Talent rating system, talent rating method, talent rating device, and talent rating program

Abstract

One aspect of the present disclosure is a human resources system including a subject terminal device, an external device for evaluating a subject, and a human resources device in which the subject terminal device and the external device are connected via a communication network. Rating The human resources device includes Rating an arithmetic unit that inputs human resources information of the subject acquired from the subject terminal device and an evaluation result of the subject acquired from the external device, and outputs characteristic information of the subject based on the human resources information and the evaluation result, and Rating a determination unit that determines the Rating of the subject based on the characteristic information output from the arithmetic unit. Rating This is a human resources system.

Description

[Technical field]

[0001] The present disclosure relates to Talent rating system and talent rating method Regarding. [Background technology]

[0002] As a conventional talent auction technology, for example, the talent auction system described in Patent Document 1 is known. The talent auction system described in Patent Document 1 matches the needs of both job seekers and companies hiring by using qualification certificates provided by qualification institutions as objective ability information of job seekers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2015-69507 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the talent auction described in Patent Document 1, it is desirable to take into consideration a comprehensive evaluation that includes other factors than the job seeker's qualifications, such as work history and evaluations from the workplace.

[0005] The present disclosure has been made in light of these circumstances, and is intended to provide a method for comprehensively evaluating human resources. Talent rating system and talent rating method The purpose is to provide. [Means for solving the problem]

[0006] The present disclosure has been made to solve the above-mentioned problems, and one aspect of the present disclosure is a talent rating device including a subject terminal device used by a subject, an external device that evaluates the subject, and a talent rating device in which the subject terminal device and the external device are connected via a communication network; A dispatch destination terminal device used by a dispatch destination company of a human resource, the target terminal device, and a human resource bidding device connected to the dispatch destination terminal device via a communication network;the talent rating device comprises: a calculation unit that receives talent information of the subject acquired from the subject terminal device and an evaluation result of the subject acquired from the external device, and outputs characteristic information of the subject based on the talent information and the evaluation result; and a rating determination unit that determines a rating of the subject based on the characteristic information output from the calculation unit; a bid price determination unit that determines a bid price of a target person based on the rating determined by the rating determination unit; Equipped with The human resource information of the target is information declared by the target, the evaluation result is a result of an external organization evaluating the target, and the external organization is an organization that evaluates the target different from the temporary staffing agency, the human resource bidding device includes an information providing unit that transmits bidding price information indicating the bidding price determined by the bidding price determination unit to the dispatch destination terminal device, a bid receiving unit that receives a bid request including the target information and bid point number information from the dispatch destination terminal device, and a dispatch destination determination unit that determines the dispatch destination of the target based on the bid request, and the bid price indicates the number of bid points required to bid on the target, It is a talent rating system.

[0007] Another aspect of the present disclosure is a subject terminal device used by a subject, an external device that evaluates the subject, and a talent rating device in which the subject terminal device and the external device are connected via a communication network; A dispatch destination terminal device used by a dispatch destination company of a human resource, the target terminal device, and a human resource bidding device connected to the dispatch destination terminal device via a communication network; a talent rating method for a talent rating system comprising: a step of the subject terminal device transmitting talent information of the subject to the talent rating device; a step of the external device transmitting an evaluation result of the subject to the talent rating device; a step of the talent rating device acquiring the talent information of the subject and the evaluation result of the subject; a step of the talent rating device calculating characteristic information of the subject based on the talent information and the evaluation result; and a step of the talent rating device determining a rating of the subject based on the characteristic information. the human resource rating device determines the bid price of the target person based on the determined rating; the human resource bidding device transmits bid price information indicating the determined bid price to the dispatch destination terminal device; the human resource bidding device receives a bid request including target person information and bid point number information from the dispatch destination terminal device; and the human resource bidding device determines a dispatch destination of the target person based on the bid request, and is equipped with a dispatch destination determination unit, wherein the human resource information of the target person is information declared by the target person, the evaluation result is a result of an external organization evaluating the target person, the external organization being an entity that evaluates the target person different from the dispatch destination company, and the bid price indicates the number of bid points required to bid on the target person, This is a human resource rating method. Effect of the Invention

[0010] According to the present disclosure, human resources can be evaluated comprehensively. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of a talent rating system according to an embodiment. [Diagram 2] 1 is a block diagram showing an example of a configuration of a talent bidding device and a talent rating device according to an embodiment; [Diagram 3]1 is a block diagram showing an example of a specific configuration of a talent rating system according to an embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a multivariate analysis unit in the embodiment. [Diagram 5] FIG. 11 is a diagram illustrating an example of a mapping process of a feature vector according to an embodiment. [Figure 6] FIG. 11 is a diagram showing an example of a subject extraction process using a feature vector in the embodiment. [Figure 7] FIG. 11 is a diagram showing an example of a calculation process of a bid price in the embodiment. [Figure 8] 4 is a sequence diagram showing an example of an operation procedure of the talent rating system according to the embodiment. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The following is a list of people who have applied this invention: Rating Systems and personnel Rating Methods, Personnel Rating Equipment, and personnel Rating The program will be described with reference to the drawings.

[0013] FIG. 1 is a diagram showing a human resources Rating FIG. 1 is a block diagram showing an example of a system 1. Rating The system 1 includes, for example, a talent bidding device 100, Data Processing The human resources bidding device 100 includes a bidding device 200, a target person terminal device 300, a dispatching source terminal device 400, a dispatching destination terminal device 500, an external device 600, and a training content providing device 700. Data ProcessingThe device 200, the target person terminal device 300, the dispatching source terminal device 400, the dispatching destination terminal device 500, the external device 600, and the training content providing device 700 are connected via a communication network NW and have a communication interface (not shown) such as a NIC (Network Interface Card) or a wireless communication module for connecting to a network such as the Internet. The network may include, for example, a general-purpose network such as the Internet, and a private network such as local 5G or WiFi (registered trademark).

[0014] The subject terminal device 300 is an information processing device such as a smartphone or a personal computer used by the subject. The dispatching source terminal device 400 is an information processing device such as a smartphone or a personal computer used by the dispatching source company. The dispatching destination terminal device 500 is an information processing device such as a smartphone or a personal computer used by the dispatching destination company. The external device 600 is an information processing device such as a server device that receives a request from the subject terminal device 300, information from the dispatching source terminal device 400, and information from the dispatching destination terminal device 500, and performs processing to evaluate the subject from the outside. The training content providing device 700 is an information processing device such as a server device that receives a request from the subject terminal device 300, and performs processing to transmit content for training and education to the subject terminal device 300.

[0015] FIG. 2 shows the talent bidding device 100 and Data Processing2 is a block diagram showing an example of the configuration of the device 200. The human resource bidding device 100 is an information processing device that provides a service of registering, for example, human resource information, evaluation results, dispatching agency information, and dispatching destination agency information, and determining the dispatch destination of the target based on the bid of the dispatching destination agency. The target as human resource is, for example, a professional with professional qualifications and skills such as a pharmacist. The human resource information is information declared by the target. The human resource information may be information including the target's age, possible work location, working style, work history, or possessed skills. The evaluation result includes evaluation information such as the test results taken by the target using an external device that evaluates the target. The evaluation result may be information including the target's training attendance history, work history at the dispatching destination, or the target's evaluation obtained from the dispatching destination terminal device 500. The dispatching agency information is information that identifies the target's dispatching agency. The dispatching destination agency information is information that identifies the dispatching destination agency.

[0016] The talent bidding device 100 includes, for example, an issuing unit 110, an information providing unit 120, a bid receiving unit 130, and a dispatch destination determination unit 140. The issuing unit 110, the information providing unit 120, the bid receiving unit 130, and the dispatch destination determination unit 140 are realized by a processor such as a CPU (Central Processing Unit) executing a program stored in a program memory.

[0017] The issuing unit 110 issues bidding points to the temporary employment agency. The issuing unit 110 may issue bidding points to the temporary employment agency and the dispatching agency. The bidding points are information that functions as a virtual currency that is virtually traded for bidding among the human resource bidding device 100, the dispatching agency, and the temporary employment agency. The issuing unit 110 may perform at least one of the following processes: a process of periodically issuing bidding points based on the temporary employment agency information being registered; a process of periodically issuing bidding points based on the dispatching agency information being registered; and a process of issuing bidding points based on the browsing history of specific content. The specific content is, for example, content that includes information on the target person, the dispatching agency, and the dispatching agency provided by the human resource bidding device 100. The specific content may be content that is displayed when the temporary employment agency inputs the recruitment conditions, may be content that is displayed when the temporary employment agency makes a bid, or may be content that is displayed when the dispatching agency registers the target person. The temporary employment agency holds the issued bidding points and can make a bid using the held bidding points.

[0018] The information providing unit 120 transmits the human resource information acquired from the target person terminal device 300 and the evaluation results acquired from the external device 600 to the dispatch destination terminal device 500. For example, the information providing unit 120 accepts a request including recruitment conditions from the dispatch destination terminal device 500, and responds to the dispatch destination terminal device 500 with the human resource information and evaluation results of the target person extracted based on the recruitment conditions. The recruitment conditions are, for example, the human resource conditions desired by the dispatch destination agent from the human resource information and the evaluation results.

[0019] The bid receiving unit 130 receives a bid request including target person information and bid point number information from the dispatch destination terminal device 500. The target person information is information that identifies the target person. The bid point number information is information that indicates the number of bid points to be used for the bid of the target person from among the bid points held by the dispatch destination company.

[0020] The dispatch destination determination unit 140 determines the dispatch destination of the target person based on the bid request. When the dispatch destination determination unit 140 receives one bid request from one target person, it determines the dispatch destination company of the dispatch destination terminal device 500 that sent the bid request as the successful bidder. When the dispatch destination determination unit 140 receives multiple bid requests from one target person, it determines the dispatch destination company with the largest number of bid points as the successful bidder.

[0021] Data Processing The device 200 includes, for example, a calculation unit 210 and Decision unit 220 and a bid price determination unit 230. Talent Rating Device The calculation unit 210, Decision unit 220 and the bid price determination unit 230 are realized by a processor such as a CPU executing a program stored in a program memory.

[0022] The calculation unit 210 inputs the subject's human resource information acquired from the subject terminal device 300 and the subject's evaluation result acquired from the external device 600 as a human resource evaluation device, and outputs the subject's feature information. The feature information is, for example, a high-dimensional feature vector including the subject's human resource information and the evaluation result. Decision unit 220 Based on the characteristic information output from the calculation unit 210, Rating Determine the target Rating is information indicating the value of a target person, such as the target person's demand level, recommendation level, and rank among many targets.

[0023] The bid price determination unit 230 Decision unit 220 Determined by Rating The initial bid price of the target is determined based on the above. The target's bid price indicates the number of bidding points required for the dispatching company to bid for the target. The initial bid price of the target is RatingThe higher the value, the higher the bid price is set. The bid price determination unit 230 corrects the bid price to be higher as the number of bid requests increases. The bid price determination unit 230 may correct the bid price to be lower when the number of bid requests is low. In response to a request from the dispatch destination terminal device 500, the information providing unit 120 transmits the target person information and the bid price determined by the bid price determination unit 230 to the dispatch destination terminal device 500.

[0024] The dispatch destination determination unit 140 determines the dispatch destination company that has made a successful bid for the target person based on the bid price determined by the bid price determination unit 230 and the bid request.

[0025] FIG. 3 shows the human resources in the embodiment. Rating FIG. 2 is a block diagram showing an example of a specific configuration of the system 1. Human Resources Rating System 1 is a function for matching talent and Rating The talent matching function includes, for example, a point management device 102 and a talent Rating The system is realized by a target employee terminal device 300, a dispatching source terminal device 400, and a dispatching destination terminal device 500, which are terminal devices operated by users of the system 1, and a talent matching device 104. The point management device 102 and the talent matching device 104 are functional parts realized by the talent bidding device 100.

[0026] Human Resources Rating The functions include, for example, an external device 600, a talent registration device 202, a storage device 212, and a learning device 214. The learning device 214 includes, for example, a talent characteristic evaluation unit 214a, a talent value evaluation unit 214b, and a learning information update unit 214c. The talent registration device 202, the storage device 212, and the learning device 214 include, Data Processing This is a functional unit realized by 200.

[0027] The talent matching device 104 is, for example, a functional unit realized by the talent bidding device 100. The talent matching device 104 includes, for example, an information providing unit 120, a bid receiving unit 130, and a dispatch destination determining unit 140.

[0028] The point management device 102 includes an issuing unit 110 and a consuming unit 112. When a dispatch destination is determined by the dispatch destination determining unit 140, the consuming unit 112 consumes the bidding points by subtracting the number of bidding points from the bidding points held by the successful bidder. The consuming unit 112 does not subtract the number of bidding points of dispatching companies other than the successful bidder.

[0029] The human resource registration device 202 includes, for example, a company demand registration unit 204 and a human resource information registration unit 206. The company demand registration unit 204 acquires target person information of targets who can be dispatched from the dispatch source terminal device 400. The company demand registration unit 204 acquires information indicating job requirements from the dispatch destination terminal device 500. The company demand registration unit 204 registers information indicating the demand of the dispatch source company and the dispatch destination company by storing the information acquired from the dispatch source terminal device 400 and the dispatch destination terminal device 500 in the storage device 212. The human resource information registration unit 206 stores the human resource information acquired from the target person terminal device 300 in the storage device 212.

[0030] The external device 600 is an information processing device that performs a process of evaluating the subject. The external device 600 includes, for example, a test implementation unit 710 and a comprehensive evaluation unit 410. The test implementation unit 710 transmits test content to the subject terminal device 300 and calculates test results based on answers acquired from the subject terminal device 300. The test implementation unit 710 stores the test results in the member information database 212a. The comprehensive evaluation unit 410 acquires, for example, evaluation information of the subject in the dispatching terminal device 400 and a viewing (attending) history of training content provided to the subject from the training content providing device 700, and stores the information in the member information database 212a as information for comprehensively evaluating the subject.

[0031] The storage device 212 is an information processing device that stores various information. The storage device 212 includes, for example, a member information database 212a and a learning database 212b. The member information database 212a accumulates information on dispatching agencies, dispatching destination agencies, and subjects as members. Specifically, the member information database 212a accumulates subject information belonging to the dispatching agency, information indicating the recruitment conditions of the dispatching destination agency, and information indicating the human resources information and evaluation results of the subjects. The learning database 212b acquires the feature vector of the subject and the feature vector indicating the recruitment conditions, and stores the feature vector as learning data.

[0032] The human resource characteristic evaluation unit 214a includes, for example, a calculation unit 210. The calculation unit 210 acquires the human resource information and evaluation results of the target from the member information database 212a, and converts the acquired information into a high-dimensional feature vector. The target's feature vector is information that expresses the target in a high-dimensional space. The calculation unit 210 stores the target's feature vector in the learning database 212b. In addition, the human resource characteristic evaluation unit 214a converts the recruiting conditions acquired from the temporary staffing agency into a feature vector of the recruiting conditions having values ​​corresponding to the requests of the temporary staffing agency, such as the human resources, working hours, and location. The feature vector of the recruiting conditions is information that expresses the recruiting conditions in a high-dimensional space. The calculation unit 210 stores the feature vector of the recruiting conditions in the learning database 212b.

[0033] The human resource value assessment unit 214b, for example, Decision unit 220 and a bid price determination unit 230. Decision unit 220 The human resource characteristic evaluation unit 214a evaluates the subject's characteristic vector according to the subject's characteristic vector obtained from the human resource characteristic evaluation unit 214a. Rating The bid price determination unit 230 changes the Rating The human resource value evaluation unit 214b outputs an instruction to the test implementation unit 710 to change the test contents, and updates the feature vector by the calculation unit 210 based on the test results of the subjects who took the changed test, and determines the bid price of the subjects. Rating Show RatingThe human resource value assessment unit 214b updates the test results and the bidding price information indicating the bidding price. The human resource value assessment unit 214b assesses the value of the subject by updating the feature vector based on the test results and the information for comprehensively assessing the subject.

[0034] The learning information update unit 214c acquires the feature vector from the human resource value assessment unit 214b and outputs it to the human resource matching device 104. The human resource matching device 104 updates the feature vector of the subject stored in the learning database 212b with the feature vector acquired from the learning information update unit 214c.

[0035] FIG. 4 is a diagram showing an example of a multivariate analysis unit 200A in the embodiment. The multivariate analysis unit 200A includes a human resource characteristic evaluation unit 214a (the calculation unit 210) and a human resource value evaluation unit 214b ( Decision unit 220 , the feature vector in the bid price determination unit 230), Rating The multivariate analysis unit 200A inputs, for example, self-reported information and external evaluation information, and performs multivariate analysis to output a feature vector.

[0036] The self-reported information is information reported by the subject, such as national qualification information, work history information, university major information, specialty and specialty field information, available work area information, available work time information, work style information such as commuting or remote work, available language information, and home information. The external evaluation information is information evaluated by an external organization, such as online examination information, comprehensive evaluation information of the dispatched organization, and information on the professional industry to which the subject belongs (e.g., the pharmacist association). The external organization includes persons other than the subject, such as the dispatching company, the examination organizer, the training organizer, and the educational content provider. The multivariate analysis unit 200A performs processing using any of logistic regression analysis, comparative hazard analysis, analysis of variance, multiple regression analysis, discriminant analysis, principal component analysis, factor analysis, and cluster analysis as the multivariate analysis. The multivariate analysis may include, for example, summary processing such as principal component analysis and factor analysis, classification processing such as cluster analysis and discriminant analysis, and prediction processing such as regression analysis.

[0037] FIG. 5 is a diagram showing an example of mapping processing of feature vectors in the embodiment. The calculation unit 210 may compress the number of dimensions of the feature vector of the subject and the feature vector of the recruitment conditions, and extract subjects having feature vectors close to the feature vector of the recruitment conditions based on the distance between the feature vector of the subject and the feature vector of the recruitment conditions in the compressed number of dimensions. t-SNE (t-distributed Stochastic Neighbor Embedding) is known as a method of distilling high-dimensional feature vectors (left diagram of FIG. 5) and mapping them to a 2D space (right diagram of FIG. 5). The multivariate analysis unit 200A needs to process the feature vectors in real time, but when comparing feature vectors in a high-dimensional feature vector space with t-SNE, the more input variables there are, the longer the calculation time required. Therefore, the multivariate analysis unit 200A can speed up the comparison process of feature vectors by compressing the dimensions of the feature vectors using t-SNE or RP (Random Projection) and finding the distance in the 2D space. For example, the high-dimensional feature vector of the subject is normalized to a 2D space, and the feature vector of the human resources required by the dispatching agency is also normalized to a 2D space. This allows the multivariate analysis unit 200A to narrow down candidates who are similar to the personnel desired by the dispatching agency in a short period of time.

[0038] FIG. 6 is a diagram showing an example of a target person extraction process using a feature vector according to the embodiment. The multivariate analysis unit 200A performs multivariate analysis on information including the recruitment conditions, thereby mapping the feature vector of the recruitment conditions into a high-dimensional feature vector space. When the feature vector of the recruitment conditions converges to an arbitrary point (x in the figure) in the feature vector space, the multivariate analysis unit 200A uses k-nearest neighbors to extract k candidates in the order of subjects having feature vectors that are closest to the arbitrary point. When the feature vector of the recruitment conditions is scattered in an arbitrary area of ​​the feature vector space as in the ellipse in FIG. 6, the human resource characteristic evaluation unit 214a extracts all candidates included in the feature vector space including the recruitment conditions. In order to speed up the calculation, the multivariate analysis unit 200A may perform t-SNE or RP to reduce the number of dimensions of the feature vector space, and extract candidates in the reduced feature vector space.

[0039] The multivariate analysis unit 200A performs multivariate analysis on the self-reported information and external evaluation information as multiple input explanatory variables, and calculates the match rate (similarity and correlation) between the subject and the job requirements in the feature vector space. This enables the multivariate analysis unit 200A to propose candidate dispatch destinations that take into account non-trivial correlations and similarities in a high-dimensional feature space, rather than determining the correlation and similarity between the subject and the dispatch destination by focusing only on certain explanatory variables.

[0040] FIG. 7 is a diagram showing an example of a calculation process of a bid price in the embodiment. The bid price determination unit 230 may change the bid price based on the bid request received by the bid reception unit 130, and may change the fluctuation range of the bid price based on the distance between the target person's feature vector and the feature vector of the recruitment conditions. The fluctuation range of the bid price indicates, for example, the change in the bid price that is updated in response to an increase in the number of bids for a certain target person. The bid price determination unit 230 of the human resource value assessment unit 214b may increase the bid price (y in the figure) as the distance d (x in FIG. 7, x=1 / d) between the target person's feature vector and the feature vector of the recruitment conditions becomes closer. For example, the bid price determination unit 230 increases the correction range y of the initial bid price g by multiplying the reciprocal of the distance d between the target person's feature vector and the feature vector of the recruitment conditions by logx, as shown in the following formula. y = g x log x (x ≥ 1) or g (x < 1) In this way, the bid price determination unit 230 can vary the range of increase in the bid price to a larger extent for candidates who are closer to the recruitment conditions of the temporary staffing agency. On the other hand, when there is no candidate who is close to the recruitment conditions of the temporary staffing agency, the bid price determination unit 230 can vary the range of increase in the bid price to a smaller extent.

[0041] FIG. 8 shows the human resources in the embodiment. Rating FIG. 2 is a sequence diagram showing an example of an operation procedure of the system 1. First, the target terminal device 300 transmits registration information S10 including human resource information to the human resource bidding device 100. Data Processing The dispatching company terminal device 400 transmits the registration information S12 including the dispatching company information and the target person information to the talent bidding device 100. The dispatching company information and the target person information are Data Processing The dispatching destination terminal device 500 transmits the registration information S14, including the dispatching destination information and the job offer information, to the personnel bidding device 100. The dispatching destination information and the job offer information are Data ProcessingThe information S14 is transmitted to the destination terminal device 500 and stored in the storage device 212. The destination terminal device 500 may include fee information that the destination company periodically pays to the administrator of the talent bidding device 100, and may include fee information for obtaining bidding points, in the registration information S14. The talent bidding device 100 transmits fee information S16, which requests the destination company to pay a brokerage fee, to the destination terminal device 500. The talent bidding device 100 transmits fee information S18, which requests the dispatching company to pay a brokerage fee, to the dispatching company terminal device 400. This enables the operator of the talent bidding device 100 to obtain the brokerage fee from the destination company and the dispatching company.

[0042] The talent bidding device 100 transmits point issuing information S20 indicating the bidding points issued to the temporary staffing company by the issuing unit 110 to the temporary staffing company terminal device 500. The talent bidding device 100 transmits point issuing information S22 indicating the bidding points issued to the temporary staffing company by the issuing unit 110 to the temporary staffing company terminal device 400.

[0043] The dispatching source terminal device 400 transmits to the talent bidding device 100 dispatch registration information S24 including target person information of the targets to be dispatched among the registered targets. Data Processing The device 200 performs the following based on the feature vector of the target person included in the dispatch registration information S24. Rating Determine, Rating Based on the information, the bidding price information S26a indicating the bidding price is transmitted to the talent bidding device 100. The talent bidding device 100 Rating The information and the bid price information S26a are transmitted to the target terminal device 300, the dispatching company terminal device 400, and the dispatching company terminal device 500. Rating and the bid prices can be viewed.

[0044] Data ProcessingThe device 200 uses the feature vector of the recruitment conditions and the feature vector of the target person to extract targets having a feature vector close to the feature vector of the recruitment conditions, and transmits matching information S26b including target person information of the extracted targets to the talent bidding device 100. The information providing unit 120 transmits the matching information S26b to the dispatch destination terminal device 500. This allows the dispatch destination agent to view targets matching the recruitment conditions.

[0045] A plurality of destination terminal devices 500 transmit bid request information S28a, 28b, ..., including target information and bid point number information of a certain target person, to the talent bidding device 100. The bid price determination unit 230 varies the bid price according to the received bid request. The destination determination unit 140 determines the destination company with the highest number of bid points among the plurality of bid request information S28a, 28b, ..., as the successful bidder, and transmits successful bid information S30, indicating the successful bidder, to the destination terminal device 500, the dispatching source terminal device 400, and the target person terminal device 300.

[0046] The human resource bidding device 100 transmits point information S32a, which is obtained by subtracting the number of bidding points from the bidding points held by the successful bidder, to the destination terminal device 500 of the successful bidder, and transmits point information S32b, ..., which returns the number of bidding points to the unsuccessful dispatching companies to the destination terminal devices 500 of the dispatching companies other than the successful bidder. The issuing unit 110 of the human resource bidding device 100 transmits point information S34, which issues bidding points as a success fee, to the dispatching source terminal device 400 corresponding to the dispatching source company of the successful target.

[0047] When the target person begins working at the dispatch destination, the issuing unit 110 transmits fee information S36a indicating a portion of the employment fee (hourly wage) as compensation for the target person's labor to the dispatch source terminal device 400 of the dispatch source company of the successful bidder. The fee information S36a may be information indicating the number of bid points. This allows the dispatch source company to receive a reward for dispatching the target person. In addition, the human resources bidding device 100 transmits fee information S36b indicating a portion of the employment fee (hourly wage) to the dispatch destination terminal device 500 of the dispatch destination company. This allows the dispatch destination company to receive a reward for employing the target person.

[0048] The human resource matching device 104 may perform collaborative filtering processing to recommend a target person to the temporary employment agency. The human resource matching device 104 may search for temporary employment agencies with a similar target person based on the search history or bidding history of the temporary employment agency, and recommend a target person similar to the target person who was successful bid by the searched temporary employment agency. The human resource matching device 104 may recommend a target person having a feature vector similar to the feature vector of the target person being viewed by the temporary employment agency by performing content-based filtering processing. The human resource matching device 104 may estimate the feature vector of the recruitment conditions of the temporary employment agency by collaborative filtering processing, and recommend a target person having a feature vector similar to the feature vector of the estimated recruitment conditions by content filtering processing.

[0049] Furthermore, the learning device 214 may train a prediction model using, as learning data, for example, the feature vector of the recruitment conditions of the temporary staffing agency, the feature vector of the target person for whom the temporary staffing agency has made a successful bid, and the successful bid price (number of bid points) updated by the learning information update unit 214c, input the feature vector of the recruitment conditions to the prediction model as an explanatory variable, and recommend a target person having a feature vector similar to the target person's feature vector output from the prediction model and an initial value of the bid price (inference result). The prediction model may use statistical models such as normal distribution and binomial distribution, and has parameters for identifying the statistical model. The parameters are set to optimal values ​​for outputting the inference result by the learning process.

[0050] The prediction model may be trained by either unsupervised learning or supervised learning. The unsupervised learning includes, for example, dimensionality reduction processing and clustering processing. The dimensionality reduction includes, for example, principal component analysis, multidimensional scaling, t-SNE, etc. The clustering processing includes, for example, k-means ms, hierarchical clustering, etc. The supervised learning includes, for example, classification processing or regression processing. The classification processing includes, for example, decision trees, support vector machines, random forests, logistics regression, etc. The regression processing includes, for example, partial least squares regression (PLS), lasso regression (least absolute shrinkage and selection operator, LASSO), ridge regression, support vector machines, random forests, logistics regression, etc. The learning device 214 constructs a prediction model using these learning methods, and performs multivariate analysis using the constructed prediction model in the multivariate analysis unit 200A (the calculation unit 210, Decision unit 220 , and the bid price determination unit 230).

[0051] As described above, according to the embodiment, the subject terminal device 300, the external device 600 that evaluates the subject, and the subject terminal device 300 and the external device 600 connected via the communication network NW Data Processing The device 200 includes: Rating The apparatus includes a calculation unit 210 that receives the personnel information of the subject acquired from the subject terminal device 300 and the evaluation result of the subject acquired from the external device 600, and outputs characteristic information of the subject based on the personnel information and the evaluation result; Rating Determine Decision unit 220 And, human resources with Rating System 1 can be realized.

[0052] Human Resources Rating According to System 1, the subject is selected based on the information declared by the subject as the subject's personnel information and the results of the subject's evaluation by an external organization as the evaluation result. Rating This allows you to comprehensively evaluate personnel and provide them with the information they need to be able to effectively serve the job seekers and dispatching companies. Rating It is possible to visualize the above.

[0053] Human Resources Rating According to System 1, Decision unit 220 Determined by Rating The bidding price of the target person is determined based on the above, bidding price determination unit 230 transmits bidding price information indicating the bidding price determined by the bidding price determination unit 230 to the dispatch destination terminal device 500, and a bid request including the target person information and bid point number information is received from the dispatch destination terminal device 500, and a dispatch destination of the target person is determined based on the bid request. Rating According to System 1, Rating Based on this, we can assist you in bidding for the target.

[0054] Human Resources Rating According to the system 1, for example, assuming that the target is a pharmacist, by winning the bid for a pharmacist who can work at a holiday outpatient clinic or at a drug store in an area where there is a shortage of pharmacists, it is possible to prevent situations where there is a shortage of pharmacists and provide medicines to patients. Rating According to System 1, in order to improve the skills of pharmacists, companies and pharmacies can dispatch pharmacists to workplaces with different industries or work styles as part of their training, and this has the advantage that dispatching companies can improve the skills of their pharmacists and increase the bidding price for pharmacists. Rating According to the system 1, training content can be provided free of charge in response to registration of target information, and the bidding price of pharmacists can be improved according to their training attendance history. Rating System 1 allows pharmacists to visualize the bidding prices based on their own skills, and also increases their value in the eyes of dispatching companies.

[0055] Human Resources Rating According to the system 1, a feature vector based on the human resource information and the evaluation result is calculated, and according to the calculated feature vector, RatingThe determined Rating This allows the initial bid value to be changed based on the Rating System 1 can set a bid price that appropriately evaluates the target person according to the target person's qualifications and evaluation.

[0056] Human Resources Rating According to the system 1, a feature vector is calculated based on the job-seeking conditions received from the destination terminal device 500, and matching information including the target person's feature vector and information on targets who have feature vectors similar to the feature vector of the job-seeking conditions can be transmitted to the destination terminal device 500. Rating System 1 can eliminate the need for manual searching for candidates who meet the job requirements.

[0057] Human Resources Rating According to system 1, the number of dimensions of the feature vectors of the target persons and the feature vectors of the job requirements can be compressed, and targets having feature vectors close to the feature vector of the job requirements in the compressed number of dimensions can be extracted, thereby speeding up the process of extracting targets close to the job requirements.

[0058] Human Resources Rating According to the system 1, the bid price can be changed based on a bid request, and the fluctuation range of the bid price can be changed based on the distance between the feature vector of the target person and the feature vector of the job requirements.

[0059] Although each embodiment and each variant have been described, these are merely examples and are not intended to be limiting. For example, any of the embodiments or variants, or a part of each embodiment or a part of each variant, may be combined with one or more other embodiments or one or more other variants to realize one aspect of the present invention. [Explanation of symbols]

[0060] 1...human resource bidding support system, 100...human resource bidding device, 102...point management device, 104...human resource matching device, 110...issuing department, 112...consuming department, 120...information providing department, 130...bid receiving department, 140...dispatch destination determining department, 200... Data Processing Apparatus, 200A...multivariate analysis unit, 202...human resources registration device, 204...business demand registration unit, 206...human resources information registration unit, 210...arithmetic unit, 212...storage device, 212a...membership information database, 212b...learning database, 214...learning device, 214...human resources characteristic evaluation unit, 214a...human resources characteristic evaluation unit, 214b...human resources value evaluation unit, 214c...learning information update unit, 220... Rating A decision unit, 230...bid price determination unit, 300...target person terminal device, 400...dispatch source terminal device, 410...overall evaluation unit, 500...dispatch destination terminal device, 600...external device, 700...training content providing device, 710...examination implementation unit

Claims

1. A personnel rating system comprising a subject terminal device used by a subject, an external device for evaluating the subject, and a personnel rating device in which the subject terminal device and the external device are connected via a communication network, wherein the personnel rating device includes an arithmetic unit that inputs the personnel information of the subject acquired from the subject terminal device and the evaluation result of the subject acquired from the external device, and outputs the characteristic information of the subject based on the personnel information and the evaluation result; and a rating determination unit that determines the rating of the subject based on the characteristic information output from the arithmetic unit. A personnel rating system.

2. The personnel rating system according to claim 1, wherein the personnel information of the subject is information declared by the subject, and the evaluation result is the result of evaluating the subject by an external institution.

3. A personnel bidding device including a destination terminal device used by a personnel dispatching destination company, the subject terminal device, and the destination terminal device connected via a communication network, wherein the personnel rating device includes a bid price determination unit that determines the bid price of a subject based on the rating determined by the rating determination unit, and the personnel bidding device includes an information providing unit that transmits bid price information indicating the bid price determined by the bid price determination unit to the destination terminal device; a bid reception unit that receives a bid request including subject information and bid point number information from the destination terminal device; and a destination determination unit that determines the destination of the subject based on the bid request. The personnel rating system according to claim 1.

4. The arithmetic unit calculates a feature vector based on the personnel information and the evaluation result, the rating determination unit changes the rating according to the feature vector calculated by the arithmetic unit, and the bid price determination unit changes the initial value of the bid price based on the rating determined by the rating determination unit. The personnel rating system according to claim 3.

5. The arithmetic unit calculates a feature vector based on the job requirements received from the destination terminal device, and the information providing unit transmits matching information including information of a subject having a feature vector close to the feature vector of the subject calculated by the arithmetic unit and the feature vector of the job requirements calculated by the arithmetic unit to the destination terminal device. The human resource rating system according to claim 4.

6. The arithmetic unit compresses the dimensionality of the feature vector of the target person and the feature vector of the job requirements, and extracts a target person having a feature vector close to the feature vector of the job requirements in the compressed dimensionality. The human resource rating system according to claim 5.

7. The auction price determination unit changes the auction price based on the auction request received by the bid reception unit, and changes the fluctuation range of the auction price based on the distance between the feature vector of the target person and the feature vector of the job requirements. The human resource rating system according to claim 5.

8. A human resource rating method for a human resource rating system including a target person terminal device used by a target person, an external device for evaluating the target person, and a human resource rating device in which the target person terminal device and the external device are connected via a communication network, The step of the target person terminal device transmitting the human resource information of the target person to the human resource rating device; The step of the external device transmitting the evaluation result of the target person to the human resource rating device; The step of the human resource rating device acquiring the human resource information of the target person and the evaluation result of the target person; The step of the human resource rating device calculating the feature information of the target person based on the human resource information and the evaluation result; The step of the human resource rating device determining the rating of the target person based on the feature information; A human resource rating method including the above.

9. In a human resource rating device in which a target person terminal device used by a target person and an external device for evaluating the target person are connected via a communication network, An arithmetic unit that inputs the human resource information of the target person acquired from the target person terminal device and the evaluation result of the target person acquired from the external device, and outputs the feature information of the target person based on the human resource information and the evaluation result; A rating determination unit that determines the rating of the target person based on the feature information output from the arithmetic unit; A human resource rating device comprising the above.

10. A computer of a human resource rating device in which a target person terminal device used by a target person and an external device for evaluating the target person are connected via a communication network, An arithmetic unit that inputs the human resource information of the target person acquired from the target person terminal device and the evaluation result of the target person acquired from the external device, and outputs the characteristic information of the target person based on the human resource information and the evaluation result, and A human resource rating program that functions as a rating determination unit that determines the rating of the target person based on the characteristic information output from the arithmetic unit.

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