Personnel post recommendation method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of artificial intelligence and data processing, and relates to a personnel post recommendation method, which comprises the following steps: acquiring personnel information of a to-be-matched personnel and post information of a to-be-matched post; performing tagging processing on the personnel information and the post information based on a preset tag system to determine a personnel portrait of a to-be-matched personnel and a post portrait of a to-be-matched post; comparing the personnel portrait with the post portrait based on a preset post matching model to obtain the matching degree of the to-be-matched personnel and the to-be-matched post; and determining a recommendation result according to the obtained matching degree. The invention further provides a personnel post recommendation device, computer equipment and a storage medium. According to the method, the personnel portrait and the post portrait are established, and the personnel portrait and the post portrait are matched based on the post matching model, so that the personnel post matching accuracy is improved, and the personnel or post recommendation is realized according to the matching degree, and the personnel selection efficiency is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and data processing technology, and in particular to a personnel position recommendation method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of information technology, especially the widespread application of cutting-edge technologies such as artificial intelligence, big data, and cloud computing, the financial industry is undergoing unprecedented changes, and the traditional financial industry is gradually transforming towards the deep integration of financial technology. This transformation not only reshapes the way financial services are provided, but also puts higher requirements on the internal operation and management model of financial institutions, especially in the selection and management of organizational personnel.

[0003] Human resource management in fintech companies and traditional financial institutions faces increasingly complex and diversified challenges. On the one hand, companies need to respond quickly to market changes and efficiently complete talent introduction and deployment to support business innovation and expansion; on the other hand, with the continuous optimization and adjustment of organizational structure, how to ensure that personnel capabilities are highly consistent with job requirements has become the key to improving organizational effectiveness and competitiveness.

[0004] Traditionally, the selection and management of organizational personnel mainly relies on the experience and judgment of human resource managers. Although this method has certain practicality, it has obvious limitations. The existing person-job matching system based on computer algorithms is introduced to deal with complex and changing job requirements and personalized personnel characteristics. It lacks sufficient flexibility and pertinence, resulting in unsatisfactory recommendation results, which affects the accuracy and efficiency of personnel selection. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a personnel position recommendation method, device, computer equipment and storage medium to solve the problem of insufficient efficiency and accuracy in personnel position matching.

[0006] In order to solve the above technical problems, the present application embodiment provides a personnel position recommendation method, which adopts the following method:

[0007] Obtaining personnel information of personnel to be matched and position information of positions to be matched;

[0008] Labeling the personnel information and the position information based on a preset labeling system to determine the personnel profile of the personnel to be matched and the position profile of the position to be matched;

[0009] Compare the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched;

[0010] The recommendation result is determined based on the obtained matching degree.

[0011] Furthermore, the step of determining the recommendation result according to the obtained matching degree specifically includes:

[0012] When the matching degree meets the first set condition, a first recommendation level is output; the first recommendation result is used to indicate direct recommendation of the job adaptation;

[0013] When the matching degree does not meet the first set condition and does not meet the second set condition, a second recommendation level is output; the second recommendation result is used to indicate that the position is recommended for adaptation after review;

[0014] When the matching degree meets the second set condition, a third recommendation result is output; the third recommendation result is used to indicate that the position adaptation is not recommended.

[0015] Furthermore, the step of comparing the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched specifically includes:

[0016] In response to a matching request for a target position, calling the position matching model;

[0017] The job matching model is used to compare the job profile of the target job with the personnel profile of the target personnel to obtain the matching degree between the target job and each target personnel.

[0018] Furthermore, the step of comparing the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched specifically includes:

[0019] In response to a target personnel matching request, calling the position matching model;

[0020] The position matching model is used to compare the personnel profile of the target person with the position profiles of each target position to obtain the matching degree between the target person and each target position.

[0021] Furthermore, the step of labeling the personnel information and the position information based on a preset labeling system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched specifically includes:

[0022] Identifying the personnel information;

[0023] The identified personnel information is mapped into a personnel portrait with multiple dimensional labels according to a preset label system; wherein the dimensional label of each personnel portrait includes at least one factor.

[0024] Furthermore, the step of labeling the personnel information and the position information based on a preset labeling system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched specifically includes:

[0025] Identifying the position information;

[0026] The identified job information is mapped into a job portrait with multiple dimensional labels according to a preset label system; wherein the dimensional label of each job portrait includes at least one factor.

[0027] Furthermore, the optimization method of the preset job matching model specifically includes:

[0028] Obtaining optimization data; the optimization data includes actual job profiles corresponding to actual personnel profiles;

[0029] Compare the optimization data with the matching degree output by the preset job matching model, and calculate the loss;

[0030] The preset job matching model is optimized according to the calculated loss to obtain an optimized job matching model.

[0031] In order to solve the above technical problems, the embodiment of the present application also provides a personnel position recommendation device, which adopts the following technical solution:

[0032] The acquisition module is used to obtain the personnel information of the personnel to be matched and the position information of the positions to be matched;

[0033] A processing module, used for labeling the personnel information and the position information based on a preset label system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched;

[0034] A comparison module, used to compare the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched;

[0035] The output module is used to determine the recommendation results according to the obtained matching degree.

[0036] Furthermore, the output module includes:

[0037] A first output submodule is used to output a first recommendation level when the matching degree meets a first set condition; the first recommendation result is used to indicate that the job is directly recommended for adaptation;

[0038] The second output submodule is used to output a second recommendation level when the matching degree does not meet the first set condition; the second recommendation result is used to indicate that the position is recommended for adaptation after review;

[0039] The third output submodule outputs a third recommendation result when the matching degree meets the second set condition; the third recommendation result is used to indicate that the position adaptation is not recommended.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0041] It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the personnel position recommendation method as described above when executing the computer-readable instructions.

[0042] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0043] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the personnel position recommendation method described above are implemented.

[0044] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by establishing personnel portraits and job portraits, and matching personnel portraits with job portraits based on a job matching model, the accuracy of personnel-job matching is improved, and the efficiency of personnel selection is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is an exemplary system architecture diagram of an application scenario of the present application;

[0047] Figure 2 A flow chart of an embodiment of a personnel position recommendation method according to the present application;

[0048] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S400;

[0049] Figure 4 is a structural schematic diagram of an embodiment of a personnel position recommendation device according to the present application;

[0050] Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0052] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0053] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0054] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0055] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0056] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0057] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0058] It should be noted that the personnel position recommendation method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the personnel position recommendation device is generally set in the server / terminal device.

[0059] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0060] Continue to refer Figure 2 , shows a flow chart of an embodiment of a personnel position recommendation method according to the present application. The personnel position recommendation method comprises the following steps:

[0061] Step S100, obtaining the personnel information of the personnel to be matched and the position information of the positions to be matched.

[0062] Specifically, personnel can be personnel in an organization who need to be matched with positions, and personnel information includes but is not limited to name, education, working time, performance, professional ability, etc. Position information can be based on position information of different institutions within the organization, including but not limited to the city where the institution is located, performance requirements, business conditions, position level, etc.

[0063] Step S200, labeling the personnel information and the position information based on a preset labeling system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched.

[0064] In one implementation of this embodiment, the following steps may be specifically included:

[0065] Identify the personnel information and position information;

[0066] According to a preset label system, the identified personnel information and position information are mapped into personnel portraits and position portraits with multiple dimensional labels; wherein the dimensional label of each portrait includes at least one factor.

[0067] That is, the label system can include label information of one or more dimensions, that is, both personnel portraits and job portraits are constructed through the label system as a combination of labels of one or more dimensions. Furthermore, each label dimension can include label factors of one or more levels, and the label factors of each level can also include one or more specific indicator parameters.

[0068] In one implementation of this embodiment, the personnel portrait includes 7 label dimensions: qualification examination, interview results, basic information, performance, ability, personnel level and personnel source. Specifically, the qualification examination dimension includes the key customer factor; that is, personnel for important customer channels need to pass the qualification examination. The interview result dimension includes the institutional interview factor and the headquarters interview factor. The basic information dimension includes the educational background factor, the length of time on the job factor, and the competitive situation factor; among them, the educational background factor may include a doctorate, a master's degree, a bachelor's degree, etc.; the competitive situation factor may include public and non-public. The performance situation dimension includes the performance factor for 2 consecutive years, the KPI factor for 2 consecutive years, etc.; the performance for 2 consecutive years and the 2-year KPI are mainly set according to the specific position or channel of the personnel, for example: the proportion of premiums of key customer business under the key customer channel for 2 consecutive years, and the corresponding indicators must reach 10%; or the performance under the key customer channel for 2 consecutive years needs to reach 5 million, etc. The capability dimension includes management capability factor, overall planning capability factor, professional capability factor, etc. Among them, overall planning capability factor and management capability factor are mainly reflected in whether or not there is experience in team management or project coordination; professional capability factor mainly involves different channels, including traditional important customers, international important customers, bank insurance, group funds, agricultural channels, and government insurance. The personnel level dimension includes VP level (director level) factor, M level (manager level) factor, etc. Among them, VP level factor includes channel director and sales director. The personnel source dimension includes general external recruitment factor, external recruitment return factor within 3 years, transfer factor from other subsidiaries, and internal transfer factor.

[0069] In one implementation of this embodiment, the job portrait includes four label dimensions: the city type of the institution, the bottom line requirements of the institution, the operating conditions of the institution, and the type of position. Specifically, the city type dimension of the institution includes one type of factor, two type of factor, and three type of factor; among them, the one type of factor includes Beijing, Shanghai, Guangzhou, and Shenzhen; the two type of factors include Chengdu, Hefei, Nanjing, Wuhan, Changsha, Chongqing, Jinan, Suzhou, Foshan, Xi'an, Tianjin, etc.; the three type of factors include other cities other than the above one and two types of factors. The bottom line requirement dimension of the institution includes the rolling one-year premium factor, the scale factor of the unified group gold business, the premium proportion factor of the important customer business, the number of important customers named by the department service factor, the scale factor of the unified government and agriculture business, the scale factor of the brokerage sales, and the FAC sales scale factor. The operating conditions dimension of the institution includes the premium planning factor, the quality factor, and the proportion factor of the number of positions. The position type dimension includes the director factor, the deputy director factor, and the team manager factor.

[0070] Understandably, the labels of the job portraits correspond to the labels of the personnel portraits. In this embodiment, for example, the basic information and performance of the personnel portraits need to meet the bottom line requirements of the corresponding job portraits. The personnel level in the personnel portraits corresponds to the job type in the job portraits. For example, if the personnel level is VP level, the corresponding director level in the job portraits means that the personnel level should be lower than the level requirements of the job. The job requirements of different organizations are associated with the city type and job type of the organization. The bottom line requirements of the job institutions in the first-class cities are higher than those in the second-class cities and the third-class cities.

[0071] In some other implementations, the label dimensions of the personnel portrait and the job portrait and the label factors corresponding to each dimension can be adjusted according to actual organizational or job requirements.

[0072] The above implementation methods are mainly aimed at the labeling system set up in the financial field, especially the insurance industry, to conduct more refined information management of personnel and organizational positions, and output more targeted and personalized matching solutions to improve selection efficiency and accuracy.

[0073] Step S300, comparing the personnel portrait with the position portrait based on a preset position matching model to obtain a matching degree between the personnel to be matched and the position to be matched.

[0074] In one implementation of this embodiment, the job matching model may be a data model. Specifically, the job matching model determines the degree of matching between personnel and positions by comparing the labels in the personnel portrait and the position portrait one by one. For example, when the labels in the personnel portrait correspond to the labels in the position portrait, it is considered a complete match, and when they partially correspond, it is considered a partial match.

[0075] In one implementation of this embodiment, the job matching model may be an artificial intelligence model. The artificial intelligence model may be trained through a data set, and further matching of the personnel portrait and the job portrait may be achieved by calculating the label similarity between the personnel portrait and the job portrait. The data set may be divided into a training set and a test set, and the job matching model may be trained through the training set. It should be noted that the training samples in the data set are the information of actual personnel in the organization and the corresponding job information.

[0076] Step S400: determining a recommendation result according to the obtained matching degree.

[0077] According to the matching degree between positions and personnel or according to the matching degree between personnel and positions, the corresponding recommendation results of personnel or positions are output, and the results are fed back to the management system.

[0078] In some optional implementations of this embodiment, step S400 specifically includes the following steps:

[0079] Step S410: When the matching degree meets the first set condition, a first recommendation level is output; the first recommendation result is used to indicate that the job is directly recommended for adaptation;

[0080] When the labels in the personnel portrait and the labels in the job portrait all correspond or the matching degree reaches 100%, that is, when they are completely matched, the output directly recommends the job or person.

[0081] Step S420: When the matching degree does not meet the first set condition, a second recommendation level is output; the second recommendation result is used to indicate that the position is recommended for adaptation after review;

[0082] When one of the labels in the personnel portrait does not correspond to the label in the position portrait or the matching degree does not reach 100% and is above a certain proportion, such as 90%, 80%, etc., that is, it is not a complete match, then the recommendation result that the position or person needs to wait for further review and accommodation will be output.

[0083] Step S430: When the matching degree satisfies the second set condition, a third recommendation result is output; the third recommendation result is used to indicate that the position adaptation is not recommended.

[0084] When there are multiple mismatches between the labels in the personnel portrait and the labels in the job portrait or the matching degree does not exceed a certain proportion, such as 50%, 60%, etc., the output is a recommendation result that does not recommend the job or personnel.

[0085] The method involved in the above steps S410 to S430 can meet the matching requirements of personnel and positions in some special cases by determining the degree of recommendation based on the degree of matching between personnel and positions, making the selection of personnel and positions more humane.

[0086] This application improves the accuracy of personnel-position matching and effectively improves the efficiency of personnel selection by establishing personnel portraits and position portraits and matching the personnel portraits with the position portraits based on a position matching model.

[0087] In some optional implementations of this embodiment, step S300 specifically includes the following steps:

[0088] In response to a matching request for a target position, the position matching model is called.

[0089] The job matching model is used to compare the job profile of the target job with the personnel profile of the target personnel to obtain the matching degree between the target job and each target personnel.

[0090] In the personnel appointment process, job matching is carried out for personnel who need to be appointed. Targeted job matching can be carried out according to actual personnel appointment needs, making the matching process more humane.

[0091] In some optional implementations of this embodiment, step S300 specifically includes the following steps:

[0092] In response to a target person matching request, the position matching model is called.

[0093] The position matching model is used to compare the personnel profile of the target person with the position profiles of each target position to obtain the matching degree between the target person and each target position.

[0094] In the process of organizational management (new establishment, splitting, merger and withdrawal), personnel matching is carried out for vacant positions. Targeted matching of personnel can be carried out according to the actual needs of the positions, making the matching process more humane.

[0095] In some optional implementations of this embodiment, in order to further improve the matching accuracy of the job matching model, the job matching model may be optimized. The optimization method of the preset job matching model specifically includes the following steps:

[0096] Obtaining optimization data; the optimization data includes actual job profiles corresponding to actual personnel profiles;

[0097] Comparing the optimization data with the matching degree output by the preset job matching model, and calculating the loss;

[0098] The preset job matching model is optimized according to the calculated loss to obtain an optimized job matching model.

[0099] Specifically, machine learning or deep learning algorithms, such as logistic regression, random forest, neural network, etc., can be used to build a preset job matching model. The model input is the feature vector of the personnel portrait and the job portrait, and the output is the degree of matching (such as matching value or score ratio). The optimized data (i.e., the associated data of the actual personnel portrait and the corresponding job portrait) is further input into the preset job matching model. The model outputs the degree of matching between each employee and the position, and compares it with the actual matching situation. Loss functions, such as mean square error (MSE), cross entropy loss, etc., are calculated to quantify the difference between the model prediction and the actual result. According to the type of model and the nature of the loss function, a suitable optimization algorithm is selected, such as gradient descent, stochastic gradient descent, Adam, etc. The parameters of the model are iteratively updated using the optimization algorithm to minimize the loss function. In each iteration, the gradient is calculated and the model parameters are updated until the predetermined stop condition is reached (such as loss convergence, the number of iterations reaches the upper limit, etc.).

[0100] By optimizing the model to improve the accuracy of the model output, the efficiency and quality of matching people and positions can be improved.

[0101] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0102] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0103] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0104] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0105] Further references Figure 4 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a personnel position recommendation device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0106] like Figure 4 As shown, the personnel position recommendation device 400 described in this embodiment includes: an acquisition module 401, a processing module 402, a comparison module 403, and an output module 404. Among them:

[0107] The acquisition module 401 is used to acquire the personnel information of the personnel to be matched and the position information of the positions to be matched;

[0108] The processing module 402 is used to label the personnel information and the position information based on a preset label system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched;

[0109] A comparison module 403 is used to compare the personnel portrait with the position portrait based on a preset position matching model to obtain a matching degree between the personnel to be matched and the position to be matched;

[0110] The output module 404 is used to determine the recommendation result according to the obtained matching degree.

[0111] By establishing personnel portraits and job portraits and matching them based on the job matching model, the accuracy of personnel-job matching is improved and the efficiency of personnel selection is effectively improved.

[0112] In some optional implementations of the present embodiment, the output module 404 includes a first output submodule, a second output submodule and a third output submodule; wherein the first output submodule is used to output a first recommendation level when the matching degree meets the first set condition; the first recommendation result is used to indicate that the position is directly recommended for adaptation; the second output submodule is used to output a second recommendation level when the matching degree does not meet the first set condition; the second recommendation result is used to indicate that the position is recommended for adaptation after review; the third output submodule is used to output a third recommendation result when the matching degree meets the second set condition; the third recommendation result is used to indicate that the position is not recommended for adaptation.

[0113] In some optional implementations of this embodiment, the comparison module 403 includes a first calling unit and a first comparing unit; wherein the first calling unit is used to call the position matching model in response to a matching request for the target position; the first comparing unit is used to compare the position profile of the target position with the personnel profile of the target personnel through the position matching model to obtain the degree of matching between the target position and each target personnel.

[0114] In some optional implementations of this embodiment, the comparison module 403 includes a second calling unit and a second comparing unit; wherein the second calling unit is used to call the position matching model in response to a target personnel matching request; the second comparing unit is used to compare the personnel portrait of the target personnel with the position portraits of each target position through the position matching model to obtain the degree of matching between the target personnel and each target position.

[0115] To solve the above technical problems, the present application also provides a computer device. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0116] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 6 with a memory 61, a processor 62, and a network interface 63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0117] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0118] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D personnel position recommendation memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the personnel position recommendation method, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0119] The processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run computer-readable instructions stored in the memory 61 or process data, such as computer-readable instructions for running the personnel position recommendation method.

[0120] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0121] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the personnel position recommendation method as described above.

[0122] This application improves the accuracy of personnel position matching by establishing personnel portraits and position portraits, and matches personnel portraits with position portraits based on the position matching model, and effectively improves the efficiency of personnel selection by recommending personnel or positions according to the degree of matching; and, mainly for the label system constructed in the financial field, especially the insurance industry, more refined information management of personnel and organizational positions is carried out, and more targeted and personalized matching schemes are output to improve the efficiency and accuracy of selection. Further, by determining the degree of recommendation based on the degree of matching between people and positions, the matching needs of personnel positions can be met in some special cases, making the selection of personnel positions more humane. In addition, in the personnel appointment process, personnel or position matching is carried out for personnel who need to be appointed or positions that need to be selected, and targeted matching of positions or personnel can be carried out according to the actual needs of personnel or positions, making the matching process more humane. Further, for the label system set in the financial field, especially the insurance industry, more refined information management is carried out for personnel and organizational positions, and more targeted and personalized matching schemes are output to improve the efficiency and accuracy of selection.

[0123] The non-company software tools or components that appear in the embodiments of the present application are merely examples and do not represent actual use. Through the description of the above implementation modes, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware, but in many cases the former is a better implementation mode. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, a disk, an optical disk), including when dry instructions are used to enable a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0124] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A personnel position recommendation method, characterized in that: The steps include: Obtaining personnel information of personnel to be matched and position information of positions to be matched; Labeling the personnel information and the position information based on a preset labeling system to determine the personnel profile of the personnel to be matched and the position profile of the position to be matched; Compare the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched; The recommendation result is determined based on the obtained matching degree.

2. The personnel position recommendation method according to claim 1, characterized in that: The step of determining the recommendation result according to the obtained matching degree specifically includes: When the matching degree meets the first set condition, a first recommendation level is output; the first recommendation result is used to indicate direct recommendation of the position; When the matching degree does not satisfy the first set condition and does not satisfy the second set condition, a second recommendation level is output; the second recommendation result is used to indicate that the position is recommended after review; When the matching degree satisfies the second set condition, a third recommendation result is output; the third recommendation result is used to indicate that the position is not recommended.

3. The personnel position recommendation method according to claim 1 or 2, characterized in that: The step of comparing the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched specifically includes: In response to a matching request for a target position, calling the position matching model; The job matching model is used to compare the job profile of the target job with the personnel profile of the target personnel to obtain the matching degree between the target job and each target personnel.

4. The personnel position recommendation method according to claim 1 or 2, characterized in that: The step of comparing the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched specifically includes: In response to a target personnel matching request, calling the position matching model; The position matching model is used to compare the personnel profile of the target person with the position profiles of each target position to obtain the matching degree between the target person and each target position.

5. The personnel position recommendation method according to claim 1, characterized in that: The step of labeling the personnel information and the position information based on a preset labeling system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched specifically includes: Identifying the personnel information; The identified personnel information is mapped into a personnel portrait with multiple dimensional labels according to a preset label system; wherein the dimensional label of each personnel portrait includes at least one factor.

6. The personnel position recommendation method according to any one of claims 1 or 5, characterized in that: The step of labeling the personnel information and the position information based on a preset labeling system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched specifically includes: Identifying the position information; The identified job information is mapped into a job portrait with multiple dimensional labels according to a preset label system; wherein the dimensional label of each job portrait includes at least one factor.

7. The personnel position recommendation method according to claim 1, characterized in that: The optimization method of the preset job matching model specifically includes: Obtaining optimization data; the optimization data includes actual job profiles corresponding to actual personnel profiles; Comparing the optimization data with the matching degree output by the preset job matching model, and calculating the loss; The preset job matching model is optimized according to the calculated loss to obtain an optimized job matching model.

8. A personnel position recommendation device, characterized in that: include: The acquisition module is used to obtain the personnel information of the personnel to be matched and the position information of the positions to be matched; A processing module, used for labeling the personnel information and the position information based on a preset label system to determine the personnel portrait of the personnel to be matched and the position portrait of the position to be matched; A comparison module, used to compare the personnel portrait with the position portrait based on a preset position matching model to obtain the matching degree between the personnel to be matched and the position to be matched; The output module is used to determine the recommendation results according to the obtained matching degree.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the personnel position recommendation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the personnel position recommendation method according to any one of claims 1 to 7 are implemented.

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