MATCHING SYSTEM, PROGRAM, AND MATCHING METHOD

Abstract

The matching system performs matching processing between human resources and projects. The matching system acquires text data expressing the contents of the project, identifies one or more project skills related to the project based on the text data, acquires one or more attributes for each of a plurality of human resources, calculates a score for each of the human resources based on each of the project skills and each of the attributes, ranks the plurality of human resources based on the score of each human resource, and outputs the score of each human resource as a numerical value.

Description

[Technical field]

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

[0002] In the activities of companies and other organizations, when managing a project that is carried out by a team of many people, it is important to select the right people for the project from among the candidates. In such a selection process, it is useful to evaluate how well the project and the people match.

[0003] Various techniques for such purposes are known. For example, Patent Document 1 describes a matching information processing system that classifies tasks and human resources, calculates scores, and performs optimal matching. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2023-184514 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the conventional technology, there was a problem that it was difficult to rank the matching results. For example, Patent Document 1 describes that candidates are proposed with reasons according to the desired human resource image, but the reasons are only explained in natural language, and it is difficult for users who view this to rank the candidates.

[0006] The present invention has been made to solve such problems, and has an object to propose a matching system, program, and matching method that make it easier to evaluate the ranking of matching results. [Means for solving the problem]

[0007] An example of a matching system according to the present invention includes: A matching system that performs matching processing between human resources and projects, Obtaining text data representing the contents of the project; identifying one or more project skills associated with the project based on the text data; Obtain one or more attributes for each of multiple personnel, Calculating a score for each talent based on each of the project skills and each of the attributes; The plurality of human resources are output in order based on the score of each human resource, and the score of each human resource is output as a numerical value.

[0008] In one example, The matching system further calculates a score for each attribute of each human resource, A score for each talent is calculated based on the scores for each of the talent's attributes.

[0009] In one example, the matching system comprises: Further determining the strength of each project skill based on the text data; Each talent's score is then calculated based on the strength of each project skill.

[0010] In one example, the matching system comprises: receiving an input from a first human resource for assigning a new attribute to the first human resource; receiving an input from a second human resource for assigning a new attribute to the first human resource; The score of the attribute imparted to the first human resource by the second human resource is made greater than the score of the attribute imparted to the first human resource by the first human resource.

[0011] In one example, the matching system comprises: accepting input of a positive evaluation of any of the attributes of the first talent from a second talent; A score for each attribute is further calculated based on the number of positive ratings for that attribute.

[0012] In one example, the matching system comprises: displaying each project skill associated with said project; Accepts input to add or remove one or more project skills.

[0013] In one example, the matching system comprises: Shows the strength of each project skill, Accepts input to change the strength of any project skill.

[0014] In one example, the matching system comprises: For each attribute, associate one or more normalization skills; A score for each attribute is calculated based on each normalized skill associated with that attribute.

[0015] In one example, the matching system comprises: For each attribute, associate one or more normalization skills; Obtain hierarchical information that represents the hierarchical relationship between each normalized skill; A total number of the normalized skills associated with one or more of the human resources is calculated based on the hierarchical information.

[0016] In one example, the matching system comprises: inputting information including the text data into a large-scale language model; The one or more project skills are obtained from the large-scale language model.

[0017] In one example, the matching system further comprises a client terminal, The client terminal outputs the score of each talent as a numerical value.

[0018] An example of the program according to the present invention causes a computer to function as the above-mentioned matching system.

[0019] An example of a matching method according to the present invention includes: A matching method for performing a matching process between human resources and projects, comprising the steps of: A step of acquiring text data representing the content of the project by a computer; a computer identifying one or more project skills associated with the project based on the text data; A step in which a computer acquires one or more attributes for each of a plurality of human resources; A computer calculates a score for each talent based on each of the project skills and each of the attributes; a computer outputting the plurality of talents in order based on the score of each talent, and outputting the score of each talent as a numerical value; Equipped with. Effect of the Invention

[0020] According to the matching system, program, and matching method of the present invention, it becomes easier to evaluate the ranking of matching results.

[0021] Effects other than those mentioned above will be described by the embodiments and modifications described in this specification and the drawings. [Brief description of the drawings]

[0022] [Figure 1] 1 shows an overview of the operation of the matching system according to the first embodiment. [Diagram 2] 1 shows an overview of the configuration of a matching system according to the first embodiment. [Diagram 3] 3 shows an example of the hardware configuration of the matching server 10 in FIG. 2. [Figure 4] 4 is a configuration example of the human resources data D1 in FIG. 3. [Diagram 5] 3 is an example of a matching processing screen 100 output by the matching server 10 of FIG. 2. [Figure 6] 3 is an example of an attribute display screen 200 output by the matching server 10 of FIG. 2. [Figure 7] 3 shows an example of a normalized skill statistics screen 300 output by the matching server 10 of FIG. 2 and a partially enlarged view 301 thereof. [Figure 8] 3 is an example of a flow of processing executed by the matching server 10 of FIG. 2 regarding processing for assigning attributes to personnel. [Figure 9] 3 is an example of a flow of a matching process executed by the matching server 10 of FIG. 2. [Figure 10] Details of step S12 in FIG. [Figure 11] Details of step S14 in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. [Embodiment 1] 1 is a diagram for explaining an overview of the operation of the matching system according to embodiment 1. The matching system performs a matching process between a project and human resources by executing a matching method described in this specification.

[0024] "Project" refers to, but is not limited to, an initiative, plan, business, job, etc. to be carried out by one or more individuals. A project may be created, executed and / or managed by an organization, such as, but not limited to, a company, a public organization, a country, a local government, etc.

[0025] "Human resources" refers to candidates for individuals (natural persons) involved in a project, and for example, suitable human resources are selected for a certain project from among a large number of human resources. Human resources may be some or all of the individuals belonging to a specific group (a company or a division thereof), but it is not necessary to be limited to individuals belonging to a specific group, and individuals not related to a specific group may also be human resources.

[0026] The matching system identifies skills related to a project (hereinafter sometimes referred to as "project skills") based on sentences (text data) expressing the contents of the project. Project skills refer to, for example, but are not limited to, techniques, abilities, skills, and the like that are useful for or required to carry out a project. Project skills are expressed, for example, as text data in the form of natural language.

[0027] The matching system also acquires attributes of each talent. In the example of FIG. 1, three talents are shown: talent A, talent B, and talent C. "Attributes" refers to, for example, but are not limited to, the talent's abilities, qualities, characteristics, etc. It may also include evaluation information of the talent held by the organization. The attributes are expressed as text data, for example. Attributes expressed as text data may be called "tags."

[0028] The matching system calculates a score for each talent based on the similarity between the project skills and the talent's attributes. A specific method for calculating the similarity and score will be described later. Then, the talents are ranked and output based on the calculated scores. At this time, the score for each talent is output as a numerical value, and a numerical ranking is output, for example.

[0029] The output result will be, for example, as shown in the result display field 140 of the matching processing screen 100 in Fig. 5, which will be described later. The project manager who receives such an output can easily understand the ranking evaluation of the matching results. In particular, since not only the ranking but also the score as a numerical value is displayed, the degree of matching of each human resource can be easily recognized.

[0030] 2 shows an overview of the configuration of the matching system according to embodiment 1. The matching system is configured, for example, by a matching server 10. The matching server 10 is configured to be able to communicate with each of a large-scale language model server 20, a vector conversion model server 30, an administrator terminal 41, and a human resources terminal 42 (two terminals in FIG. 2) via a communication network (including, for example, the Internet).

[0031] 2, the matching system can be configured only by the matching server 10, but may include other components. For example, the matching system may include any or all of the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 described above.

[0032] The manager terminal 41 is, for example, a client terminal operated by a manager who manages the matching server 10. The talent terminal 42 is, for example, a client terminal operated by each talent. Client terminals operated by users other than these may also be provided.

[0033] In the example of Figure 2, the networks to which the matching server 10 is connected are separate: a network for communicating with the large-scale language model server 20 and the vector conversion model server 30, and a network for communicating with the administrator terminal 41 and the human resources terminal 42. However, these may be the same network, for example the Internet.

[0034] 3 shows an example of the hardware configuration of the matching server 10. The matching server 10 has a hardware configuration as a known computer, and includes, for example, a calculation means 11, a storage means 12, and an input / output means 13. The calculation means 11 includes, for example, a processor. The processor can be manufactured using an integrated circuit, an ASIC, an FPGA, or the like. The storage means 12 includes, for example, a storage medium such as a semiconductor memory device and a magnetic disk device. A part or all of the storage medium may be a non-transitory storage medium.

[0035] The storage means 12 stores the talent data D1. The storage means 12 may also store a program (not shown). A processor of a computer executes this program, causing the computer to function as the matching server 10 described in this embodiment. That is, the program may cause the computer to function as the matching server 10 or matching system according to this embodiment and execute the matching method according to this embodiment. The matching method may include each step described in relation to this embodiment.

[0036] The input / output means 13 includes, for example, an input device such as a keyboard and a mouse, an output device such as a display and a printer, and a communication device such as a network interface. The communication device can function as both an input device and an output device. The communication device can perform wired communication and / or wireless communication.

[0037] Although the illustration of the hardware configuration of the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 is omitted, each has a hardware configuration as a known computer, for example, the same configuration as the configuration described above for the matching server 10. Each storage device of the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 may store a program not shown. By the processor of the computer executing these programs, each computer may execute the functions of the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 described in this embodiment. In other words, these programs may cause the computer to function as the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 according to this embodiment. Note that the large-scale language model server 20, the vector conversion model server 30, the administrator terminal 41, and the human resources terminal 42 do not need to store the human resources data D1.

[0038] 4 shows an example of the configuration of talent data D1. Talent data D1 associates, for multiple talents, a talent ID that identifies each talent with information about the talent. The talent information includes the talent's name and information about attributes. The attribute information includes the attribute name (expressed as a tag), type, number of evaluations, and normalized skill.

[0039] The name of the attribute is, for example, an expression of the attribute as text data, but may be in a format other than text data. The type indicates the giver of the attribute, for example, whether the attribute is an attribute given by the human resource himself / herself (personal tag) or an attribute given by another person (peer tag). Here, "other people" includes, for example, other human resources, individuals not included in the human resources, an organization to which the human resource belongs, an organization not directly related to the human resource, etc., but is not limited to these. The number of evaluations indicates the number or degree to which other human resources etc. have given positive evaluations (for example, "Like!" operations) for the attribute. The normalization skill is an expression actually used when matching the attribute with the project skill. This normalization skill is, for example, text data, but may be in a format other than text data.

[0040] Formal skills are skills that belong to a set of skills defined by, for example, a public institution. One example is the European Skills, Competences, Qualifications and Occupations Organization (ESCO).<https: / / esco.ec.europa.eu / en / classification / skill_main> ) can be used. According to the ESCO skill classification, the names of many skills and the hierarchical relationships between each skill are defined, which is useful for analyzing the skills possessed by human resources.

[0041] However, the normalization skill is not limited to this, and any expression, content, and format of skill may be used as long as it is in a format that can be commonly associated with multiple attributes.

[0042] 5 shows an example of a matching processing screen 100 output by the matching server 10. This matching processing screen 100 is displayed on, for example, the administrator terminal 41. The matching processing screen 100 includes a text input field 110, a skill field 120, a submit button 130, and a result display field 140. The text input field 110 is an input field for accepting input of a sentence expressing the contents of the project. In this example, the sentence "I want to make a video that will become popular on video sites" is input.

[0043] The skill column 120 includes a skill display column 121 , a skill input column 122 , a strength change column 123 , an add button 124 , and a delete button 125 .

[0044] The skill display field 121 includes the name of the project skill and a parameter indicating the importance of the project skill (in this embodiment, the parameter is "strength", but the name is not limited to this). In this way, the matching server 10 displays each project skill related to the project and the strength of each project skill in the skill display field 121.

[0045] In the example of FIG. 5, for example, the strength of a project skill named "video creation" is 100[%], and the strength of a project skill named "social media marketing" is 50[%]. The project skill selected at that time (in this example, "video creation") is displayed at the top of the skill display column 121. The selected project skill can be changed by performing an operation on any project skill in the skill display column 121 (e.g., by clicking with the mouse). That is, for example, when the part displayed as "content creation" is operated, the matching server 10 accepts the selection for "content creation" and changes the display at the top of the skill display column 121 accordingly.

[0046] When the matching server 10 accepts the operation of the delete button 125, it deletes the project skill currently selected from among the project skills required for the project, and accordingly erases the display of the project skill from the skill display column 121.

[0047] The skill input field 122 accepts input of a new project skill to be added. When the matching server 10 accepts the operation of the add button 124, the matching server 10 stores the project skill currently displayed in the skill input field 122 as a project skill required for the project, and accordingly adds the display of the project skill to the skill display field 121.

[0048] In this manner, the matching server 10 can accept input to add or remove one or more project skills.

[0049] The strength change field 123 includes a strength change knob 123a and is used to change the strength of any of the project skills. The matching server 10 changes the strength of the project skill currently selected in response to an operation (e.g., a drag operation) on the strength change knob 123a. In this manner, the matching server 10 can accept an input for changing the strength of any of the project skills.

[0050] It should be noted that the GUI for changing the strength of a skill is not limited to this, and a numerical value may be input directly, or a pull-down list or the like may be used to select from a range of predetermined values.

[0051] The submit button 130 is used to instruct execution of the matching process after the project skills to be used for matching are determined. When the submit button 130 is operated, the matching server 10 executes the matching process between the project and the human resources based on the project skills and strengths at that time.

[0052] The result display field 140 displays the matching results between projects and human resources. In the example of FIG. 5, the score (total_score) and the breakdown (related_tags) of each of four human resources (users) are displayed. For example, for the human resource "Mr. A," the highest score of 0.34 is displayed for the attribute "tends to use moving emojis," the next highest score of 0.31 is displayed for the attribute "original thinking," and so on in descending order of the scores. Note that for convenience of illustration, some of the breakdown is omitted in FIG. 5, so the human resource score does not match the sum of the breakdown.

[0053] FIG. 6 shows an example of an attribute display screen 200 output by the matching server 10. This attribute display screen 200 is displayed, for example, on the administrator terminal 41 and the human resource terminal 42. In this example, a plurality of attributes for one human resource are displayed as "tags." In this example, each attribute is displayed classified into one of a plurality of categories ("special skills / good points," "hobbies / interests," and "about yourself"), but such a categorization display format is not essential, and categorization other than that shown in the figure may be performed, or display may be performed without categorization. Note that in FIG. 6, for convenience of illustration, the display of some attributes is omitted.

[0054] Attributes are displayed in the form of tags 201. As described above, attributes can be assigned by the talent themselves, or by others. In the example of FIG. 6, attributes that the talent assigned to themselves ("self-assigned tags", e.g., tag 201a "white wine lover") are shown in dark grey, while attributes assigned by others ("peer tags", e.g., tag 201b "AI") are shown in light grey. The color of each tag can be designed as appropriate. Here, as an example of interpretation, attributes related to peer tags can be said to represent the talent more objectively than attributes related to self-assigned tags, and therefore are more reliable.

[0055] The attribute display screen 200 includes a plurality of tags 201 and a rating count display 202 for each tag 201. The rating count display 202 indicates the number of positive evaluations for that attribute of the human resource. If the number of positive evaluations is 0, the number of positive evaluations is not displayed on the attribute display screen 200. As an example of interpretation, it can be said that an attribute associated with a tag having a larger number of positive evaluations represents the human resource more appropriately than an attribute associated with a tag having a smaller number of positive evaluations, and therefore has higher reliability.

[0056] The number of positive evaluations indicates, for example, the number of times that other people have performed a predetermined positive evaluation operation, or the number of such other people. A positive evaluation operation is, for example, an operation that expresses approval or praise for the tag, and a more specific example can be an operation similar to a "Like!" operation in a social networking service (SNS). Such an operation can be input, for example, from the talent terminal 42 via the attribute display screen 200. The talent terminal 42 transmits this operation to the matching server 10. Upon receiving this operation, the matching server 10 increases the number of evaluations of the talent for the attribute in the talent data D1. In this way, the matching server 10 can accept the input of a positive evaluation of any of the attributes of a certain talent (assumed to be a first talent) from another entity (assumed to be a second talent, for example).

[0057] 7 shows an example of a normalized skill statistics screen 300 output by the matching server 10 and a partial enlarged view 301 thereof. This normalized skill statistics screen is displayed, for example, on the manager terminal 41 and the human resource terminal 42. The normalized skill statistics screen 300 displays statistical information on the normalized skills of each human resource for a group consisting of one or more human resources. In the example of FIG. 7, the total number of each normalized skill is displayed in a format similar to a pie chart, and the total number is represented by the angle range of each area.

[0058] In the example of Fig. 7, the normalized skill statistics screen 300 also displays the hierarchical relationship of normalized skills. For example, normalized skills are classified into three hierarchical levels, an upper level, a middle level, and a lower level, and are distinguished by adding "_1", "_2", and "_3" to the end of the skill name. In the example of the enlarged partial view 301 of Fig. 7, the upper level normalized skill "sociability and communication ability" is associated with the middle level normalized skill "communication ability", which is further associated with the lower level "ability to speak to an audience".

[0059] A specific method for generating such a normalized skill statistics screen 300 can be appropriately designed by a person skilled in the art, but an example will be described below. The matching server 10 can acquire hierarchical information indicating the hierarchical relationship between each normalized skill in advance. The hierarchical information can be generated by the matching server 10 or another computer based on, for example, the ESCO skill classification. The hierarchical information may also be generated manually based on, for example, the ESCO skill classification.

[0060] Next, the matching server 10 accepts input of information that identifies the talent for which statistical information is to be displayed. This information may include, for example, one or more talent IDs, or may include information that identifies the group to which each talent belongs. Next, the matching server 10 refers to the talent data D1 and calculates the total number of each normalized skill (corresponding to a lower hierarchical level) related to one or more talents to be displayed. Furthermore, based on the above hierarchical information, the matching server 10 similarly calculates the total number for higher-level normalized skills. Then, the matching server 10 generates a normalized skill statistics screen 300 based on the total number of normalized skills for each hierarchical level.

[0061] The operation of the matching system having the above configuration will be described below.

[0062] 8 shows an example of a flow of a process executed by the matching server 10 for assigning an attribute to a human resource. The process in FIG. 8 is started in response to a predetermined instruction (for example, an operation by a human resource to assign an attribute to itself or another human resource).

[0063] In the process of Fig. 8, first, the matching server 10 acquires attributes to be assigned to a specific human resource (step S1). The attributes are acquired, for example, from the manager terminal 41, the human resource terminal 42, or another computer. For example, a human resource can operate his / her own human resource terminal 42 to display an attribute display screen 200 relating to himself / herself or another human resource, and input new attributes to be assigned to the human resource via this screen.

[0064] More precisely, the process in which the matching server 10 assigns a new attribute to a certain human resource (first human resource) is as follows. That is, the matching server 10 can receive an input from the first human resource to assign a new attribute to the first human resource (principal). In this way, when a new attribute is assigned to the first human resource (principal) by the first human resource, the type of the attribute becomes a principal tag. In addition, the matching server 10 can receive an input from an entity different from the first human resource (e.g., a second human resource) to assign a new attribute to the first human resource. In this way, when a new attribute is assigned to the first human resource by the second human resource, the type of the attribute becomes a peer tag.

[0065] The human resources terminal 42 accepts the input of the attributes and transmits them to the matching server 10, and the matching server 10 receives them and acquires the attributes.

[0066] Next, the matching server 10 associates one or more normalized skills with each of the acquired attributes (steps S2 to S7 below). A specific example of this process will be described below, but the specific implementation method is not limited to this.

[0067] The matching server 10 inputs the name of the attribute into the large-scale language model (step S2). This input is performed, for example, by transmitting the name of the attribute to the large-scale language model server 20.

[0068] The large-scale language model server 20 stores a large-scale language model, and can input a language into the large-scale language model to obtain the output. The large-scale language model is a trained model that has been trained to output an appropriate answer in natural language in response to an input in natural language (more typically, natural sentences) using, for example, a large amount of text data, and various known models can be used. For example, BERT, GPT-3, GPT-4, PaLM, etc. are known, but are not limited to these. The large-scale language model server 20 transmits the output from the large-scale language model to the matching server 10.

[0069] Here, when inputting attributes into the large-scale language model, the matching server 10 may additionally input information other than the name of the attribute. The added information may include an explanatory statement that the attribute represents the ability of a human resource, a command statement to output one or more skills related to the attribute that are useful for the project, and the like. The additional information input here may be fixed and input in advance to the matching server 10.

[0070] Next, the matching server 10 acquires the skills output from the large-scale language model (step S3). This is performed, for example, by receiving information transmitted from the large-scale language model server 20. Hereinafter, the skills acquired in step S3 will be referred to as "provisional skills" in order to distinguish them from project skills and normalized skills.

[0071] Next, the matching server 10 transmits the name of the provisional skill to the vector conversion model server 30 (step S4). The vector conversion model server 30 stores a trained model and can convert a linguistic expression such as a name into a vector (for example, 768 dimensions). Such a vector conversion model server 30 can be configured using, for example, paraphrase-multilingual-mpnet-base-v2 (https: / / huggingface.co / sentence-transformers / paraphrase-multilingual-mpnet-base-v2), but is not limited thereto.

[0072] The vector conversion model server 30 transmits the vector (the converted vector) representing the provisional skill to the matching server 10, which receives and acquires it (step S5). In this manner, the provisional skill is vectorized. That is, a multi-dimensional vector is acquired based on the name of the provisional skill.

[0073] Next, the matching server 10 selects from among the normalized skills a vector similar to the received vector (vector representing the provisional skill) (step S6). It is preferable that the normalized skill is converted in advance into a vector of the same dimension as the vector representing the attribute by the same process as in steps S4 and S5, but if this is not the case, a similar process may be performed in step S6. A specific method for calculating the similarity between the vector representing the provisional skill and the vector representing the normalized skill can be appropriately designed by a person skilled in the art based on known techniques, but it can be calculated based on cosine similarity, for example. The matching server 10 selects the normalized skill that gives the highest similarity.

[0074] Next, the matching server 10 stores the selected normalized skill in the talent data D1 in association with the attribute (step S7). If multiple provisional skills are acquired for one attribute, a normalized skill can be selected for each provisional skill, and in that case, multiple normalized skills are associated with one attribute. In this way, the process of FIG. 8 ends.

[0075] This process can reduce output fluctuations caused by large-scale language models. Large-scale language models, due to their characteristics, may generate similar but slightly different language expressions as outputs for the same input. Even in such cases, by selecting and using similar normalization skills, it is possible to absorb some fluctuations and treat them as the same normalization skill, thereby improving matching accuracy.

[0076] 9 shows an example of the flow of the matching process executed by the matching server 10. The process in FIG. 9 is started in response to a predetermined instruction (for example, a process start operation input from the administrator terminal 41).

[0077] In the process of FIG. 9, the matching server 10 first acquires natural sentences (text data) that express the contents of the project (step S11). An example of the text data is a single sentence such as "I want to create a video that will become popular on video sites," but the text data is not limited to this and may be a sentence consisting of multiple sentences. Furthermore, while natural sentences are used in this embodiment, in modified examples, expressions that do not form natural sentences (such as a list of words) may also be used. It is preferable that the text data clearly express the contents of the project.

[0078] The method of acquiring text data can be designed arbitrarily, but an example is shown below. The matching server 10 displays the text input field 110 of the matching processing screen 100 on the administrator terminal 41. Furthermore, the matching server 10 or the administrator terminal 41 may perform a display that prompts the input of text data that represents the contents of the project. The administrator terminal 41 may accept the input via the text input field 110 of the matching processing screen 100 and transmit it to the matching server 10. The input on the administrator terminal 41 may be input via a keyboard, or may be designation of information indicating the location of text data that has been input in advance and stored in the administrator terminal 41. Also, text data may be stored in the matching server 10 in advance (such as reuse of previous text data), and information indicating the location of the stored text data may be transmitted from the administrator terminal 41 to the matching server 10.

[0079] Next, the matching server 10 identifies information about project skills based on the text data (step S12). The information about project skills includes, for example, for one or more project skills, a name (character string) representing the project skill and the strength of the project skill.

[0080] 10 shows the details of step S12. In step S12, the matching server 10 first inputs information including text data representing the contents of the project to the large-scale language model (step S121). This input is performed, for example, by transmitting the text data to the large-scale language model server 20.

[0081] The large-scale language model server 20 stores the large-scale language model, and can input a language into the large-scale language model to obtain the output. The large-scale language model server 20 transmits the output from the large-scale language model to the matching server 10.

[0082] Here, when inputting text data into the large-scale language model, the matching server 10 may additionally input information other than the text data. The added information may include an explanatory statement that the text data describes the contents of a project, a command statement to identify and output one or more project skills required for the project, a command statement to output a "strength" indicating the degree to which each project skill is required for the project as a numerical value associated with each project skill, and the like. The additional information input here may be fixed and input in advance to the matching server 10.

[0083] Next, the matching server 10 obtains one or more project skills and a strength associated with each project skill from the large-scale language model (step S122), for example by receiving information transmitted from the large-scale language model server 20. In this way, the matching server 10 can determine the project skills and the strength of each project skill based on the text data.

[0084] Next, the matching server 10 outputs the received project skill and strength, and accepts a change operation of the project skill (step S123). The change operation of the project skill can be accepted via the skill field 120 of the matching processing screen 100 of FIG. 5, and includes, for example, the following operations: -Adding project skills -Removal of project skills - Changing the strength of a project skill (e.g. increasing or decreasing) As a modified example, step S123 may be omitted.

[0085] Next, the matching server 10 accepts input of an operation to confirm the project skill (step S124). For example, this operation is accepted as an operation on the submit button 130 on the matching processing screen 100 in Fig. 5. In this manner, the matching server 10 identifies information related to the project skill based on the text data, and the process of step S12 in Fig. 9 ends here.

[0086] 9, after step S12, the matching server 10 acquires information related to attributes (step S13). For example, the matching server 10 refers to the talent data D1 and acquires one or more attributes for each of a plurality of talents.

[0087] Next, the matching server 10 calculates a score for each of the personnel based on each of the project skills and each of the attributes (step S14).

[0088] 11 shows the details of step S14. In step S14, the matching server 10 first vectorizes the project skill (step S141). That is, based on the name of the project skill (for example, "video creation"), a multi-dimensional (for example, 768-dimensional) vector is obtained. This process can be executed by, for example, the same process as steps S4 and S5 in FIG. 8.

[0089] Next, the matching server 10 vectorizes the normalized skills for each of the attributes of the human resource (step S142). That is, based on the name of the normalized skill, a multi-dimensional (e.g., 768-dimensional) vector is obtained. This process can be executed, for example, by the same process as steps S4 and S5 in FIG. 8. Note that, if the result of vectorization previously executed for the normalized skill is stored in the matching server 10 (e.g., the human resource data D1) or another computer, step S142 can be executed by simply acquiring the result.

[0090] Next, the matching server 10 calculates a score based on the similarity between each project skill and the attributes of the human resource (step S143). Here, the score can be calculated based on the normalized skill of the attribute, and when multiple normalized skills are associated with one attribute, the most appropriate one can be selected and used. The most appropriate normalized skill is, for example, the one with the highest similarity (e.g., cosine similarity) between the project skill and the attribute.

[0091] A similarity (cosine similarity as an example below) is calculated between the normalized skill (one of the normalized skills if there are multiple skills) associated with the attribute and the project skill, and a score for the combination of the attribute and the project skill is calculated based on the cosine similarity. A method for calculating the score can be appropriately designed by those skilled in the art, but as a non-limiting example, the score can be calculated using the following formula. Score(s,t) = CosineSimilarity * Strength * (1 + w_reaction * reaction_count ) * (1 + w_peer * is_peer) … (Formula 1) Here, Score(s,t) is the score for the combination of project skill s and its attribute t, CosineSimilarity is the cosine similarity between the project skill and the normalized skill, Strength is the strength of the project skill, reaction_count is the number of ratings associated with the attribute, w_reaction is the weight of the number of ratings, is_peer is a value indicating whether it is a peer tag (for example, 0 for the person's tag and 1 for a peer tag), and w_peer is the weight of whether it is a peer tag or not.

[0092] According to the above formula 1, the matching server 10 can make the score of a peer tag higher than the score of a personal tag, depending on the value of the variable is_peer, assuming that all other variables are the same. This allows more objective attributes to obtain higher scores, improving the accuracy of matching. Note that the specific formula for making the score of a peer tag higher than the score of a personal tag is not limited to the above formula 1, and can be appropriately designed by those skilled in the art. Note that, as a modified example, a calculation formula that does not use the variable is_peer may be used. In that case, whether it is a peer tag or a personal tag will not be taken into consideration.

[0093] Furthermore, according to formula 1, the matching server 10 can calculate the score of each attribute based on the number of evaluations for that attribute, using the value of the variable reaction_count. Therefore, for example, an attribute that has received many positive evaluations from others will have a higher score. This results in a higher score for more objective attributes, improving the accuracy of matching. As a modified example, a calculation formula that does not use the variable reaction_count may be used. In that case, positive evaluations will not be taken into account.

[0094] Furthermore, according to formula 1, the matching server 10 can calculate the score of each attribute (and thus the score of each human resource) based on the strength of each project skill, using the value of the variable Strength. This allows a score to be calculated that places emphasis on project skills that are more relevant to the target project, improving the accuracy of matching. As a modified example, a calculation formula that does not use the variable Strength may be used. In that case, all project skills are considered with a uniform weight.

[0095] According to the formula 1, the variable CosineSimilarity represents the cosine similarity between the project skill and the normalized skill, so that the matching server 10 can calculate the score of each attribute based on the normalized skill associated with the attribute, using the value of this variable. As a modified example, a similarity other than the cosine similarity may be used.

[0096] Next, the matching server 10 calculates a score for each attribute of each human resource (step S144). The score of an attribute is calculated as, for example, the sum of the scores of the attribute and each project skill. That is, Score(t) = Σ_skill { Score(s,t)} Here, Score(t) is the score for attribute t, Score(s,t) is the score for the combination of project skill s and attribute t as described above, and Σ_skill represents the operation of taking the sum over all project skills.

[0097] Next, the matching server 10 calculates the score of the talent (step S145). The talent score can be calculated based on the scores of each attribute of the talent, and is calculated as the sum of the scores of each attribute, for example. That is, Score_Person = Σ_tag { Score(t)} Here, Score_Person is the score of the person, Score(t) is the score of attribute t as described above, and Σ_tag is the operation of taking the sum of all attributes. In this way, the scores for all people are calculated, and step S14 in FIG. 9 is completed.

[0098] Returning to Fig. 9, after step S14, the matching server 10 outputs the multiple talents in order based on the score of each talent (step S15). At this time, the score of each talent is output as a numerical value. This output is displayed, for example, in the result display field 140 of the matching processing screen 100 shown in Fig. 5. As described above, the matching processing screen 100 is displayed on a client terminal such as the administrator terminal 41, and it can be said that the client terminal outputs the score of each talent as a numerical value.

[0099] In the example of Fig. 5, the user column of the result display column 140 displays A as the first ranked talent (the talent with the highest score), B as the second ranked talent (the talent with the next highest score), followed by C, D, and so on. In addition to showing the ranking, the total_score column displays the score of each talent, so that, for example, A's score is about 1.688, and it is easy to understand how high it is compared to the scores of other talents displayed in the same way.

[0100] In addition, in the related_tags field, the score is displayed for each attribute as a breakdown of the score, and in particular, each attribute is output in order according to the score. For example, in the case of Mr. A, the attribute "tends to use moving emojis" has the highest score of 0.34 and is displayed in first place (leftmost). Then, the attribute "origin thinking" has the next highest score of 0.31 and is displayed in second place (second from the left). Similarly, the following are followed by "easy to read documents" and "operation design". In this way, by ordering not only the score of the personnel but also each attribute of the personnel and displaying them together with the score, it is possible to easily understand the reason why the personnel has a high score for the project. In this way, according to the matching system of this embodiment, it becomes easier to evaluate the ranking of the matching results.

[0101] [Other embodiments] Those skilled in the art can add, change or delete components in each of the above-mentioned embodiments as desired within the scope of the present invention. For example, as a hardware configuration of the matching system, the entirety of FIG. 2 can be configured by one computer, or it can be configured by more computers than those shown in FIG. 2. In that case, those skilled in the art can appropriately design which computer executes each step described in FIGS. 8 to 11.

[0102] 2 shows two human resource terminals 42, the number of human resource terminals 42 may be one, or three or more. Also, a single human resource terminal 42 may be shared by a plurality of human resources.

[0103] In the above-described first embodiment, the normalization skill is used when calculating the score of the attribute, but it is possible to change so that the normalization skill is not used for some or all of the attributes. If the normalization skill is not used for any attribute, steps S2 and S3 in Fig. 8 can be omitted, and the attribute itself can be used instead of the normalization skill in the subsequent processing.

[0104] The order of steps shown in each diagram may be changed as appropriate. For example, the order of steps S141 and S142 in Fig. 11 may be interchanged. [Explanation of symbols]

[0105] 10…Matching server (matching system) 20…Large-scale language model server 30...Vector conversion model server 41...Administrator terminal 42…Human Resources Terminal 100...Matching processing screen 110...Text input field 120...Skills column 121...Skill display column 122...Skill input field 123…Strength change column 123a…Strength adjustment knob 124…Add button 125…Delete button 130…Submit button 140…Result display field 200…Attribute display screen 201 (201a, 201b)…Tags 202…Number of ratings 300…Normalized Skill Statistics Screen 301...Partially enlarged view D1: Human Resources Data

Claims

1. A matching system that performs matching processing between human resources and projects, Obtaining text data representing the content of the project; Identifying one or more project skills associated with the project based on the text data; Acquire one or more attributes for each of multiple personnel, Calculating a score for each attribute of each talent based on each of the project skills and each of the attributes; Calculating a score for each talent based on the scores for each attribute of that talent; outputting the plurality of talents in order based on the score of each talent, and outputting the score of each talent as a numerical value; The matching system includes: For each attribute, associate one or more normalization skills; calculating a score for each attribute based on each normalized skill associated with that attribute; Matching system.

2. A matching system that performs matching processing between human resources and projects, Obtaining text data representing the content of the project; Identifying one or more project skills associated with the project based on the text data; Acquire one or more attributes for each of multiple personnel, Calculating a score for each attribute of each talent based on each of the project skills and each of the attributes; Calculating a score for each talent based on the scores for each attribute of that talent; outputting the plurality of talents in order based on the score of each talent, and outputting the score of each talent as a numerical value; The matching system includes: For each attribute, associate one or more normalization skills; Obtain hierarchical information that represents the hierarchical relationship between each normalized skill; calculating a total number of the normalized skills associated with one or more of the human resources based on the hierarchical information; Matching system.

3. A matching system that performs matching processing between human resources and projects, Obtaining text data representing the content of the project; Identifying one or more project skills associated with the project based on the text data; Acquire one or more attributes for each of multiple personnel, Calculating a score for each attribute of each talent based on each of the project skills and each of the attributes; Calculating a score for each talent based on the scores for each attribute of that talent; outputting the plurality of talents in order based on the score of each talent, and outputting the score of each talent as a numerical value; The matching system includes: receiving an input from a first human resource to assign a new attribute to the first human resource; receiving an input from a second human resource to assign a new attribute to the first human resource; making the score of the attribute assigned to the first human resource by the second human resource greater than the score of the attribute assigned to the first human resource by the first human resource; Matching system.

4. A matching system that performs matching processing between human resources and projects, Obtaining text data representing the content of the project; Identifying one or more project skills associated with the project based on the text data; Acquire one or more attributes for each of multiple personnel, Calculating a score for each attribute of each talent based on each of the project skills and each of the attributes; Calculating a score for each talent based on the scores for each attribute of that talent; outputting the plurality of talents in order based on the score of each talent, and outputting the score of each talent as a numerical value; The matching system includes: receiving, from a second talent, an input of a positive evaluation of any of the attributes of the first talent; calculating a score for each attribute further based on the number of positive ratings for that attribute; Matching system.

5. The matching system includes: Further determining the strength of each project skill based on the text data; Each talent's score is further calculated based on the strength of each project skill. The matching system according to any one of claims 1 to 4.

6. The matching system includes: displaying each project skill associated with said project; Accepting input to add or remove one or more project skills; The matching system according to any one of claims 1 to 4.

7. The matching system includes: Shows the strength of each project skill, Accepts input to change the strength of any project skill, The matching system according to claim 5 .

8. The matching system includes: inputting information including the text data into a large-scale language model; obtaining the one or more project skills from the large-scale language model; The matching system according to any one of claims 1 to 4.

9. The matching system further includes a client terminal; The client terminal outputs the score of each talent as a numerical value. The matching system according to any one of claims 1 to 4.

10. A program that causes a computer to function as the matching system according to any one of claims 1 to 4.

11. A matching method for performing a matching process between human resources and projects, comprising: A step in which a computer acquires text data representing the content of the project; a computer identifying one or more project skills associated with the project based on the text data; A step in which a computer acquires one or more attributes for each of a plurality of human resources; A computer calculates a score for each attribute of each human resource based on each of the project skills and each of the attributes; A step in which a computer calculates a score for each talent based on the score of each attribute of the talent; a step in which the computer outputs the plurality of talents in order based on the score of each talent, and outputs the score of each talent as a numerical value; a computer associating one or more normalized skills with each attribute; calculating, by the computer, a score for each attribute based on each normalized skill associated with that attribute; A matching method comprising:

12. A matching method for performing a matching process between human resources and projects, comprising: A step in which a computer acquires text data representing the content of the project; a computer identifying one or more project skills associated with the project based on the text data; A step in which a computer acquires one or more attributes for each of a plurality of human resources; A computer calculates a score for each attribute of each human resource based on each of the project skills and each of the attributes; A step in which a computer calculates a score for each talent based on the score of each attribute of the talent; a step in which the computer outputs the plurality of talents in order based on the score of each talent, and outputs the score of each talent as a numerical value; a computer associating one or more normalized skills with each attribute; A step in which a computer obtains hierarchical information representing a hierarchical relationship between each normalized skill; calculating a total number of the normalized skills associated with one or more of the human resources based on the hierarchical information; A matching method comprising:

13. A matching method for performing a matching process between human resources and projects, comprising: A step in which a computer acquires text data representing the content of the project; a computer identifying one or more project skills associated with the project based on the text data; A step in which a computer acquires one or more attributes for each of a plurality of human resources; A computer calculates a score for each attribute of each human resource based on each of the project skills and each of the attributes; A step in which a computer calculates a score for each talent based on the score of each attribute of the talent; a step in which the computer outputs the plurality of talents in order based on the score of each talent, and outputs the score of each talent as a numerical value; a step of receiving, by a computer, an input from a first human resource for assigning a new attribute to the first human resource; a step of receiving, by a computer, an input from a second human resource for assigning a new attribute to the first human resource; a step of the computer increasing a score of an attribute assigned by the second human resource to the first human resource to be greater than a score of an attribute assigned by the first human resource to the first human resource; A matching method comprising:

14. A matching method for performing a matching process between human resources and projects, comprising: A step in which a computer acquires text data representing the content of the project; a computer identifying one or more project skills associated with the project based on the text data; A step in which a computer acquires one or more attributes for each of a plurality of human resources; A computer calculates a score for each attribute of each human resource based on each of the project skills and each of the attributes; A step in which a computer calculates a score for each talent based on the score of each attribute of the talent; a step in which the computer outputs the plurality of talents in order based on the score of each talent, and outputs the score of each talent as a numerical value; A step of receiving, by a computer, an input of a positive evaluation of any of the attributes of the first talent from a second talent; The computer calculates a score for each attribute further based on the number of positive ratings for that attribute; A matching method comprising:

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