first parameter includes a parameter relating to at least one of presence/absence of reply to said scout document, time until reply to said scout document, and success or failure of hiring said job seeker. The first parameter is a parameter for relatively high weighting of a word group contained in a scout document that has been sent in the past and has received a reply to the scout document; A parameter that relatively increases the weighting of the word group contained in the scout document sent to , and the weighting of the word group contained in the previously sent scout document used in the case of hiring the job seeker 3. The recruitment support device according to claim 1, wherein the recruitment support device is at least one of relatively high parameters. the scout document includes points focused on the job seeker and points evaluating the job seeker;
4. The control unit according to any one of claims 1 to 3 , wherein the word group extracted from the profile information is reflected in the scout document as a point for focusing on the job seeker and a point for evaluating the job seeker. 1. The employment support device according to 1. 5. The recruitment support device according to any one of claims 1 to 4, wherein said word group extracted from said profile information includes a word group relating to abilities required of said job seeker. 6. The word group related to the abilities required of the job seeker includes words extracted from at least one of the job seeker's current industry, the job seeker's current occupation, and the job seeker's career. Recruitment support device described. 7. The recruitment support device according to any one of claims 1 to 6, wherein said group of words extracted from said profile information includes a group of words relating to achievements required of said job seeker. 8. The word group according to claim 7, wherein the group of words related to the performance desired of the job seeker includes words extracted from at least one of the job seeker's current industry, the job seeker's current occupation, and the job seeker's career. Recruitment support device described. a step A in which the recruitment support device manages the profile information of the job seeker;
step B in which the recruitment support device generates a scout document reflecting a word group including one or more words extracted from the profile information;
a step C in which the recruitment support device extracts the word group based on a correlation between the word group and a first parameter related to hiring of the job seeker;
The recruitment support method, wherein the first parameter is a parameter indicating an influence on employment of the job seeker.

Images