Job search support system, job search support method and program

Abstract

To provide a job search support system, etc. capable of presenting job offers suitable for job searcher.SOLUTION: There is provided a job search support system. The job search support system comprises a processor. The processor is configured to perform the following steps: a registration step of registering registration information of a job searcher; and a presentation step of presenting at least one recommended job offer to be recommended to the job searcher from among job offers to which the job searcher can apply, on the basis of job searcher characteristic information and reference information. The job searcher characteristic information is at least one of the registration information and a behavior history of the job searcher to job offers. The reference information includes first reference information and second reference information that are used depending on timing after registration of the registration information. The first reference information includes a first correlation between the job searcher characteristic information and the job offer. The second reference information includes a second correlation between the job searcher characteristic information and the job offer, which is different from the first correlation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a job search support system, a job search support method, and a program. [Background technology]

[0002] As disclosed in Patent Document 1, a technique is known that enables job seekers to register user information such as resumes and search for job openings. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-85437 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned job search services, it may be difficult for job seekers to set search conditions to search for suitable jobs.

[0005] In view of the above circumstances, the present invention provides a job-seeking support system and the like that can present suitable job offers to job seekers. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided a job search support system. The job search support system includes a processor. The processor is configured to execute the following steps: In the registration step, registration information of a job seeker is registered. In the presentation step, at least one recommended job offer is presented to the job seeker from among job offers to which the job seeker can apply, based on the job seeker characteristic information and the reference information. The job seeker characteristic information is at least one of the registration information and the job seeker's behavioral history regarding job offers. The reference information includes first reference information and second reference information that are used differently depending on the time after the registration of the registration information. The first reference information includes a first correlation between the job seeker characteristic information and job offers. The second reference information includes a second correlation between the job seeker characteristic information and job offers, which is different from the first correlation.

[0007] According to this embodiment, recommended job offers suitable for a job seeker can be presented according to the job seeker's registered information. Furthermore, because different reference information for determining recommended job offers is used depending on the time after registration of the registered information, suitable recommended job offers can be presented even to a job seeker who has just registered. As a result, the value of the recruitment support service provided by the job search support system can be presented to a job seeker who has just registered. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a configuration diagram illustrating a job search support system 1. FIG. [Figure 2] 2 is a block diagram showing the hardware configuration of the server device 10. FIG. [Figure 3] FIG. 2 is a block diagram showing the hardware configuration of a recruiting party terminal 20 and a job seeker terminal 30. [Figure 4] 1 is a block diagram showing functions realized by a server device 10 (control unit 11), a recruiting party terminal 20 (control unit 21), and a job seeker terminal 30 (control unit 31). [Figure 5] 10 is a diagram showing an example of a recommended job offer display screen RD displayed on the job seeker terminal 30. FIG. [Figure 6] FIG. 10 is a diagram showing an example of a job offer viewing screen JD displayed on a job seeker terminal 30. [Figure 7]10 is a diagram showing an example of a registration information editing screen ED displayed on the job seeker terminal 30. FIG. [Figure 8] 1 is an activity diagram showing the flow of information processing (recommended job offer presentation processing) executed by job seeking support system 1. FIG. [Figure 9] FIG. 2 is an activity diagram showing the flow of information processing (processing for proposing corrections) executed by job seeking support system 1. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0010] Incidentally, the program for realizing the software appearing in this embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0011] In this embodiment, the term "unit" may also include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In addition, various types of information are handled in this embodiment, and this information may be represented by, for example, physical values ​​of signal values ​​representing voltages and currents, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations may be performed on a circuit in the broad sense.

[0012] In addition, a circuit in the broad sense is a circuit realized by at least appropriately combining a circuit, circuitry, a processor, a memory, etc. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0013] 1. Hardware Configuration This section explains the hardware configuration.

[0014] <Job Search Support System 1> Fig. 1 is a configuration diagram showing a job search support system 1. The job search support system 1 comprises a communication line 2, a server device 10, a plurality of recruiter terminals 20, and a plurality of job seeker terminals 30. The server device 10, the recruiter terminals 20, and the job seeker terminals 30 are configured to be able to communicate with each other via the communication line 2. The connection between the server device 10, the recruiter terminals 20, and the job seeker terminals 30 may be wired or wireless.

[0015] The job search support system 1 constitutes part of a recruitment and job search system used by multiple recruiters (first recruiter U1 and second recruiter U2) and multiple job seekers (first job seeker U3 and third job seeker U4). The job search support system 1 mainly manages job seeker registration information and job postings. In one embodiment, the job search support system 1 is made up of one or more devices or components. These components are described below.

[0016] <Server device 10> 2 is a block diagram showing the hardware configuration of server device 10. Server device 10 includes a control unit 11, a storage unit 12, a communication unit 13, and a communication bus 14. Control unit 11, storage unit 12, and communication unit 13 are electrically connected within server device 10 via communication bus 14.

[0017] <Control unit 11> The control unit 11 processes and controls the overall operations related to the server device 10. The control unit 11 is, for example, a central processing unit (CPU). The control unit 11 realizes various functions related to the server device 10 by reading out predetermined programs stored in the storage unit 12. In other words, information processing by software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11. These will be described in more detail in the next section. Note that the control unit 11 is not limited to being single, and multiple control units 11 may be provided for each function. A combination of these may also be used.

[0018] <Storage section 12> The memory unit 12 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the server device 10 executed by the control unit 11, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The memory unit 12 stores various programs, variables, etc. related to the server device 10 executed by the control unit 11.

[0019] <Communications Department 13> The communication unit 13 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), or wired LAN network communication, but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, or BLUETOOTH (registered trademark) communication as needed. That is, it is more preferable to implement it as a collection of multiple communication means. That is, the server device 10 may communicate various information from the outside via the communication unit 13 and the network.

[0020] The server device 10 may be an on-premise server or a cloud server. The cloud server device 10 may provide the above-described functions and processes in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0021] <Recruiter Terminal 20> Fig. 3 is a block diagram showing the hardware configuration of the recruiting party terminal 20 and the job seeker terminal 30. As shown in Fig. 3A, the recruiting party terminal 20 comprises a control unit 21, a memory unit 22, a communication unit 23, an input unit 24, an output unit 25, and a communication bus 26. The control unit 21, the memory unit 22, the communication unit 23, the input unit 24, and the output unit 25 are electrically connected within the recruiting party terminal 20 via the communication bus 26. The explanation of the control unit 21, the memory unit 22, and the communication unit 23 is the same as the explanation of each unit in the server device 10, and will therefore be omitted. Note that the recruiting party terminal 20 may also be a terminal operated by a recruitment agency that interacts with job seekers on behalf of the recruiter.

[0022] <Input section 24> The input unit 24 accepts operation inputs made by the user. The operation inputs are transferred as command signals to the control unit 21 via the communication bus 26. The control unit 21 can execute predetermined control or calculations based on the transferred command signals as necessary. The input unit 24 may be included in the housing of the recruiter terminal 20 or may be attached externally. For example, the input unit 24 may be implemented as a touch panel integrated with the output unit 25. When the input unit 24 is implemented as a touch panel, the user can input tap operations, swipe operations, etc. to the input unit 24. Instead of a touch panel, a switch button, a mouse, a trackpad, a QWERTY keyboard, etc. can be used as the input unit 24.

[0023] <Output section 25> The output unit 25 displays a screen of a graphical user interface (GUI) that can be operated by the user. The output unit 25 may be included in the housing of the recruiter terminal 20, or may be attached externally. Specifically, the output unit 25 may be implemented as a display device such as a CRT display, a liquid crystal display, an organic EL display, or a plasma display. It is preferable that these display devices are implemented by selectively using them depending on the type of recruiter terminal 20.

[0024] <Job Seeker Terminal 30> 3B, the job seeker terminal 30 includes a control unit 31, a memory unit 32, a communication unit 33, an input unit 34, an output unit 35, and a communication bus 36. The control unit 31, the memory unit 32, the communication unit 33, the input unit 34, and the output unit 35 are electrically connected via the communication bus 36 inside the job seeker terminal 30. The explanation of the control unit 31, the memory unit 32, the communication unit 33, the input unit 34, and the output unit 35 will be omitted as they are the same as the explanation of each unit in the recruiter terminal 20.

[0025] 2. Functional configuration This section describes the functional configuration of this embodiment. Information processing by the software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11 (a processor provided in the job search support system 1).

[0026] FIG. 4 is a block diagram showing functions realized by the server device 10 (controller 11), the recruiter terminal 20 (controller 21), and the job seeker terminal 30 (controller 31).

[0027] As shown in Fig. 4A, server device 10 (control unit 11) includes a basic display control unit 111, a registration unit 112, a publication unit 113, a reception unit 114, a presentation unit 115, a correction suggestion unit 116, a registered information editing unit 117, and an artificial intelligence unit 120. As shown in Fig. 4B, recruiter terminal 20 (control unit 21) includes a display unit 211 and an operation acquisition unit 212. As shown in Fig. 4C, job seeker terminal 30 (control unit 31) includes a display unit 311 and an operation reception unit 312.

[0028] <Basic display control unit 111> The basic display control unit 111 is configured to display various information on the recruiter terminal 20 and the job seeker terminal 30. For example, the basic display control unit 111 displays a resume and curriculum vitae prepared by the job seeker, a job advertisement and scouting document prepared by the recruiter, etc. on the display unit 211 of the recruiter terminal 20 or the display unit 311 of the job seeker terminal 30.

[0029] Recruiters include organizations such as for-profit corporations (such as companies), non-profit corporations (such as cooperatives and foundations), and public corporations (such as local governments). Recruiters also include recruitment agencies that act as agents of organizations to mediate between job seekers and organizations. Recruitment agencies are also called headhunters or agents.

[0030] <Registration Section 112> The registration unit 112 is configured to register job seekers to use the service. Specifically, the registration unit 112 accepts input from the operation acceptance unit 312 of the job seeker terminal 30, and registers the job seeker's registration information (hereinafter referred to as job seeker registration information), including the job seeker's resume, curriculum vitae, and other profile information. The job seeker registration information is registered in a job seeker database stored in the storage unit 12, for example.

[0031] A "resume" is a document that mainly contains a job seeker's profile, current situation, educational background, work history, desired working conditions, etc., while a "curriculum vitae," also known as a resume, is a document in which a job seeker conveys to an employer his or her work history, experience, skills, qualifications, etc. In addition, the job seeker registration information may also include the job seeker's desired conditions (desired industry, desired job type, etc.).

[0032] The registration unit 112 determines whether new job seeker registration information that is accepted for registration includes all of the required items set in advance (whether all required items have been entered), and if the required items are included, registers the job seeker registration information in the job seeker database. If any required items are missing, the registration unit 112, for example, presents the missing required items to the job seeker terminal 30.

[0033] <Public Section 113> The disclosure unit 113 discloses the job seeker registration information registered by the registration unit 112 so as to make it public to employers. Specifically, the disclosure unit 113 discloses the job seeker registration information registered by the registration unit 112 that has passed a predetermined screening to employers who use the job search support system 1. The screening involves, for example, determining whether the content and amount of information written in the job seeker registration information meets predetermined standards. Job seeker registration information that does not pass the screening is not disclosed to employers, and the job seeker is required to correct it. In other words, job seeker registration information is provisionally registered until it is made public, and is officially registered and disclosed to employers once it passes the screening.

[0034] A job seeker whose job seeker registration information has been made public to an employer (full registration) becomes a target for job seeker searches by employers in the recruitment support service provided by the job search support system 1, and is able to receive scouting documents from employers. In addition, a fully registered job seeker can search for jobs in the recruitment support service provided by the job search support system 1, and can apply for jobs.

[0035] <Reception Department 114> The reception unit 114 is configured to receive input from the job seeker terminal 30 of the registered job seeker. Specifically, the reception unit 114 receives the job seeker's selection of at least one candidate job offer from among job offers to which the job seeker can apply. A "candidate job offer" is, for example, a job offer selected by the job seeker as a job offer to be viewed (i.e., the job seeker has instructed the job seeker terminal 30 to display the contents of the job posting), or a job offer selected by the job seeker as a job offer to be assigned a candidate label (bookmark) (i.e., the job seeker has instructed the job seeker to assign a candidate label). Job offers assigned with a candidate label are registered, for example, in a bookmark list prepared for each job seeker. By referring to the bookmark list, the job seeker can select a job offer to view, apply for, or take other action on from among the job offers assigned with a candidate label. Furthermore, a "job offer to which the job seeker can apply" is a job offer registered in the job search support system 1 for which the job seeker meets the recruitment requirements.

[0036] The receiving unit 114 may accept a selection by the job seeker of at least one candidate job offer from among the "recommended job offers" presented by the presentation unit 115, which will be described later. For example, the receiving unit 114 may accept a selection of a recommended job offer to be registered in the bookmark list from among the recommended job offers presented based on the first reference information, which will be described later, immediately after the job seeker registration information is registered.

[0037] A job posting, which is the content of a job offer, lists job information for multiple items such as the name of the position being recruited, job content and working conditions (annual salary, job type, industry, work location, work style, work environment, etc.), application qualifications (skills), desired personality, selling points, etc. Job postings may also include items such as the job title, headline, and information about the employer (company size (sales, number of employees, etc.), industry, etc.).

[0038] <Presentation part 115> The presentation unit 115 is configured to present at least one recommended job offer to the job seeker from among job offers to which the job seeker can apply, based on the job seeker characteristic information and the reference information. The presentation unit 115 extracts recommended job offers from among job offers to which the job seeker can apply, for example, in a procedure described below, and displays the extracted recommended job offer on the job seeker terminal 30, for example, as a "recommended job offer."

[0039] The job seeker characteristic information is at least one of the job seeker registration information and the job seeker's behavioral history regarding job offers. The "behavioral history regarding job offers" includes, for example, viewing a job offer, applying for a job offer, registering a job offer in a bookmark list, replying to a scouting document sent based on the job offer, and passing the job offer screening (document screening, interview screening, etc.). The behavioral history is composed of information indicating the job offer that was the target or starting point of the behavior and the content of the behavior. The behavioral history is recorded for each job seeker (i.e., linked to the job seeker registration information) in, for example, a job seeker database. When extracting recommended job offers, the presentation unit 115 may refer to both the job seeker registration information and the behavioral history, or may refer to only one of the job seeker registration information and the behavioral history.

[0040] The reference information used by the presentation unit 115 when extracting recommended job offers is stored, for example, in the storage unit 12. The reference information includes first reference information and second reference information that are used differently depending on the time after the registration of the registration information. The "time after registration" refers to the time after the date or time when the job seeker registration information is registered (provisionally registered) by the registration unit 112. Furthermore, for example, the first reference information and the second reference information may be used differently depending on whether a predetermined number of days have passed since the date of registration until the present.

[0041] The presentation unit 115 determines whether the time after registration of the job seeker registration information for which recommended job offers are to be presented is the time to use the first reference information. By setting in advance the time to use the first reference information, it becomes possible to present appropriate recommended job offers according to the time after registration of the job seeker registration information. The time to use the first reference information may be, for example, a fixed period from provisional registration by the registration unit 112 (e.g., one week from provisional registration), or a period until a predetermined event occurs (e.g., publication by the publication unit 113).

[0042] Therefore, if the time after registration of the job seeker registration information for which recommended job offers are presented is before the job seeker registration information is made public, the presentation unit 115 may present recommended job offers based on the job seeker characteristic information and the first reference information. If the time after the job seeker registration information is made public is after the job seeker registration information is made public, the presentation unit 115 may present recommended job offers based on the job seeker characteristic information and the second reference information. This allows the presentation of recommended job offers based on the minimum information for job seeker registration information that has not yet been made public and is insufficient in content, for example, information close to the minimum required for registration. For job seeker registration information that has been made public and has a certain level of content, the presentation unit 115 may present recommended job offers with increased accuracy based on more information. Therefore, by presenting job offers registered in the job search support system 1 at a certain level to job seekers immediately after registration, the value of the recruitment support service provided by the job search support system 1 can be immediately presented and continuous use of the recruitment support service can be encouraged. This allows the presentation of recommended job offers to relatively motivated job seekers immediately after registration, leading to actions such as viewing job offers, applying for jobs, and adding jobs to their bookmark lists. Furthermore, after full registration, job seekers can be provided with higher quality job recommendations.

[0043] The presentation unit 115 presents recommended job offers based on the first reference information, for example, immediately after the job seeker registration information is registered (i.e., at the timing when provisional registration is completed). Furthermore, the presentation unit 115 presents recommended job offers based on the second reference information, for example, at the timing when the job seeker (user) performs an action such as logging in, editing the job seeker registration information, or replying to a scout document after the job seeker registration information is made public.

[0044] The first reference information includes a first correlation between the job seeker characteristic information and the job offer (such as information included in a job advertisement or information associated with the job offer). The second reference information includes a second correlation between the job seeker characteristic information and the job offer, which is different from the first correlation. In other words, the first reference information and the second reference information are different information from each other.

[0045] Specifically, the elements of the job seeker characteristic information included in the first correlation (i.e., the content of the job seeker characteristic information referenced together with the first reference information when extracting recommended job offers) differ from the elements of the job seeker characteristic information included in the second correlation (i.e., the content of the job seeker characteristic information referenced together with the second reference information when extracting recommended job offers). For example, the first correlation describes the job type (or industry) and annual income of the job seeker registration information, among the job seeker characteristic information, and their relationships with various job offers (conditions for determining a job offer as a recommended job offer), while the second correlation describes the job type (or industry), annual income, and bookmark list of the job seeker registration information, among the job seeker characteristic information, and their relationships with various job offers (conditions for determining a job offer as a recommended job offer). In other words, the second correlation may include a relationship between the bookmark list and job offers that is not included in the first correlation.

[0046] In this way, when registration information is used as job seeker characteristic information, it is preferable that the number of items in the registration information referred to by the presentation unit 115 together with the second reference information is greater than the number of items in the registration information referred to by the presentation unit 115 together with the first reference information. This makes it possible to provide simple job recommendations using the first reference information to job seekers whose job seeker characteristic information includes a small number of items (whose contents are described), while providing more accurate job recommendations using the second reference information to job seekers whose job seeker characteristic information includes a large number of items.

[0047] <1st reference information> When the presentation unit 115 refers to the job seeker registration information as the job seeker characteristic information, the first reference information is prepared as, for example, information including extraction conditions for extracting recommended job offers from job offers to which a job seeker can apply (e.g., a code describing the extraction conditions). Specifically, the first reference information may include, as the first correlation (extraction condition), first similarity determination information indicating a job type or industry that is the same as or similar to the job type or industry included in the job seeker registration information, and an annual income condition. When this first reference information is used depending on the time after the registration of the job seeker registration information for presenting recommended job offers (in other words, when the time after the registration of the job seeker registration information for presenting recommended job offers is the time of use of the first reference information), the presentation unit 115 presents, as recommended job offers, job offers that include a job type or industry that is the same as or similar to the job type or industry included in the job seeker registration information and that satisfy the annual income condition for the annual income included in the job seeker registration information. As a result, if job seeker registration information including at least the job type or industry and annual salary is registered, job offers that the user job seeker finds attractive in terms of annual salary can be presented as recommended job offers.

[0048] The "job type or industry in the job seeker registration information" referenced by the presentation unit 115 includes the job seeker's desired job type or industry as well as the job seeker's current job type or industry. The similarity between the job type (current job type or desired job type) or industry (current industry or desired industry) in the job seeker registration information and the job type or industry in the job posting is determined based on "first similarity determination information" (information defining a similarity range of job types or industries) included in the first reference information. The first similarity determination information may define at least one similar keyword for a keyword indicating the job type or industry, or may define a similarity criterion for a feature amount obtained by vectorizing a keyword indicating the job type or industry. The similarity criterion for a feature amount is a threshold value for the difference (vector distance) of the feature amounts between two job types or industries. For example, job types or industries whose feature amount difference is less than the threshold value (or whose cosine similarity is equal to or greater than the threshold value) are determined to be similar to each other.

[0049] The "annual income condition" included in the first reference information may be, for example, that the annual income of the job offer satisfies the desired annual income included in the job seeker registration information (that is, that the annual income range overlaps with the desired annual income), or that the annual income of the job offer (e.g., the minimum annual income in the case of an annual income range) is higher than the job seeker's current annual income (the annual income included in the job seeker registration information). Job offers that satisfy such annual income conditions are determined to be recommended job offers by the presenting unit 115. In addition, a lower limit value may be set by subtracting the job seeker's current annual income from the annual income of the job offer. In other words, the annual income condition may be set to an amount of increase relative to the job seeker's current annual income.

[0050] When the presentation unit 115 references the job seeker registration information as the job seeker characteristic information, the first reference information may include, as the first correlation, second similarity judgment information indicating attributes identical or similar to attributes included in the job seeker registration information, or third similarity judgment information indicating the similarity of behavioral histories, and a judgment criterion for the behavioral history. When the presentation unit 115 uses this first reference information depending on the time after registration of the job seeker registration information for which recommended job offers are presented, the presentation unit 115 may present, as recommended job offers, job offers whose behavioral history of a reference job seeker satisfies the judgment criterion. A reference job seeker is another job seeker whose registration information has attributes identical or similar to attributes included in the job seeker registration information, or another job seeker whose behavioral history is similar to that of the job seeker. This allows the user job seeker to be presented with job offers that are of high interest to a reference job seeker who has attributes or behavioral history similar to the user's own. Note that the reference job seeker is extracted from job seekers whose registration information is registered in the job seeker database of the job search support system 1.

[0051] Examples of "attributes included in the job seeker registration information" include items such as occupation, industry, skills, and qualifications. The similarity between an attribute in the job seeker registration information and an attribute in the registration information of another job seeker is determined based on "second similarity judgment information" (information defining the range of similarity of the attribute) included in the first reference information. The second similarity judgment information may define at least one similar keyword for a keyword indicating the attribute, or may define a similarity criterion for a feature obtained by vectorizing a keyword indicating the attribute or a sentence. The similarity criterion for a feature is a threshold value for the difference (vector distance) between the feature values ​​of two attributes; for example, attributes whose difference in feature values ​​is less than the threshold value (or whose cosine similarity is equal to or greater than the threshold value) are determined to be similar to each other.

[0052] The presentation unit 115 may extract reference job seekers based on the similarity of the attributes of a single item (for example, only job type), or may extract reference job seekers based on the similarity of the attributes of each of multiple items (for example, job type and skill). In addition, the similarity of the behavioral histories between job seekers is determined based on "third similarity determination information" (information that defines the range of similarity in the behavioral histories) included in the first reference information.

[0053] When the first reference information including the second similarity judgment information or the third similarity judgment information is used, the behavioral history used to extract reference job seekers and recommended job offers includes at least one of viewing job offers, applying for job offers, registering job offers in a bookmark list, and replying to a scouting message sent based on a job offer. The third similarity judgment information, for example, defines a threshold for determining similarity based on the number of common job offers among the job offers that two job seekers have viewed through their respective actions. In other words, two job seekers who have viewed a number of common job offers equal to or greater than a threshold are determined to have similar behavioral histories based on the third similarity judgment information. The third similarity judgment information may also define criteria for determining similarity based on a combination of multiple types of behavioral histories (e.g., determining similarity based on the number of common job offers among the job offers applied for and the number of common job offers among the job offers replied to the scouting message). For example, a judgment formula may be used that compares a value obtained by adding weights for the number of common job offers for each behavioral history with a threshold.

[0054] When the first reference information including the second similarity judgment information or the third similarity judgment information is used, the criterion for extracting recommended job offers is that the behavior score calculated based on the behavioral histories of multiple reference job seekers exceeds a predetermined threshold. This makes it possible to extract recommended job offers according to the active behavior of the reference job seekers regarding job offers, thereby presenting recommended job offers that are likely to lead to similar behavior for the user job seeker himself.

[0055] Specifically, the presentation unit 115 calculates a behavior score for each job posting using at least one of the number of reference job seekers who viewed the job posting, the number of reference job seekers who applied, the number of reference job seekers registered in a bookmark list, and the number of replies to the scout document. The behavior score is set so that the greater the number of times the job posting has been the subject of behavioral history (i.e., the number of reference job seekers who have been the subject of behavior), the higher the behavior score. The presentation unit 115 may weight the behavioral history according to the type of behavioral history when calculating the behavioral score. For example, the presentation unit 115 may set a weighting coefficient for the number of application behavior histories to a higher value than the weighting coefficient for the number of other behavioral histories, and may use the sum of values ​​obtained by multiplying the number of times each behavioral history is multiplied by the respective weighting coefficient as the behavioral score. This allows job postings with a high application rate from reference job seekers to be presented as recommended job postings with priority.

[0056] The behavior score may also be calculated based on behavioral transition rates such as the percentage of people who viewed a job posting and then applied for that job posting (the value obtained by dividing the number of applications by the number of views), the percentage of people who applied to a job posting and then passed the screening process for documents, interviews, etc. for that job posting or were hired (the value obtained by dividing the number of people who passed the screening process or the number of people hired by the number of applications).The behavior score may also be calculated based on behavioral transition rates such as the percentage of people who received a scouting document from a recruiter and then applied for that job posting (the value obtained by dividing the number of applications by the number of scouting messages sent), and the percentage of people who applied to a job posting based on a scouting document and then passed the screening process for documents, interviews, etc. for that job posting or were hired (the value obtained by dividing the number of people who passed the screening process or the number of people hired by the number of scouting messages sent).In other words, the behavior score may be set to be higher as these behavioral transition rates increase.

[0057] The first reference information may be a first recommended job offer determination model that has been trained to be capable of inputting job seeker characteristic information and outputting recommended job offers. In this case, when the first reference information is used depending on the time after registration of the job seeker registration information for which recommended job offers are to be presented, the presentation unit 115 inputs the job seeker characteristic information into the first recommended job offer determination model of the artificial intelligence unit 120 and causes the first recommended job offer determination model to output recommended job offers. This makes it possible to present recommended job offers based on a large amount of job seeker characteristic information or information equivalent thereto.

[0058] The first recommended job vacancy determination model is a learning model trained using data on job seeker characteristic information for learning (registration information and / or behavioral history of the job seeker to be trained) and data on job vacancies included in the behavioral history of the job seeker to be trained (for example, job vacancies to which the job seeker to be trained has applied, job vacancies for which the job seeker to be trained has received a scouting document, job vacancies for which the job seeker to be trained has passed screening (document screening or interview screening), job vacancies for which the job seeker to be trained has been hired, etc.) as training data. Candidate job seekers to be trained include all job seekers whose registration information is registered in the job seeker database. In this case, the parameters of the first recommended job vacancy determination model calculated or tuned by training correspond to the first correlation. In particular, the first recommended job vacancy determination model may be a learning model trained using data on the registration information of the job seeker to be trained and data on job vacancies to which the job seeker to be trained has applied, job vacancies for which the job seeker to be trained has passed screening, or job vacancies for which the job seeker to be trained has been hired, as training data.

[0059] When registration information is used as job seeker characteristic information, the first recommended job offer determination model may be trained so as to be able to determine recommended job offers based on the occupation or industry and annual income included in the registration information. In this case, the presentation unit 115 inputs the job seeker registration information into the first recommended job offer determination model and causes the first recommended job offer determination model to output recommended job offers. As a result, if job seeker registration information including at least the occupation or industry and annual income is registered, the first recommended job offer determination model can extract job offers that the user job seeker finds attractive in terms of annual income.

[0060] The first recommended job offer determination model may be a learning model that has learned the relationship (degree of association) between the registration information and behavioral history of multiple job seekers to be studied. In this case, when the first reference information is used depending on the time after registration of the job seeker registration information for which recommended job offers are presented, the presentation unit 115 inputs job seeker characteristic information into the first recommended job offer determination model and causes the first recommended job offer determination model to output recommended job offers based on the job offer information included in the learned behavioral history. As a result, job offers included in the behavioral history of other job seekers whose registration information and behavioral history are similar to those of the user job seeker are extracted as recommended job offers. Therefore, recommended job offers that are of high interest to job seekers similar to the user job seeker can be presented.

[0061] The behavioral history used for inputting or learning the first recommended job determination model includes at least one of viewing a job, applying for a job, registering a job in a bookmark list, replying to a scout document sent based on a job, and passing a job screening. This makes it possible to extract recommended job openings based on the active behavior of job seekers other than the user regarding job openings or the screening results, and therefore present recommended job openings that the job seeker user is likely to be interested in or that have a high probability of being hired.

[0062] The relevance between registered information and behavioral history is an index that indicates the likelihood that a job seeker who has the registered information will take an action included in the behavioral history (for example, viewing a job posting, applying for a job posting, adding the job posting to a bookmark list, replying to a scouting letter, passing a screening by an employer, etc.). In other words, the higher the relevance, the more likely a job seeker who has the registered information will take an action such as viewing or applying for a job posting included in the behavioral history.

[0063] In the first recommended job determination model that has learned the degree of association between registered information and behavioral history, when job seeker registration information is input as job seeker characteristic information, for example, job openings that are included in related behavioral history from the learned behavioral history and have a certain degree of association with the input job seeker registration information, or job openings that are included in multiple related behavioral histories at a certain frequency or more, are output as recommended job openings.

[0064] Furthermore, in the first recommended job offer determination model, when a behavioral history is input as job seeker characteristic information, for example, a job offer included in a similar behavioral history that is similar to the input behavioral history among the learned behavioral histories, or a job offer that is included more than a certain frequency in a plurality of the similar behavioral histories, is output as a recommended job offer. The similarity of the behavioral histories is determined, for example, based on the number of common job offers among the job offers that are the subject of each of the two behavioral histories to be compared (for example, whether the number of common job offers is more than a certain number).

[0065] The first recommended job offer determination model may be a learning model that is trained to be able to determine recommended job offers based on the behavioral history of a reference job seeker regarding job offers. The reference job seeker here is another job seeker whose registered information has the same or similar attributes as the attributes included in the job seeker registration information, or another job seeker whose behavioral history is similar to that of the job seeker.

[0066] The first recommended job offer determination model may be a generation AI including a large-scale language model. In this case, the presentation unit 115 inputs job seeker characteristic information, inputs a prompt including an instruction to output recommended job offers to the first recommended job offer determination model, and causes the first recommended job offer determination model to output the recommended job offers. Furthermore, the presentation unit 115 may input, to the first recommended job offer determination model, a prompt that includes, as input and output samples, for example, one or more samples of job seeker characteristic information and one or more corresponding samples of recommended job offers, in addition to the instruction to output recommended job offers and the job seeker characteristic information.

[0067] When the first reference information is used depending on the time after registration of the job seeker registration information for which recommended job offers are presented and the job seeker registration information is used as job seeker characteristic information, the presentation unit 115 may calculate a job offer score for each job offer based on the job seeker registration information and the first reference information, and determine recommended job offers based on the level of the job offer score. This makes it possible to present recommended job offers in various modes based on the job offer score. For example, the presentation unit 115 presents, on the job seeker terminal 30, job offers whose calculated job offer score is equal to or greater than a predetermined threshold as recommended job offers. The presentation unit 115 may also present multiple recommended job offers in descending order of job offer score. Furthermore, the presentation unit 115 may cause the job seeker terminal 30 to display the job offer score (degree of recommendation).

[0068] The job offer score may be calculated based on a match score calculated based on a comparison between the details included in the job seeker registration information and the details included in the job offer, and a behavior score calculated based on the behavioral history of reference job seekers with other registration information that has the same or similar attributes as the attributes included in the job seeker registration information. This makes it possible to present recommended jobs taking into consideration whether the job offer is suitable for the user job seeker and the level of interest of reference job seekers who are similar to the user job seeker.

[0069] The details contained in the job seeker registration information and the job posting that are compared when calculating the match score include, for example, current job type, desired job type, current industry, desired industry, skills, qualifications, current annual salary, and desired annual salary. The first reference information for calculating the job posting score defines the match score using, for example, a table or a function, depending on the similarity between the details contained in the job seeker registration information and the details contained in the job posting (the content of the job posting). The similarity is defined, for example, by the distance between a first feature value obtained by vectorizing words or sentences contained in the job seeker registration information and a second feature value obtained by vectorizing words or sentences contained in the job posting. For example, cosine similarity is used as the similarity, and the closer the cosine similarity is to 1, the greater the similarity. The first reference information may also define the match score depending on the difference between the current annual salary or desired annual salary in the job seeker registration information and the annual salary in the job posting (for example, the increase in current annual salary). Furthermore, the first reference information may include a match score calculation model that has been trained to be able to input the job seeker registration information and the job offer and output the match score.

[0070] The behavioral score used to calculate the job offer score is the same as the behavioral score used in the "criteria for extracting recommended job offers." That is, the behavioral score is set to be higher the greater the number of times the job offer has been targeted in its behavioral history (i.e., the number of reference job seekers whose behavior has been targeted). Furthermore, the presentation unit 115 may weight the behavioral score according to the type of behavioral history when calculating the behavioral score. Furthermore, the first reference information may include a behavioral score calculation model that is trained to input job seeker registration information and output a behavioral score based on the behavioral history of the reference job seeker.

[0071] The job offer score is calculated, for example, by adding the values ​​obtained by multiplying the match score and the behavior score by weighting coefficients.

[0072] The first reference information for calculating the job vacancy score may be a job vacancy score calculation model that has been trained to be able to input the job seeker registration information and the job vacancy that is the target of score calculation and output the job vacancy score. In this case, the presentation unit 115 inputs the job seeker registration information and the target job vacancy into the job vacancy score calculation model, and causes the job vacancy score calculation model to output the job vacancy score of the target job vacancy.

[0073] The job vacancy score calculation model is a learning model trained using training data of job seeker registration information and job offers and corresponding job offers scores as training data. The job vacancy score calculation model may be a generative AI including a large-scale language model. In this case, the presentation unit 115 inputs the job seeker registration information and job offers, inputs a prompt including an instruction to output the job offer score to the job vacancy score calculation model, and causes the job vacancy score calculation model to output the job offer score. Furthermore, the presentation unit 115 may input, to the job vacancy score calculation model, a prompt in which, for example, one or more samples of job seeker registration information and job offers and one or more corresponding job offer score samples are inserted as input and output samples, in addition to the instruction to output the job offer score, the job seeker registration information, and the job offer.

[0074] The first reference information may include both information including extraction conditions for extracting recommended job offers by referring to the job seeker registration information (e.g., code in which the extraction conditions are written) and a first recommended job offer determination model. For example, the presentation unit 115 may present to the job seeker at least one first recommended job offer extracted using the information including the extraction conditions and at least one second recommended job offer extracted using the first recommended job offer determination model. Furthermore, the first reference information may include both information for calculating a job offer score by referring to the job seeker registration information (e.g., definitions of the match score and the behavioral score) and a job offer score calculation model. The presentation unit 115 may present to the job seeker at least one first recommended job offer extracted based on the first job offer score calculated using the information for calculating the job offer score and at least one second recommended job offer extracted based on the second job offer score calculated using the job offer score calculation model.

[0075] This allows recommended jobs to be extracted using a combination of two different conditions or models (for example, extraction conditions and a first recommended job determination model) as reference information, making it possible to stably present appropriate job offers that suit job seekers. In particular, for job seekers with relatively little registered information, such as job seekers who have just registered, the first recommended job determination model, which is a learning model, may not be able to successfully extract appropriate recommended job offers, but by using information including extraction conditions for extracting recommended job offers on a rule-based basis as the first reference information, it is possible to stably present appropriate recommended job offers to job seekers.

[0076] FIG. 5 is a diagram showing an example of a recommended job offer display screen RD displayed on the job seeker terminal 30. The recommended job offer display screen RD displays at least one recommended job offer RJ. The recommended job offer RJ displays, for example, the job offer title JT, expected annual salary JI, job type JO, work location JL, organization name JC, industry JB, etc. of the recommended job offer. By selecting any recommended job offer RJ on the job seeker terminal 30, the job seeker can view the job offer corresponding to the recommended job offer RJ, register it in their bookmark list, etc.

[0077] The presentation unit 115 may present recommended job offers based on the job seeker registration information and the first reference information to a job seeker who is a user for whom a certain period of time has passed since the last time he or she used (logged in) the job search support system 1. In other words, the presentation unit 115 may determine that the time to use the first reference information is a period after the job seeker's last login, among periods after the registration of the job seeker registration information for which recommended job offers are presented.

[0078] <Second reference information> The second reference information is referenced when the time after registration of the job seeker registration information that presents recommended job offers is not the time to use the first reference information (i.e., the time to use the second reference information). As described above, the second reference information includes a second correlation between the job seeker characteristic information and job offers, which is different from the first correlation included in the first reference information. In other words, the algorithm for determining recommended job offers using the second reference information is different from the algorithm for determining recommended job offers using the first reference information.

[0079] The second reference information, like the first reference information, may be information including extraction conditions for extracting recommended job offers by referring to job seeker registration information (for example, a code describing the extraction conditions), or may be a second recommended job offer determination model that has been trained to be able to input job seeker characteristic information (job seeker registration information or behavioral history) and output recommended job offers.

[0080] The second recommended job vacancy determination model uses different data for learning from the first recommended job vacancy determination model. For example, the second recommended job vacancy determination model uses data of registered information for which the period after registration is the period when the first reference information is used (for example, registered information before publication by publication unit 113) for learning, and the second recommended job vacancy determination model uses data of registered information for which the period after registration is the period when the second reference information is used (for example, registered information after publication by publication unit 113) for learning.

[0081] <Revision proposal section 116> The correction suggestion unit 116 is configured to suggest to the job seeker that the job seeker correct his or her registered job seeker information. Specifically, the correction suggestion unit 116 suggests corrections to the job seeker registration information based on the candidate job offers and the third reference information received by the reception unit 114. "Corrections" include adding words or sentences to the job seeker registration information, deleting words or sentences included in the job seeker registration information, and replacing words or sentences included in the job seeker registration information (for example, correcting expressions or summarizing sentences). For example, the correction suggestion unit 116 displays words that are recommended to be included in the job seeker registration information as corrections on the job seeker terminal 30 as recommended keywords.

[0082] The third reference information includes a correlation between candidate job offers and correction items. The third reference information is stored, for example, in the storage unit 12. The third reference information may be a correction determination model that has been trained to be able to input at least one candidate job offer and output correction items. In this case, the correction suggestion unit 116 inputs the content (job posting) of at least one candidate job offer into the correction determination model of the artificial intelligence unit 120 and causes the correction determination model to output correction items.

[0083] The correction determination model is a learning model that determines corrections to be made to correct registered job seeker information to make it more suitable for candidate job offers. The correction determination model is a learning model that is trained using data from job postings for learning and data from registered job seekers that are suitable for the job posting (for example, those who have received a scout based on the job offer or whose job offer has been concluded) as training data. In this case, the parameters of the correction determination model that have been calculated or tuned through learning represent the correlation between candidate job offers and corrections.

[0084] The correction determination model may be a generative AI including a large-scale language model. In this case, the correction suggestion unit 116 inputs the job posting of the candidate job, inputs a prompt including an instruction to output corrections to the job seeker registration information to the correction determination model, and causes the correction determination model to output the corrections. Furthermore, in addition to the instruction to generate and output the corrections and the job posting of the candidate job, the correction suggestion unit 116 may input, as input and output samples, a prompt into which, for example, samples of job postings of one or more candidate job postings and one or more corresponding samples of corrections are inserted to the correction determination model.

[0085] The correction suggestion unit 116 may extract recommended information from the registered information of a first reference job seeker and suggest adding the recommended information as a correction. A first reference job seeker is another job seeker who has a history of actions related to a candidate job offer. "Actions related to a candidate job offer" include, for example, actions taken by a recruiter toward a job seeker, such as receiving a scout document based on the candidate job offer and passing an examination (document screening, interview screening, etc.) for the candidate job offer. As a result, corrections are suggested for a candidate job offer in which the user job seeker is interested, by referring to the registered information of other job seekers who have experienced actions such as receiving a scout document and passing an examination. Therefore, corrections that are likely to be taken in relation to the candidate job offer can be suggested, which are more likely to be taken by the user job seeker.

[0086] Note that "actions related to a candidate job offer" also include "actions related to a similar job offer that is similar to the candidate job offer." A "similar job offer" is a job offer that a job seeker can apply for, whose similarity to the candidate job offer, as determined based on the information in the job advertisement, is equal to or exceeds a predetermined threshold. The similarity between two job offers is calculated, for example, by comparing features such as vector data of words or sentences included in the job advertisement, or by determining the similarity using a learning model (large-scale language model). The revision suggestion unit 116 refers to the job seeker database and extracts first reference job seekers who have a history of actions related to the candidate job offer or similar job offers.

[0087] The first reference job seeker may have a history of receiving scout documents created based on the candidate job offer or similar job offers to the candidate job offer. This allows the user (job seeker) to be suggested to modify his / her job seeker registration information to make it easier for him / her to receive scout documents from recommended job offers.

[0088] The correction suggestion unit 116 may extract recommended information by comparing words or sentences contained in the registration information of the first reference job seeker with words or sentences contained in the registration information of multiple job seekers other than the first reference job seeker (i.e., job seekers who do not have a history of actions such as receiving scouting documents from candidate job openings) among the job seekers registered in the job seeker database.

[0089] A specific procedure for extracting recommended information is, for example, as follows: First, the correction suggestion unit 116 uses natural language processing to extract first elements, which are words or sentences included in the registered information of the first reference job seeker. Similarly, the correction suggestion unit 116 uses natural language processing to extract second elements, which are words or sentences included in the registered information of job seekers other than the first reference job seeker. Next, the correction suggestion unit 116 extracts, from the extracted first elements, words or sentences that have not been extracted as second elements as recommended information.

[0090] The recommended information proposed as corrections by the correction suggestion unit 116 may be words or sentences extracted from the registered information of a plurality of first reference job seekers based on the frequency of appearance of the words or sentences contained in the registered information. This makes it possible to suggest corrections to the job seeker, who is the user, that can increase the expected value of an action, such as receiving a scouting document, from the recommended job offer.

[0091] Specifically, the correction suggestion unit 116 calculates the frequency of appearance of these sentence components on a word or sentence basis for the registered information of a plurality of first reference job seekers extracted from the job seeker database. The correction suggestion unit 116 extracts, as recommended information, words or sentences whose frequency of appearance is equal to or greater than a predetermined threshold from the sentence components included in the registered information of a plurality of first reference job seekers.

[0092] In particular, the correction suggestion unit 116 may calculate the frequency of occurrence of sentence components extracted by comparing the registered information of the first reference job seeker with the registered information of multiple job seekers other than the first reference job seeker, as described above, and extract recommended information based on the frequency of occurrence.

[0093] Furthermore, the correction suggestion unit 116 may determine recommended information according to the magnitude of the appearance score of a sentence component based on a first appearance frequency in the registered information of the first reference job seeker and a second appearance frequency in the registered information of job seekers other than the first reference job seeker. The correction suggestion unit 116 increases the appearance score of the sentence component the higher the first appearance frequency, and increases the appearance score of the sentence component the lower the second appearance frequency. The correction suggestion unit 116 may also weight each of the first appearance frequency and the second appearance frequency. For example, the correction suggestion unit 116 may determine the appearance score for a certain word by subtracting a value corresponding to the second appearance frequency (e.g., a value obtained by multiplying the second appearance frequency by a weighting coefficient) from a value corresponding to the first appearance frequency (e.g., a value obtained by multiplying the first appearance frequency by a weighting coefficient), and determine words whose appearance score is equal to or greater than a threshold as recommended information.

[0094] The recommended information may be words or phrases that indicate skills, words or phrases that indicate qualifications, or words or phrases contained in the job summary. This simplifies the content of the corrections, making it easier for the job seeker (user) to accept the suggestions. It also promotes an increase in the expected value of actions, such as receiving a scouting document. Note that a "job summary" is a text that describes the job seeker's work history. For example, the job summary may include a chronological description of the department, role or job type, job content, results or achievements, etc.

[0095] The revision suggestion unit 116 may determine the importance of job descriptions included in the candidate job description and may also suggest adding important descriptions with high importance as revisions. The importance is a value that is set in advance for each job description in the candidate job description, or is determined based on the frequency of occurrence of search keywords used in job seeker searches. This allows the job seeker (user) to be prompted to revise their registered information so that it includes words or phrases that the employer considers important.

[0096] The "job details included in the candidate job posting" may be keywords (e.g., words representing individual skills) or may be keyword categories that encompass multiple keywords (e.g., "IT skills," "accounting skills," etc.). In other words, importance may be set for each individual keyword, or for each keyword category. When importance is set for a keyword category, the same importance is set for all keywords included in one keyword category. For example, if the importance for "IT skills" is set to "1," a keyword included in the "IT skills" category (e.g., "app development") is determined to have an importance of "1."

[0097] Furthermore, the "job details included in the candidate job posting" include details that are not listed in the job posting. That is, the "job details" may be keywords that are listed in the job posting itself, or important words that are not listed in the job posting but are stored in association with the job posting as information about the job. For example, even if a keyword related to the abilities that the employer is looking for in a job seeker, such as "communication skills," is not listed in the job posting itself, it may be stored in the database in association with the job posting as an important (highly important) important word in advance by the employer. The revision suggestion unit 116 presents the addition of such important words as revisions to the job seeker's registered information.

[0098] When the importance is determined based on the frequency of appearance of the search keyword, the revision suggestion unit 116 determines the importance of the words included in the recommended job offers by referring to importance determination information, such as the frequency of appearance of each word or a table that lists the importance based on the frequency of appearance. The importance determination information is stored, for example, in the storage unit 12. In this case, the importance is set to be higher as the frequency of appearance increases. The frequency of appearance of each word included in the importance determination information is calculated, for example, based on the search history of all job seekers in the recruitment support service provided by the job search support system 1. This allows the user, the job seeker, to modify the job seeker registration information to make it more likely to be found in a job seeker search by a job seeker.

[0099] For example, the revision suggestion unit 116 determines that a job listing (word or sentence) whose determined importance is equal to or greater than a predetermined threshold and that is not included in the current job seeker registration information is an important listing, and suggests that it be added to the job seeker registration information.

[0100] The correction suggestion unit 116 may extract recommended information from important matters based on the frequency of appearance in the registration information of a second reference job seeker, and may suggest adding the recommended information as a correction. The second reference job seeker here is another job seeker to whom a scout document created based on a job posting for a candidate job or a similar job posting similar to the candidate job posting is sent, or another job seeker whose registration information is viewed by a recruiter for the candidate job posting or a similar job posting. This makes it possible to encourage the job seeker (user) to add information included in the registration information of the second reference job seeker, who is attracting a lot of attention from the candidate job posting or a similar job posting, to his or her own registration information.

[0101] Specifically, the correction suggestion unit 116 extracts, from among the job listings (words or sentences) determined to be important listings, job listings whose frequency of appearance in the registration information of multiple second reference job seekers is equal to or exceeds a predetermined threshold as recommended information. This allows the job seeker (user) to correct the job seeker registration information to make it easier for the employer to send scouting documents and to view the registration information.

[0102] The second reference job seeker may also be another job seeker whose registration information includes details that are highly similar to the details included in the job seeker registration information. This makes it possible to prompt the job seeker (user) to add to his or her own registration information the information included in the registration information of the second reference job seeker whose attributes are similar to those of the job seeker (user).

[0103] Here, the "similarity of the description" is defined as the difference between a first feature obtained by vectorizing a word or sentence included in the job seeker's registration information and a second feature obtained by vectorizing a word or sentence included in the registration information of another job seeker, and the smaller the difference, the greater the similarity. For example, the correction suggestion unit 116 extracts, as the second reference job seeker, a job seeker whose registration information includes a description such that the difference between the first feature and the second feature is less than a threshold.

[0104] The correction suggestion unit 116 may input the job posting of the candidate job into the recommended information extraction model, cause the recommended information extraction model to output recommended information, and suggest adding the recommended information as a correction. The recommended information extraction model is an example of the correction determination model described above, and is a learning model that takes a job posting as input and is trained to be able to extract job posting details with high importance contained in the job posting as recommended information. This makes it possible to suggest corrections based on examples of extraction of recommended information (important details) from a large number of job postings.

[0105] The recommendation information extraction model is a learning model trained using training job posting data and data indicating the job requirements of high importance in the job posting as training data. The recommendation information extraction model may be a generative AI including a large-scale language model. In this case, the revision suggestion unit 116 inputs the job posting of the candidate job, inputs a prompt including an instruction to output recommended information to the recommendation information extraction model, and causes the recommendation information extraction model to output the recommended information. Furthermore, in addition to the instruction to extract and output recommended information and the job posting of the candidate job, the revision suggestion unit 116 may input a prompt into the recommendation information extraction model as input and output samples, for example, including samples of job postings of one or more candidate jobs and one or more corresponding samples of recommended information.

[0106] The correction suggestion unit 116 may suggest additions, deletions, or edits to the entries as corrections depending on the amount of information included in the job seeker registration information. For example, the correction suggestion unit 116 may suggest adding information to items in the job seeker registration information that have not been filled in, items in which the amount of information entered (number of characters) is less than a predetermined value (for example, the average value of other job seekers), etc. Specifically, after determining the items that need to be added in this way, the correction suggestion unit 116 may extract the above-mentioned recommended information for the items that need to be added and suggest adding the recommended information.

[0107] Furthermore, for example, the correction suggestion unit 116 may suggest deleting words or sentences, summarizing sentences, etc. for items in the job seeker registration information that have more entry (number of characters) than a predetermined value (for example, the average value of other job seekers, etc.) Specifically, after determining items that require reduction in the entry amount in this way, the correction suggestion unit 116 may present the deletion content (words or sentences to be deleted), summary content (summary sentences and original sentences to be replaced with the summary sentences), etc. for the items that require reduction.

[0108] The correction suggestion unit 116 may suggest corrections based on the candidate job offer on the candidate job offer viewing screen and display the corrections together with the contents of the candidate job offer. This allows the job seeker (user) to check the corrections in comparison with the candidate job offer, thereby encouraging the job seeker to correct their registered information.

[0109] The correction suggestion unit 116 may display an object that accepts instructions to edit the job seeker registration information along with the correction items on the candidate job offer viewing screen. This allows the job seeker registration information to be edited immediately from the candidate job offer viewing screen, making it easier to guide the job seeker (user) to correct his or her own registration information. An example of an object that accepts instructions is a button or the like that can be selected and input on the job seeker terminal 30.

[0110] When multiple candidate job openings are selected (for example, when multiple job openings are registered in the bookmark list), the revision suggestion unit 116 may extract additional recommended information from the registered information of multiple first reference job seekers associated with each of the multiple candidate job openings, including candidate job openings other than the candidate job opening displayed on the viewing screen. As described above, the first reference job seekers are other job seekers who have a history of actions related to each candidate job opening. Each candidate job opening is associated with multiple first reference job seekers.

[0111] The recommended information to be added is extracted from the registered information of the first reference job seekers in the same manner as the recommended information described above. That is, the correction suggestion unit 116 sets words or sentences extracted from the registered information of the first reference job seekers as the recommended information to be added, for example, based on the frequency of appearance of the words or sentences included in the registered information of the first reference job seekers.

[0112] FIG. 6 is a diagram showing an example of a job offer viewing screen JD displayed on the job seeker terminal 30. The job offer viewing screen JD displays a correction item CM and a job posting JP. The job posting JP contains the details of the candidate job offer selected by the job seeker to view. The correction item CM includes corrections proposed to the registration information of the job seeker viewing the candidate job offer. In the example of FIG. 6, the correction item CM displays the addition of skills (an example of recommended information) to the job seeker registration information. The correction item CM includes an update button B11 that accepts an instruction to edit the job seeker registration information. When the update button B11 is operated and input, an editing screen for the job seeker registration information, described below, is displayed on the job seeker terminal 30. In this way, by presenting required skills and the like while the user (job seeker) is viewing a job offer, a guide for the job seeker to enhance their job seeker registration information can be provided. Furthermore, the job seeker registration information can be corrected to make the job seeker registration information more likely to attract the attention of the employer of the job being viewed, more likely to receive scouting documents, and more likely to be hired.

[0113] The correction suggestion unit 116 may display an object that accepts an instruction to update the job seeker registration information along with the corrections on the candidate job offer viewing screen. Upon receiving an operation input for the object, the correction suggestion unit 116 overwrites and saves the job seeker registration information in the job seeker database with the job seeker registration information that reflects the corrections. In this case, the update of the job seeker registration information is completed without the editing screen described below being displayed on the job seeker terminal 30. This allows the job seeker, who is the user, to appropriately update their job seeker registration information to match the job offer of interest, without having to go through the trouble of updating their job seeker registration information.

[0114] The correction suggesting unit 116 may suggest corrections to the job seeker registration information by using the recommended job offers presented (extracted) by the presenting unit 115, instead of the candidate job offers selected by the job seeker who is the user. In other words, the correction suggesting unit 116 may suggest corrections based on at least one recommended job offer and the third reference information.

[0115] <Registration Information Editorial Department 117> The registered information editing unit 117 is configured to accept edits to the job seeker registration information from the job seeker terminal 30 and update the job seeker registration information. Specifically, upon receiving an instruction to edit the job seeker registration information from the job seeker terminal 30, the registered information editing unit 117 displays at least a part of the job seeker registration information in a state where the corrections have been reflected, and accepts edits to the job seeker registration information.

[0116] Specifically, the registration information editing unit 117 transitions the display on the job seeker terminal 30 from a viewing screen to an editing screen, and displays the updated registration information on the job seeker terminal 30 on the editing screen. The updated registration information is registration information obtained by adding recommended information (words or sentences) included in the corrections to the job seeker registration information before editing, deleting or replacing words or sentences included in the job seeker registration information before editing, etc. The registration information editing unit 117 may highlight the corrections in the updated registration information (changes from the job seeker registration information before editing) by coloring them, etc.

[0117] The registered information editing unit 117 accepts further modifications to the updated registered information from the job seeker terminal 30. Furthermore, upon receiving an input indicating completion of the update from the job seeker terminal 30, the registered information editing unit 117 overwrites and saves the updated registered information in the job seeker database.

[0118] The registered information editing unit 117 may present the job seeker registered information (updated registered information) in a state where the corrections have been reflected, along with additional recommended information extracted by the correction suggestion unit 116 from the registered information of the first reference job seeker related to multiple candidate job offers. This allows the job seeker (user) to be presented with additional recommended information based on the first reference job seeker related to other candidate job offers, in addition to suggestions for corrections based on the job offer currently being viewed. This makes it possible to provide the job seeker (user) with a wide range of materials for correcting his or her registered information.

[0119] 7 is a diagram showing an example of the registration information edit screen ED displayed on the job seeker terminal 30. The registration information edit screen ED includes a registration information display area RA, a save button B21, an add button B22, and an option display area OA.

[0120] The registration information display area RA is an area that displays updated registration information. In the example of FIG. 7, the contents of the "Skills" item, which is part of the updated registration information, are displayed. Specifically, the skills included in the updated registration information are displayed in each skill display field SF. When a skill display field SF in which no skills are displayed is selected, the registration information editing unit 117 accepts additional input of skills. Furthermore, when the add button B22 is operated and input, a skill display field SF is added. Furthermore, the registration information editing unit 117 also accepts modification and deletion of skills that have already been input in each skill display field SF.

[0121] The option display area OA is an area that displays additional recommended information derived from sources other than the job offer being viewed. In the example of Fig. 7, candidate skills SO extracted as additional recommended information (extracted from the registration information of multiple first reference job seekers) are displayed as selectable input objects. The registration information editing unit 117 accepts the selection of candidate skills SO from the job seeker terminal 30, and adds the selected candidate skills SO to the updated registration information (i.e., adds it to the registration information display area RA).

[0122] When the save button B21 is operated, the job seeker registration information is saved with the content displayed in the registration information display area RA. In other words, the item of the job seeker registration information displayed in the registration information display area RA ("Skills" in the example of FIG. 7) is updated to the content displayed in the registration information display area RA.

[0123] <Artificial Intelligence Department 120> The artificial intelligence unit 120 is configured to receive input from each functional unit and return the instructed output. The artificial intelligence used by the server device 10 in each functional unit may be a common one, or may be prepared individually for each functional unit.

[0124] The artificial intelligence unit 120 is an AI (Artificial Intelligence) equipped with a language model such as a Transformer including GPT (Generative Pretrained Transformer, including GPT-1, GPT-2, and GPT-3), BERT (Bidirectional Encoder Representations from Transformers), and BART (Bidirectional and Auto-regressive Transformer), and a Recurrent Neural Network (RNN), and may include a generative AI.

[0125] The language model is an example of a learning model based on a machine learning algorithm. Specific examples of machine learning algorithms include nearest neighbor methods, naive Bayes methods, decision trees, support vector machines, and deep learning using neural networks. The artificial intelligence unit 120 can apply the above algorithms as appropriate.

[0126] The artificial intelligence unit 120 may have a trained model constructed by a learning method such as supervised learning, unsupervised learning, or self-supervised learning. In supervised learning, machine learning is performed using training data (training data). The training data consists of pairs of input data for learning and output data (correct answer data). Furthermore, the language model may not only be trained for a specific task, but also be a general-purpose model that can be used for a wide range of tasks.

[0127] The artificial intelligence unit 120 may be a general-purpose natural language processing learning model such as a large language model (LLM) that has learned a huge amount of data as artificial intelligence. Such a general-purpose learning model includes a language model that can handle various tasks without fine tuning using one-shot learning, few-shot learning, etc. Furthermore, the general-purpose learning model may also be configured to be able to handle various tasks using zero-shot learning. The artificial intelligence used in each functional unit of the control unit 11 may be a separate learning model, or may be a common general-purpose learning model.

[0128] The learning models included in the artificial intelligence unit 120 (learning models used in each functional unit, such as the recommended job offer determination model and the correction determination model) can undergo additional learning as transfer learning or fine tuning. For example, each time new job seeker registration information and new job postings are registered, the artificial intelligence unit 120 may perform additional learning and fine tuning using these as new training data. This improves the accuracy of the information output from the learning model.

[0129] The learning model included in the artificial intelligence unit 120 may be a learning model (distilled model) obtained by knowledge distillation using an original learning model. In knowledge distillation, a trained model such as a large-scale language model is used as a teacher model, and the parameters of the student model (distilled model) are adjusted to minimize the output loss (Soft Target Loss) of the student model (distilled model) relative to the output (Soft Target) of the teacher model, thereby training the student model, which becomes the distilled model. Alternatively, the student model may be trained to minimize the output loss (Hard Target Loss) of the student model relative to the correct label (Hard Target) of the teacher data (combination of input data and output data of the learning model). Compared to the original learning model (teacher model), the distilled model has a smaller number of parameters and a smaller processing load while maintaining performance similar to the learning model. Therefore, using a distilled model can reduce the cost of the job search support system 1.

[0130] For example, the recommended job vacancy determination model or the revised determination model may be a distilled model trained using a combination of input data and output data in a large-scale language model as training data. Furthermore, when the job search support system 1 is introduced, a large-scale language model may be used as the recommended job vacancy determination model or the revised determination model, and once training data from the large-scale language model has been accumulated, the distilled model obtained by knowledge distillation using the training data may be used as the recommended job vacancy determination model or the revised determination model.

[0131] <Display> The display unit 211 of the recruiting party terminal 20 and the display unit 311 of the job seeker terminal 30 each display a screen indicated by the screen data transmitted from the server device 10.

[0132] <Operation acquisition part> The operation acquisition unit 212 of the recruiting party terminal 20 accepts operations by a user (recruiter) who uses the recruiting party terminal 20. The operation acceptance unit 312 of the job seeker terminal 30 accepts operations by a user (job seeker) who uses the job seeker terminal 30.

[0133] 3. Information Processing Method This section describes an information processing method of the server device 10. This information processing method is executed by a computer, with each unit of the server device 10 acting as each step.

[0134] Specifically, this information processing method includes the information processing of the first embodiment (processing for presenting recommended job offers) and the information processing of the second embodiment (processing for proposing corrections).

[0135] The information processing of the first embodiment includes a registration step, a first presentation step, a publication step, and a second presentation step. In the registration step, job seeker registration information is registered. In the first presentation step, at least one recommended job offer to be recommended to the job seeker from among job offers to which the job seeker can apply is presented based on the job seeker characteristic information and the first reference information. In the publication step, the registered job seeker registration information is made public to the employer. In the second presentation step, at least one recommended job offer to be recommended to the job seeker from among job offers to which the job seeker can apply is presented based on the job seeker characteristic information and the second reference information.

[0136] 8 is an activity diagram showing the flow of information processing (recommended job offer presentation processing) executed by job search support system 1. Below, the information processing will be explained along with each activity in this activity diagram.

[0137] The process of presenting recommended job offers begins with a job seeker registering his or her own registration information. The job seeker inputs the information required for registration into the job seeker terminal 30 (activity A101). The server device 10 accepts the job seeker registration information input from the job seeker terminal 30 and registers it in the job seeker database (activity A102). Next, the server device 10 extracts recommended job offers (initial recommended job offers) based on the job seeker registration information before disclosure and the first reference information (activity A103). Furthermore, the server device 10 outputs the extracted initial recommended job offers to the job seeker terminal 30 (activity A104). As a result, the initial recommended job offers are displayed (presented) on the job seeker terminal 30 (activity A105).

[0138] Furthermore, the server device 10 discloses the job seeker registration information that satisfies predetermined conditions to the employer (activity A106). Next, the server device 10 extracts recommended job offers (medium-term recommended job offers) based on the disclosed job seeker registration information and the second reference information (activity A107). Furthermore, the server device 10 outputs the extracted medium-term recommended job offers to the job seeker terminal 30 (activity A108). As a result, the medium-term recommended job offers are displayed (presented) on the job seeker terminal 30 (activity A109).

[0139] The information processing of the second embodiment includes a receiving step, a correction suggesting step, and a registered information editing step. In the receiving step, a selection of at least one candidate job offer by the job seeker is accepted from among job offers to which the job seeker can apply. In the correction suggesting step, corrections to the job seeker's registered information are suggested based on the candidate job offer accepted in the receiving step and the third reference information. In the registered information editing step, in response to an instruction, at least a portion of the job seeker's registered information in a state where the corrections have been reflected is displayed, and edits to the job seeker's registered information are accepted.

[0140] 9 is an activity diagram showing the flow of information processing (processing for proposing corrections) executed by job seeking support system 1. Below, the information processing will be explained along with each activity in this activity diagram.

[0141] The process of proposing corrections begins with the selection of candidate job offers by a job seeker whose job seeker registration information is registered. The job seeker selects at least one candidate job offer on the job seeker terminal 30 as a candidate for viewing, registration in a bookmark list, or the like (activity A201). The server device 10 accepts input of the candidate job offers from the job seeker terminal 30 and acquires information about the candidate job offers (activity A202). Next, the server device 10 extracts recommended information that is suggested to be added to the job seeker registration information based on the candidate job offers and the third reference information (activity A203). Furthermore, the server device 10 outputs updated registration information to the job seeker terminal 30, with the extracted recommended information added to the job seeker registration information (activity A204). As a result, the updated registration information is displayed (presented) on the job seeker terminal 30 (activity A205).

[0142] The job seeker, who is the user, edits the updated registration information displayed on the job seeker terminal 30 as necessary (activity A206). The server device 10 accepts the edits made to the updated registration information by the job seeker terminal 30 (activity A207). Furthermore, the server device 10 registers the edited updated registration information in the job seeker database as new job seeker registration information (activity A208).

[0143] 4. Effect The operation of this embodiment can be summarized as follows. That is, recommended job offers suitable for a job seeker can be presented according to the job seeker's registration information. Furthermore, because different reference information for determining recommended job offers is used depending on the time after registration of the registration information, suitable recommended job offers can be presented even to a job seeker who has just registered. As a result, the value of the hiring support service provided by the job search support system 1 can be presented to a job seeker who has just registered, thereby encouraging continued use of the hiring support service.

[0144] Although the embodiment of the present invention has been described above, the present invention is not limited to this and can be modified as appropriate within the scope of the technical idea of ​​the invention.

[0145] 5.Other In the above embodiment, the server device 10 performs various storage and control operations. However, multiple external devices may be used instead of the server device 10. That is, various information and programs may be distributed and stored in multiple external devices using blockchain technology or the like. In particular, the artificial intelligence unit 120 may be configured external to the server device 10. In this case, the artificial intelligence unit 120, which is an external configuration, is configured to receive input from each functional unit of the server device 10 and return instructed output to the server device 10.

[0146] Control unit 11 does not necessarily have to include correction suggestion unit 116 and registered information editing unit 117. In other words, job search support system 1 does not necessarily have to implement suggestions for corrections.

[0147] The aspect of this embodiment is not limited to the job search support system 1, but may also be an information processing method or a program. The job search support method includes steps executed by the job search support system 1. The program causes a computer to execute the steps of the job search support system 1.

[0148] It may be provided in the following manner.

[0149] (1) A job search support system comprising a processor configured to execute the following steps: in a registration step, registering registration information of a job seeker; and in a presentation step, presenting at least one recommended job offer to the job seeker from among job offers to which the job seeker is eligible to apply, based on job seeker characteristic information and reference information; wherein the job seeker characteristic information is at least one of the registration information and the job seeker's behavioral history regarding job offers; the reference information includes first reference information and second reference information that are used depending on the time after the registration of the registration information; the first reference information includes a first correlation between the job seeker characteristic information and the job offer; and the second reference information includes a second correlation between the job seeker characteristic information and the job offer, which is different from the first correlation.

[0150] (2) The job search support system according to (1) above, wherein the presenting step determines whether the time is a time to use the first reference information.

[0151] (3) In the job search support system described in (1) or (2) above, the processor is configured to further execute the following steps: in the publishing step, the registered registration information is published to employers; and in the presenting step, if the time is before the publication of the registration information, the recommended job offers are presented based on the job seeker characteristic information and the first reference information; and if the time is after the publication of the registration information, the recommended job offers are presented based on the job seeker characteristic information and the second reference information.

[0152] (4) In the job search support system described in any one of (1) to (3) above, the job seeker characteristic information is the registered information, and the number of items of the registered information referenced in the presentation step together with the second reference information is greater than the number of items of the registered information referenced in the presentation step together with the first reference information.

[0153] (5) In the job search support system described in any one of (1) to (4) above, the job seeker characteristic information is the registered information, and in the presenting step, when the first reference information is used according to the time period, the job offers that include job types or industries that are the same as or similar to the job types or industries included in the registered information and that satisfy annual income conditions for the annual income included in the registered information are presented as the recommended job offers, and the first reference information includes, as the first correlation, information indicating job types or industries that are the same as or similar to the job types or industries included in the registered information and the annual income conditions.

[0154] (6) In the job search support system described in any one of (1) to (5) above, the job seeker characteristic information is the registered information, and in the presenting step, when the first reference information is used according to the time period, the job offer for which the behavioral history of a reference job seeker satisfies a judgment criterion is presented as the recommended job offer, and the reference job seeker is another job seeker whose registered information has an attribute that is identical or similar to an attribute included in the registered information, or another job seeker whose behavioral history is close to that of the job seeker, and the first reference information includes, as the first correlation, information indicating an attribute that is identical or similar to an attribute included in the registered information, or information indicating the closeness of the behavioral history, and the judgment criterion for the behavioral history.

[0155] (7) In the job search support system described in (6) above, the behavioral history includes at least one of viewing the job posting, applying for the job posting, adding the job posting to a bookmark list, and replying to a scouting document sent based on the job posting, and the determination criterion is that the behavioral score calculated based on the behavioral histories of the reference job seekers exceeds a predetermined threshold.

[0156] (8) In the job search support system described in any one of (1) to (4) above, the first reference information is a recommended job offer determination model that has been trained to be able to input the job seeker characteristic information and output the recommended job offers, and in the presentation step, when the first reference information is used according to the time period, the job seeker characteristic information is input into the recommended job offer determination model and the recommended job offer is output by the recommended job offer determination model.

[0157] (9) In the job search support system described in (8) above, the job seeker characteristic information is the registered information, and the recommended job offer determination model is trained to be able to determine the recommended job offers based on the job type or industry and annual income included in the registered information.

[0158] (10) In the job search support system described in (8) above, the recommended job offer determination model is a learning model that has learned the relationship between the registration information of multiple job seekers to be studied and the behavioral history, and in the presentation step, when the first reference information is used according to the time period, the job seeker characteristic information is input into the recommended job offer determination model, and the recommended job offer is output by the recommended job offer determination model based on the job offer information included in the learned behavioral history.

[0159] (11) In the job search support system described in (10) above, the behavioral history includes at least one of viewing the job posting, applying for the job posting, adding the job posting to a bookmark list, replying to a scouting document sent based on the job posting, and passing the screening process for the job posting.

[0160] (12) In the job search support system described in any one of (1) to (11) above, the job seeker characteristic information is the registered information, and in the presentation step, when the first reference information is used depending on the time period, a job vacancy score is calculated for each job vacancy based on the registered information and the first reference information, and the recommended job vacancies are determined depending on the level of the job vacancy score.

[0161] (13) In the job search support system described in (12) above, the job offer score is calculated based on a match score calculated based on a comparison between the details contained in the registration information and the details contained in the job offer, and a behavior score calculated based on the behavioral history of reference job seekers who have other registration information with attributes that are the same as or similar to the attributes contained in the registration information, regarding the job offer.

[0162] (14) A job search support method comprising the steps executed by the job search support system described in any one of (1) to (13) above.

[0163] (15) A program that causes a computer to execute each step of the job search support system described in any one of (1) to (13) above. Of course, this is not the case.

[0164] Finally, while various embodiments of the present disclosure have been described, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0165] 1: Job search support system 2: Communication line 10: Server device 11: Control section 12: Storage section 13: Communications Department 14: Communication bus 20: Recruiter terminal 21: Control unit 22: Storage section 23: Communications Department 24: Input section 25: Output section 26: Communication bus 30: Job seeker terminal 31: Control unit 32: Storage section 33: Communications Department 34: Input section 35: Output section 36: Communication bus 111: Basic display control section 112: Registration Department 113: Public Section 114: Reception 115: Presentation part 116: Revision proposal department 117:Registration Information Editorial Department 120: Artificial Intelligence Department 211: Display section 212: Operation acquisition section 311: Display section 312: Operation reception section

Claims

1. A job search support system, a processor; The processor is configured to perform the following steps: In the registration step, job seeker registration information is registered, In the presenting step, at least one recommended job offer is presented to the job seeker from among job offers to which the job seeker is eligible to apply, based on the job seeker characteristic information and the reference information, wherein the job seeker characteristic information is at least one of the registration information and the job seeker's behavior history regarding job offers; the reference information includes first reference information and second reference information that are used depending on the time after registration of the registration information, the first reference information includes a first correlation between the job seeker characteristic information and the job offer; The second reference information includes a second correlation between the job seeker characteristic information and the job offer, the second correlation being different from the first correlation.

2. The job-seeking support system according to claim 1, In the presenting step, the job search support system determines whether the time is a time to use the first reference information.

3. The job-seeking support system according to claim 1, The processor is further configured to perform the steps of: In the disclosure step, the registered information is disclosed to the recruiter. In the presentation step, if the time is before the publication of the registered information, the recommended job offers are presented based on the job seeker characteristic information and the first reference information, and if the time is after the publication of the registered information, the recommended job offers are presented based on the job seeker characteristic information and the second reference information.

4. The job-seeking support system according to claim 1, the job seeker characteristic information is the registration information, a number of items of the registered information referred to in the presentation step together with the second reference information is greater than a number of items of the registered information referred to in the presentation step together with the first reference information.

5. The job-seeking support system according to claim 1, the job seeker characteristic information is the registration information, In the presenting step, when the first reference information is used according to the time period, the job offer that includes a job type or industry that is the same as or similar to the job type or industry included in the registration information and that satisfies an annual income condition for the annual income included in the registration information is presented as the recommended job offer, A job search support system, wherein the first reference information includes, as the first correlation, information indicating job types or industries that are the same as or similar to the job types or industries included in the registration information, and the annual income conditions.

6. The job-seeking support system according to claim 1, the job seeker characteristic information is the registration information, In the presenting step, when the first reference information is used according to the time period, the job offer for which the behavioral history of the reference job seeker satisfies a determination criterion is presented as the recommended job offer; The reference job seeker is another job seeker whose registered information has attributes that are the same as or similar to the attributes included in the registered information, or another job seeker whose behavioral history is similar to that of the job seeker, A job search support system, wherein the first reference information includes, as the first correlation, information indicating attributes that are identical or similar to attributes included in the registered information, or information indicating the similarity of the behavioral history, and the judgment criteria for the behavioral history.

7. 7. The job-seeking support system according to claim 6, the action history includes at least one of viewing the job offer, applying for the job offer, registering the job offer in a bookmark list, and replying to a scouting document sent based on the job offer; The job search support system, wherein the determination criterion is that a behavior score calculated based on the behavioral histories of the plurality of reference job seekers exceeds a predetermined threshold.

8. The job-seeking support system according to claim 1, the first reference information is a recommended job offer determination model that has been trained to input the job seeker characteristic information and output the recommended job offers, In the presentation step, when the first reference information is used depending on the time period, the job seeker characteristic information is input into the recommended job offer determination model, and the recommended job offer is output from the recommended job offer determination model.

9. 9. The job-seeking support system according to claim 8, the job seeker characteristic information is the registration information, The job search support system, wherein the recommended job offer determination model is trained so as to be able to determine the recommended job offers based on the job type or industry and annual salary included in the registration information.

10. 9. The job-seeking support system according to claim 8, the recommended job offer determination model is a learning model that learns the relationship between the registered information of a plurality of job seekers to be trained and the behavioral history, In the presentation step, when the first reference information is used according to the time period, the job seeker characteristic information is input into the recommended job offer determination model, and the recommended job offer determination model is caused to output the recommended job offer based on job offer information included in the learned behavioral history.

11. The job-seeking support system according to claim 10, A job search support system, wherein the behavioral history includes at least one of viewing the job posting, applying for the job posting, adding the job posting to a bookmark list, replying to a scouting document sent based on the job posting, and passing the screening for the job posting.

12. The job-seeking support system according to claim 1, the job seeker characteristic information is the registration information, In the presentation step, when the first reference information is used depending on the time period, a job search support system calculates a job offer score for each job offer based on the registered information and the first reference information, and determines the recommended job offer depending on the level of the job offer score.

13. The job-seeking support system according to claim 12, A job search support system in which the job score is calculated based on a match score calculated based on a comparison between the details contained in the registration information and the details contained in the job offer, and a behavioral score calculated based on the behavioral history of reference job seekers who have other registered information that has attributes that are the same as or similar to the attributes contained in the registration information, regarding the job offer.

14. A job search support method, comprising: A job search support method comprising the steps executed by the job search support system according to any one of claims 1 to 13.

15. A program, A program causing a computer to execute each step of the job search support system according to any one of claims 1 to 13.

Images