Estimation device, estimation method, and estimation program

Abstract

An estimation device (10) for estimating a candidate career plan of a subject user comprises an acquisition unit (134), an estimation unit (135), and an estimation result output unit (136). The acquisition unit (134) acquires a task characteristic of each task that the subject user has been in charge of up to the current time. After that, the estimation unit (135) estimates a candidate career plan appropriate for the subject user from the task characteristic for the subject user on the basis of a model trained by using a task characteristic for each reference user who has responded that the current business field fits himself or herself. Then, the estimation result output unit (136) outputs the result of estimating a candidate career plan appropriate for the subject user.

Description

[Technical field]

[0001] The present invention relates to an estimation device, an estimation method, and an estimation program for estimating candidates for a user's career plan. [Background technology]

[0002] When considering the career plan of a user (for example, an employee), it is necessary to take into consideration both the characteristics of the user and the characteristics of the organization (for example, a department, etc.). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Basic knowledge of human resource development, 7th edition, Career design, [Retrieved July 9, 2021], Internet<https: / / hr.nttls.co.jp / column / knowledge / step1 / detail-07.html> Summary of the Invention [Problem to be solved by the invention]

[0004] However, in addition to the wide variety of user characteristics, there are also many organizations. Therefore, it is time-consuming to consider combinations of all user characteristics and organization characteristics in order to consider candidates for a user's career plan. Furthermore, even if candidates for a career plan are considered using the above method, they may not necessarily be suitable for the user. Therefore, the present invention aims to solve the above problems, reduce the time and effort required to consider candidates for a user's career plan, and improve accuracy. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention is an estimation device that estimates candidate career plans for a target user, and is characterized by comprising: an acquisition unit that acquires work characteristics for each work that the target user has handled up to now; an estimation unit that estimates candidate career plans suitable for the target user from the work characteristics of the target user based on a model learned using the work characteristics of each reference user who answered that they are suited to their current work field; and an estimation result output unit that outputs the estimation result of the candidate career plans suitable for the target user. Effect of the Invention

[0006] According to the present invention, it is possible to reduce the effort required for a user to consider possible career plans and to improve accuracy. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram for explaining an outline of the operation of the estimation device. [Diagram 2] FIG. 2 is a diagram showing examples of candidate career plans for employees output by the estimation device of FIG. [Diagram 3] FIG. 3 is a diagram showing examples of career plan candidates for managers output by the estimation device of FIG. [Figure 4] FIG. 4 is a diagram illustrating an example of personnel data used by the estimation device. [Diagram 5] FIG. 5 is a diagram illustrating the learning data used by the estimation device and an overview of the flow from model learning to the estimation of career plan candidates. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of an estimation device. [Figure 7] FIG. 7 illustrates a specific example of data preprocessing and feature generation by the estimation device. [Figure 8] FIG. 8 is a diagram illustrating an example of characteristics that the estimation device uses to generate features. [Figure 9] FIG. 9 is a diagram illustrating an example of feature quantities generated by the estimation device. [Figure 10]FIG. 10 is a diagram showing the importance of each feature amount with respect to the carrier axis. [Figure 11] FIG. 11 is a diagram showing the importance of each feature amount with respect to the future business type. [Figure 12] FIG. 12 is a diagram showing the importance of each feature amount related to short-term business types. [Figure 13] FIG. 13 is a diagram for explaining the setting of targets by the estimation device. [Figure 14] FIG. 14 is a flowchart illustrating an example of a procedure in which the estimation device learns a model. [Figure 15] FIG. 15 is a flowchart illustrating an example of a procedure in which the estimation device estimates candidates for a career plan of a target user. [Figure 16] FIG. 16 is a diagram showing a verification result of the estimation performance of the estimation device for the candidates of the career plan of the target user. [Figure 17] FIG. 17 is a diagram illustrating an example of the configuration of a computer that executes an estimation program. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, a mode (embodiment) for carrying out the present invention will be described with reference to the drawings. The present invention is not limited to the embodiment described below.

[0009] First, an outline of the operation of the estimation device of this embodiment will be described with reference to Fig. 1. Here, a case will be described in which the estimation device 10 estimates candidates for a career plan of an employee based on the employee's business goals, performance, etc.

[0010] First, the estimation device 10 acquires the work goals, performance, etc. of the employee and registers them in a personnel DB (database). After that, the estimation device 10 performs preprocessing of the data in the personnel DB. Then, the estimation device 10 uses the preprocessed data in the personnel DB to train a model (a model that outputs an estimation result of a candidate career plan for an employee from the work characteristics of the employee), and estimates a candidate career plan for the employee based on the trained model. Then, the estimation device 10 outputs the estimation result of the candidate career plan to the employee or the employee's manager.

[0011] For example, as shown in Figure 2, candidate career plans include three items appropriate for the target employee: (1) career axis (the employee's career direction), (2) future job type (the job type the employee will be doing at the final stage of their career), and (3) short-term job type (the job type the employee will be doing in the short term in the future, for example, through job rotation).

[0012] The candidate career plans for employees may, for example, show first to third candidates of job types suitable for employees for each of the above items (1) to (3) as shown in Fig. 2. In addition, the candidates may include a value of the reliability of the estimation for each job type.

[0013] The candidate career plans may be arranged for the managers of employees. For example, the candidate career plans for managers may be a compilation of candidate career plans related to the above items (1) to (3) for multiple employees (see Figure 3). The career plans for each employee may be sorted or filtered using the job types shown in (1) to (3) as keys, for example, so that managers can quickly find suitable employees.

[0014] Next, an overview of the data in the personnel DB used by the estimation device 10 and model learning by the estimation device 10 will be described with reference to Fig. 4. The data in the personnel DB includes, for example, information on characteristics of companies and departments, characteristics of employees, career plans of employees, etc., as shown in Fig. 4.

[0015] An employee's career plan includes the above-mentioned "career axis" and "job type." The "career axis" is information that indicates the direction of the employee's career, and broad keywords that indicate the type of job, such as information systems, system infrastructure, information security, sales, general affairs, etc., are set. Note that "intentions (suitability and prospects)" in "employee characteristics" reflects the results of an intention survey on the employee's suitability for their current job type, their prospects, etc.

[0016] The estimation device 10 performs model learning using data of employees who answered "suitable" for the current job type from among the data in the above-mentioned personnel DB.

[0017] For example, the estimation device 10 generates features (job characteristics) from each of the data on "company / department characteristics" and "employee characteristics" in the data of employees who have answered "suitable" for the current job type among the data in the personnel DB. Then, the estimation device 10 uses pairs of the generated features and targets (the above (1) career axis, (2) future job type, and (3) short-term job type) as learning data to train a model.

[0018] For example, the estimation device 10 learns three models suitable for employees: (1) a model for estimating candidates for career axes, (2) a model for estimating candidates for future job types, and (3) a model for estimating candidates for short-term job types.

[0019] For example, as shown in Fig. 5, the estimation device 10 extracts data of employees who have answered "Aptitude" (pairs of features of the aptitude employee and the target) from the data in the above-mentioned personnel DB as learning data. Then, the estimation device 10 uses the learning data to learn the above three models.

[0020] After that, the estimation device 10 creates analysis data (for example, data extracted from the personnel data for this year) and applies the above three models to this analysis data to obtain estimation data suitable for the target employee on (1) career axis, (2) future job type, and (3) short-term job type. This enables the estimation device 10 to estimate candidates for career plans suitable for the employee.

[0021] [Configuration example] Next, a configuration example of the estimation device 10 will be described with reference to Fig. 6. The estimation device 10 includes, for example, an input / output unit 11, a storage unit 12, and a control unit 13, as shown in Fig. 6.

[0022] The input / output unit 11 is an interface for inputting and outputting various information to the estimation device 10. For example, the input / output unit 11 accepts input of personnel data and outputs estimation results of candidates for the career plan of each employee.

[0023] The storage unit 12 stores data for the control unit 13 to execute various processes. The storage unit 12 stores, for example, personnel data. The personnel data includes, for example, data of reference users (for example, personnel data up to the previous year) and data of target users (for example, personnel data for the current year). The storage unit 12 also stores models learned by the control unit 13 (for example, models relating to each of the above-mentioned (1) to (3)).

[0024] The control unit 13 controls the entire estimation device 10. The control unit 13 includes a data preprocessing unit 131, a feature generating unit 132, a model learning unit 133, an acquiring unit 134, an estimation unit 135, and an estimation result output unit 136.

[0025] The data preprocessing unit 131 performs preprocessing of the personnel data. The preprocessing here includes, for example, data combination, data division, data type conversion, name matching, outlier removal, missing value completion, normalization, morphological analysis, feature selection, and the like for the personnel data (see FIG. 7).

[0026] The feature generation unit 132 generates features from the preprocessed personnel data. For example, the feature generation unit 132 generates features (job features) from each piece of data on "company / department characteristics" and "employee characteristics" included in the preprocessed personnel data. For example, the feature generation unit 132 generates features from each piece of data on "company / department characteristics" and "employee characteristics" included in the preprocessed personnel data by one-hot encoding, TF-IDF, lag features, etc., as shown in FIG. 7.

[0027] When generating features, the feature generation unit 132 generates features related to, for example, five characteristics (company characteristics, area characteristics, department characteristics, business characteristics, and employee characteristics) shown in Fig. 8. For example, the feature generation unit 132 generates features for each data item shown in Fig. 9 using one-hot encoding, morphological analysis, and TF-IDF for the above five characteristics.

[0028] For example, the feature generating unit 132 generates features related to work history: company and work history: department by one-hot encoding for "characteristics of company and department." The estimation unit 135 uses such features to estimate the career axis and work related to the work type based on the employee's current affiliation and past transfer history.

[0029] Furthermore, for example, the feature generating unit 132 generates features related to the current workplace area for the "characteristics of the company / department" by one-hot encoding. Since there are area-specific tasks, the estimation unit 135 can use such features to estimate the tasks present in the corresponding area as tasks suitable for the employee.

[0030] Furthermore, for example, the feature generating unit 132 generates features related to the personality and skills of employees by one-hot encoding for the "characteristics of the company / department." The estimation unit 135 can use such features to estimate the characteristics of human resources that can be assigned based on the specialty area of ​​the corresponding company / organization.

[0031] Furthermore, for example, the feature generation unit 132 generates features related to the business goals of the department by morphological analysis and TF-IDF for "company / department characteristics." In other words, the feature generation unit 132 generates features by extracting specific business content through text analysis of the business goals of the department. The estimation unit 135 can use such features to distinguish between unique tasks and common tasks of an organization.

[0032] Furthermore, the feature generation unit 132 generates features related to the employee's work goals and work reviews for "employee characteristics" by morphological analysis and TF-IDF. That is, the feature generation unit 132 extracts the specific work content that the employee is responsible for by analyzing the text of the employee's work goals. Then, the feature generation unit 132 generates features using the extraction results. The estimation unit 135 can estimate the employee's expertise by using such features.

[0033] Furthermore, the feature generation unit 132 generates features related to the work review for "company / department characteristics" by morphological analysis and TF-IDF. That is, the feature generation unit 132 extracts the current work or the work experienced in the past by text analysis of the work review. Then, the feature generation unit 132 generates features using the extraction results. By using such features, the estimation unit 135 can identify what kind of performance the employee has produced in the current work or the work experienced in the past, and estimate the employee's work aptitude.

[0034] Returning to the explanation of FIG. 6, the model learning unit 133 uses the learning data to learn models for each of (1) career axis, (2) future job type, and (3) short-term job type. For example, the model learning unit 133 uses pairs of features generated from each data of "company / department characteristics" and "employee characteristics" for users who have responded that they are suitable for their current job field among the personnel data, and targets ((1) career axis, (2) future job type, (3) short-term job type) as learning data to learn models related to the above (1) to (3).

[0035] In other words, the model learning unit 133 learns three models suitable for an employee based on the employee's work characteristics: (1) a model for estimating possible career axes, (2) a model for estimating possible future work types, and (3) a model for estimating possible short-term work types.

[0036] Here, we will explain the features that were found to be highly important when applying the Random Forest Classifier to the data of reference users.

[0037] For example, as shown in Figure 10, with regard to (1) career axis, the importance of work review (what kind of results did the employee produce in their current work or past work experience), work history: work content, etc. was high. Also, as shown in Figure 11, with regard to (2) future work type, the importance of work history: work content, work review, etc. was high. This can be inferred to be because the current work and past work experience have a strong influence on the career axis and future work type.

[0038] Furthermore, as shown in Figure 12, for (3) short-term task type, the importance of the probability of transfer between departments was high, next to the review of tasks. It can be inferred that the reason why the importance of the probability of transfer between departments was high is because the ease of transfer between departments has an influence on the above short-term task types.

[0039] In learning the above models (1) to (3), model learning unit 133 sets targets as shown in FIG. 13, for example.

[0040] For example, when the model learning unit 133 (1) learns a model related to career axes, the model learning unit 133 learns the model by targeting three years' worth of features of expert-level employees who answered that they have aptitude from the data of reference users and the job type of those employees one year later.

[0041] In addition, when (2) learning a model related to future work types, the model learning unit 133 learns the model by targeting three years' worth of features of expert-level employees who answered that they have aptitude from the data of reference users and the work types of those employees one year from now.

[0042] In addition, when (3) learning a model related to short-term job types, the model learning unit 133 learns the model by targeting three years' worth of features of employees who answered that they have aptitude from the data of reference users and the job types of those employees three years later.

[0043] Returning to the description of Fig. 6, the acquisition unit 134 acquires the characteristics of tasks that the target user, who is the presumed target of a career plan candidate, has been in charge of up to now. For example, the acquisition unit 134 acquires the characteristic amount (task characteristics) of each task that the target user has been in charge of up to now from the data of the target user in the personnel DB.

[0044] The estimation unit 135 estimates a candidate career plan suitable for the target user from the work characteristics of the target user based on the above models related to (1) to (3) learned by the model learning unit 133. For example, the estimation unit 135 estimates a candidate career plan suitable for the target user for the career axis from the work characteristics of the target user (for example, a review of the user's work, the user's work content, etc.) based on the model related to the career axis (1).

[0045] Also, for example, the estimation unit 135 (2) estimates candidates for career plans suitable for the target user for future work types from the work characteristics of the target user (e.g., a review of the user's work, the user's work content, etc.) based on a model regarding future work types.

[0046] Furthermore, for example, the estimation unit 135 (3) estimates career plan candidates suitable for the target user for short-term work types from the work characteristics of the target user (e.g., a review of the user's work, the probability of transfer between departments, etc.) based on a model for short-term work types.

[0047] The estimation result output unit 136 outputs the candidates for career plans suitable for the target user estimated by the estimation unit 135 via the input / output unit 11. For example, as shown in Fig. 2, the estimation result output unit 136 outputs candidates for career plans suitable for the target user for each of the above (1) career axis, (2) future work type, and (3) short-term work type.

[0048] [Example of processing procedure] Next, an example of the processing procedure of the estimation device 10 will be described with reference to Fig. 14 and Fig. 15. First, an example of the processing procedure of model learning will be described with reference to Fig. 14.

[0049] First, the data preprocessing unit 131 of the estimation device 10 performs preprocessing of personnel data (reference user data and target user data) in the personnel DB (S1). Then, the feature generating unit 132 generates features from the data after the preprocessing (S2). After that, the model learning unit 133 uses the features generated in S2 to learn a model related to the above (1) to (3) (S3).

[0050] Next, an example of a processing procedure for estimating candidates for a career plan of a target user will be described with reference to Fig. 15. First, the acquisition unit 134 of the estimation device 10 acquires data of the target user (S11). For example, the acquisition unit 134 acquires task features (feature amounts) for each task from the preprocessed data of the target user.

[0051] After S11, the estimation unit 135 estimates candidates for career plans suitable for the target user based on the data of the target user acquired in S11 and the model related to (1) to (3) learned in S3 of Fig. 14 (S12). For example, the estimation unit 135 estimates candidates for career plans suitable for the target user for each of (1) career axis, (2) future work type, and (3) short-term work type. Thereafter, the estimation result output unit 136 outputs the estimation result from S12 (S13).

[0052] [Verification of estimation accuracy] FIG. 16 shows the verification result of the estimation accuracy of the candidate career plans of the target users by the estimation device 10. Here, the estimation device 10 constructed a model for each of the above (1) career axis, (2) future job type, and (3) short-term job type using a Random Forest Classifier. Then, the estimation device 10 estimated the candidate career plans of each employee in 2020 based on the personnel data up to 2019 using each of the constructed models (1) to (3).

[0053] The accuracy rate, precision rate, and recall rate of the estimation results of (1) career axis, (2) future job type, and (3) short-term job type by the estimation device 10 were shown in Fig. 16. From this, it was confirmed that the estimation device 10 can accurately estimate candidates for career plans suitable for the target user.

[0054] [System configuration, etc.] In addition, each component of each unit shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or a part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc. Furthermore, each processing function performed by each device can be realized in whole or in part by a CPU and a program executed by the CPU, or can be realized as hardware using wired logic.

[0055] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified.

[0056] [program] The above-mentioned estimation device 10 can be implemented by installing a program (estimation program) as package software or online software on a desired computer. For example, by executing the above-mentioned program on an information processing device, the information processing device can function as the estimation device 10. The information processing device referred to here includes mobile communication terminals such as smartphones, mobile phones, and PHS (Personal Handyphone System), as well as terminals such as PDAs (Personal Digital Assistants).

[0057] 17 is a diagram showing an example of a computer that executes an estimation program. The computer 1000 includes, for example, a memory 1010 and a CPU 1020. The computer 1000 also includes a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0058] The memory 1010 includes a read only memory (ROM) 1011 and a random access memory (RAM) 1012. The ROM 1011 stores a boot program such as a basic input output system (BIOS). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0059] The hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program that defines each process executed by the above-mentioned estimation device 10 is implemented as a program module 1093 in which a computer-executable code is written. The program module 1093 is stored, for example, in the hard disk drive 1090. For example, a program module 1093 for executing a process similar to the functional configuration of the estimation device 10 is stored in the hard disk drive 1090. The hard disk drive 1090 may be replaced with an SSD (Solid State Drive).

[0060] Data used in the processing of the above-described embodiment is stored as program data 1094 in, for example, the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads out the program module 1093 or the program data 1094 stored in the memory 1010 or the hard disk drive 1090 into the RAM 1012 as necessary and executes them.

[0061] Note that the program module 1093 and the program data 1094 are not limited to being stored in the hard disk drive 1090, but may be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and the program data 1094 may be stored in another computer connected via a network (such as a local area network (LAN) or wide area network (WAN)). The program module 1093 and the program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070. [Explanation of symbols]

[0062] 10 Estimation device 11 Input / output section 12 Storage section 13 Control section 131 Data Preprocessing Unit 132 Feature Generation Unit 133 Model Learning Department 134 Acquisition Department 135 Estimation Department 136 Estimation result output section

Claims

1. An estimation device that estimates candidates for a career plan of a target user, An acquisition unit that acquires task characteristics for each task that the target user has been in charge of up to now; an estimation unit that receives the business characteristics of the target user as an input and obtains, as an output, a candidate career plan suitable for the target user based on a model that is trained using the business characteristics of each reference user who answered that they are suitable for their current business field, and outputs a candidate career plan suitable for the target user based on the model that outputs a candidate career plan suitable for the user; an estimation result output unit that outputs an estimation result of candidates of a career plan suitable for the estimated target user; An estimation device comprising:

2. The candidates for the career plan are: A potential business field that will be the core of the target user's future career, The business characteristics are: At least includes a review of the user's work and the content of the user's work 2. The estimation device according to claim 1 .

3. The candidates for the career plan are: A business type of the target user in the long term, The business characteristics are: A review of the user's work and at least the content of the user's work 2. The estimation device according to claim 1 .

4. The candidates for the career plan are: A business type of the target user in the short term, The business characteristics are: Includes review of user work and probability of transfer between departments 2. The estimation device according to claim 1 .

5. An estimation method executed by an estimation device, comprising: A step of acquiring work characteristics for each work currently performed by a target user for estimating a career plan candidate; A process of inputting the work characteristics of the target user and obtaining, as an output, a career plan candidate suitable for the target user based on a model that is trained using the work characteristics of each reference user who answered that they are suitable for their current work field, and that outputs a career plan candidate suitable for the target user; outputting an estimated result of a candidate career plan suitable for the target user; The estimation method according to claim 1,

6. A step of acquiring work characteristics for each work currently performed by a target user for estimating a career plan candidate; A process of inputting the work characteristics of the target user and obtaining, as an output, a career plan candidate suitable for the target user based on a model that is trained using the work characteristics of each reference user who answered that they are suitable for their current work field, and that outputs a career plan candidate suitable for the target user; outputting an estimated result of a candidate career plan suitable for the target user; An estimation program characterized by being executed by a computer.

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