Employment matching and occupational development planning system

Abstract

The invention belongs to the technical field of employment market management, and provides an employment matching and occupational development planning system, which comprises a user information acquisition module, a post information analysis module, a matching algorithm module and an occupational planning suggestion module, the user information acquisition module, the post information analysis module, the matching algorithm module and the occupational planning suggestion module are electrically connected in sequence; according to the invention, the user information acquisition module is constructed to comprehensively acquire user information, the post information analysis module is combined to analyze post requirements in real time, and the advanced matching algorithm module is adopted to realize accurate post matching; a machine learning algorithm is introduced through the occupational planning suggestion module, so that a personalized occupational development path and a training improvement scheme are provided for the user; according to the design, the matching accuracy and the user satisfaction degree are remarkably improved, meanwhile, the user is helped to achieve precise planning of occupational development, and the user experience is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of employment market management, and specifically relates to an employment matching and career development planning system. Background Art

[0002] In the current employment market, job seekers and employers often face the problem of information asymmetry. Job seekers have difficulty in accurately finding positions that match their abilities and career plans, while employers also have difficulty in screening out the most suitable candidates from a large number of job seekers. Most traditional employment matching methods rely on simple matching by recruitment websites or human resources agencies. Such methods often only screen based on keywords or simple conditions and cannot comprehensively and accurately evaluate the matching degree between the abilities of job seekers and the positions. In addition, for job seekers, formulating a career development plan that suits their own situation and market demand is also a very challenging task. As a result, most existing career planning services lack personalization and accuracy and are difficult to provide effective career development path suggestions and training improvement plans according to the specific situation of job seekers.

[0003] For this reason, technical personnel in this field have proposed an employment matching and career development planning system to solve the problems raised in the background art. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides an employment matching and career development planning system to solve the problems in the prior art that most career planning services lack personalization and accuracy and are difficult to provide effective career development path suggestions and training improvement plans according to the specific situation of job seekers.

[0005] An employment matching and career development planning system includes:

[0006] A user information acquisition module for acquiring the personal basic information, educational background, skill level, work experience, and career planning goals of a user;

[0007] A job information analysis module for collecting and analyzing the duty requirements, required skills, salary and benefits, and career development paths of each position;

[0008] A matching algorithm module that, according to the data of the user information acquisition module and the job information analysis module, uses a preset matching algorithm to match the user with the most suitable position;

[0009] A career planning advice module that, according to the career planning goals of the user and the matching results, provides personalized career development path suggestions and training improvement plans for the user.

[0010] Preferably, the user information acquisition module further includes a user interest preference acquisition unit for acquiring the user's career interest preferences to perform more accurate job matching.

[0011] Preferably, the job information analysis module further includes a job dynamic update unit for real-time updating of job information to ensure the timeliness of the matching results. The job dynamic update unit uses the ARIMA model to predict the future trends of job information. Through the ARIMA model, the system can predict the future trends of job information, thereby adjusting the matching strategy in advance to improve the timeliness and accuracy of the matching results.

[0012] Preferably, the matching algorithm module adopts the collaborative filtering algorithm, which recommends jobs by analyzing the user's historical behavior and the behavior of similar users to improve the accuracy and efficiency of the matching.

[0013] Preferably, in the career planning advice module, a machine learning algorithm is introduced to provide personalized career development path advice for users based on their career planning goals and matching results.

[0014] Preferably, the career planning advice module further includes a user feedback unit for collecting user feedback on career planning advice to continuously optimize the accuracy and practicality of the advice. The user feedback unit uses the online gradient descent algorithm to update the parameters of the career planning advice model.

[0015] A processor configured to execute the employment matching and career development planning system according to the above.

[0016] A computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements the employment matching and career development planning system according to the above.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. By constructing the user information acquisition module, the present invention can comprehensively and accurately acquire the user's personal basic information, educational background, skill level, work experience, and career planning goals, providing a solid foundation for subsequent matching and planning.

[0019] 2. Through the job information analysis module, the present invention can collect and analyze in real time the job responsibilities, required skills, salary and benefits, and career development paths of each job, ensuring the accuracy and timeliness of job information and improving the accuracy and efficiency of the matching.

[0020] 3. The present invention adopts an advanced matching algorithm module to match the user with the most suitable job according to the user information and job information, significantly improving the accuracy of the matching and user satisfaction.

[0021] 4. Through the career planning advice module, the present invention introduces machine learning algorithms and provides users with personalized career development path advice and training improvement plans based on the users' career planning goals and matching results, helping users achieve precise career development planning.

[0022] 5. The career planning advice module of the present invention further includes a user feedback unit, which can collect users' feedback on career planning advice to continuously optimize the accuracy and practicality of the advice and further improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a framework diagram of the employment matching and career development planning system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following further describes the embodiments of the present invention in detail in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0025] Embodiment: The present invention provides an employment matching and career development planning system, as Figure 1 shown, including a user information acquisition module, a job information analysis module, a matching algorithm module, and a career planning advice module. The user information acquisition module, the job information analysis module, the matching algorithm module, and the career planning advice module are electrically connected in sequence:

[0026] The user information acquisition module is used to acquire the user's personal basic information, educational background, skill level, work experience, and career planning goals;

[0027] The job information analysis module is used to collect and analyze the duty requirements, required skills, salary treatment, and career development paths of each job;

[0028] The matching algorithm module, based on the data of the user information acquisition module and the job information analysis module, uses a preset matching algorithm to match the user with the most suitable job;

[0029] The career planning advice module provides users with personalized career development path advice and training improvement plans according to the users' career planning goals and matching results.

[0030] As can be seen from the above, the system comprehensively acquires user information by constructing a user information acquisition module, analyzes job requirements in real time in combination with the job information analysis module, realizes precise job matching by using an advanced matching algorithm module, and introduces machine learning algorithms through the career planning advice module to provide users with personalized career development paths and training improvement plans. This design significantly improves the accuracy of matching and user satisfaction, while helping users achieve precise career development planning and optimizing the user experience.

[0031] Furthermore, the user information acquisition module further includes a user interest preference acquisition unit for acquiring the user's career interest preferences to perform more accurate job matching.

[0032] As can be seen from the above, adding a user interest preference acquisition unit to the user information acquisition module can comprehensively understand the user's career tendencies and preferences. By acquiring the user's career interest preferences, the system can consider more dimensions when performing job matching, thereby recommending jobs that better meet the user's interests and expectations. This can not only improve the accuracy of matching but also increase the user's satisfaction with the matching results, further enhancing the effectiveness of the entire employment matching process and the user experience.

[0033] Furthermore, the job information analysis module further includes a job dynamic update unit for real-time updating of job information to ensure the timeliness of matching results. The job dynamic update unit uses the ARIMA model to predict the future trend of job information. Through the ARIMA model, the system can predict the future trend of job information, thereby adjusting the matching strategy in advance to improve the timeliness and accuracy of matching results. The formula of the ARIMA model includes:

[0034] φ(B)(1 - B) d Y t = θ(B)ε t ;

[0035] where φ(B) and θ(B) are polynomials of the autoregressive and moving average parts respectively; B is the lag operator; d is the order of differencing; Y t is the job information value at time t (including demand, salary, etc.); ε t is the white noise error term.

[0036] As can be seen from the above, introducing a job dynamic update unit in the job information analysis module and using the ARIMA model to predict the future trend of job information ensure the timeliness and accuracy of job information. By real-time updating job information and combining the prediction ability of the ARIMA model, the system can anticipate changes in job demands in advance and adjust the matching strategy in a timely manner, thereby providing users with more timely and accurate job matching services. This not only improves the efficiency of matching but also enhances the effectiveness and reliability of matching results.

[0037] Furthermore, the matching algorithm module uses the collaborative filtering algorithm. This algorithm recommends jobs by analyzing the user's historical behavior and the behavior of similar users to improve the accuracy and efficiency of matching. The formula of the collaborative filtering algorithm includes:

[0038] P(u,i) = Σ v∈N(u) wuv ·r vi ;

[0039] Among them, P(u, i) represents the predicted score of user u for position i; N(u) represents the set of users similar to user u; w uv represents the similarity weight between user u and user v; r vi represents the actual score of user v for position i.

[0040] As can be seen from the above, the collaborative filtering algorithm is adopted in the matching algorithm module, and positions are recommended by analyzing the historical behaviors of users and the behaviors of similar users, significantly improving the accuracy and efficiency of matching. The collaborative filtering algorithm can deeply mine the potential associations in user behavior data, recommend positions that match the interests and behaviors of users, thereby improving the satisfaction of users with the matching results. At the same time, this algorithm can also make recommendations based on the behaviors of similar users, broadening the scope of recommendations, increasing the opportunities for users to discover potential suitable positions, and further enhancing the effectiveness and practicality of the entire matching process.

[0041] Furthermore, in the career planning advice module, a machine learning algorithm is introduced to provide personalized career development path advice for users according to their career planning goals and matching results. The formula of the machine learning algorithm includes:

[0042]

[0043] Among them, V(s) is the value function of state s, P(s′∣s, a) is the state transition probability, R(s, a, s′) is the reward function, and γ is the discount factor.

[0044] As can be seen from the above, introducing a machine learning algorithm in the career planning advice module can provide more personalized career development path advice for users according to their career planning goals and matching results. It enables the system to intelligently analyze the career needs and goals of users, combine the results of position matching, and customize career development paths and training improvement plans for users. Through the continuous optimization and learning of the machine learning algorithm, the system can continuously improve the accuracy and practicality of the advice, help users better achieve accurate career development planning, and enhance personal career competitiveness.

[0045] Furthermore, the career planning advice module also includes a user feedback unit for collecting user feedback on career planning advice to continuously optimize the accuracy and practicality of the advice. The user feedback unit adopts the online gradient descent algorithm to update the parameters of the career planning advice model. The formula of the online gradient descent algorithm includes:

[0046]

[0047] where θ t is the model parameter at time t; η is the learning rate; is the gradient of the loss function L with respect to the parameter θ; x t and y t are the user feedback features and the corresponding labels at time t, respectively.

[0048] As can be seen from the above, adding a user feedback unit to the career planning advice module and using the online gradient descent algorithm to update the parameters of the career planning advice model greatly improves the accuracy and practicality of the advice. By collecting user feedback on career planning advice, the system can timely understand the needs and expectations of users, and use the online gradient descent algorithm to quickly adjust the model parameters, thereby continuously optimizing the quality of the advice. This continuous feedback and optimization mechanism enables the system to better adapt to the personalized needs of different users, provide more accurate and practical career development path advice and training improvement plans, and further enhance user satisfaction and experience.

[0049] Working principle: By constructing a user information acquisition module, a job information analysis module, a matching algorithm module, and a career planning advice module, the system can comprehensively obtain user information and job requirements, use an advanced matching algorithm to achieve accurate job matching, and provide personalized career development path advice and training improvement plans according to the user's career planning goals and matching results. At the same time, a user feedback unit is introduced to continuously optimize the advice quality to meet the personalized needs of users.

[0050] Furthermore, the employment matching and career development planning system of the embodiment is compared with the current traditional employment matching method (comparative example) in terms of effects, and the following table is obtained:

[0051]

[0052]

[0053] As can be seen from the above table, the employment matching and career development planning system of this embodiment is superior to the traditional employment matching method in terms of user information acquisition, job information analysis, matching accuracy, career planning advice, user feedback and optimization, and user experience. The system can obtain user information and job requirements more comprehensively and accurately, achieve accurate matching, and provide personalized career planning services, thereby significantly improving user experience and satisfaction.

[0054] An embodiment of the present application provides an electronic device, which is applicable to the above-mentioned employment matching and career development planning system, and includes:

[0055] A memory for storing computer programs and data;

[0056] A processor for running system programs.

[0057] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned employment matching and career development planning system, and performs hierarchical confidentiality management on the above-mentioned system and data according to the requirements of confidentiality management.

[0058] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0060] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0062] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0063] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0064] Computer-readable media includes both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0065] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements inherent to such process, article, or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, article, or apparatus that comprises the element.

[0066] The embodiments of the present invention are given for purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An employment matching and career development planning system, characterized in that: include: User information acquisition module, used to obtain the user's basic personal information, educational background, skill level, work experience and career planning goals; Position information analysis module, used to collect and analyze the job requirements, required skills, salary and benefits, and career development paths of each position; The matching algorithm module uses the preset matching algorithm to match users with the most suitable positions based on the data from the user information acquisition module and the position information analysis module; The career planning advice module provides users with personalized career development path advice and training improvement plans based on their career planning goals and matching results.

2. The employment matching and career development planning system as claimed in claim 1, characterized in that: The user information acquisition module also includes a user interest preference acquisition unit, which is used to acquire the user's career interest preference.

3. The employment matching and career development planning system as claimed in claim 1, characterized in that: The job information analysis module also includes a job dynamic update unit for updating job information in real time. The job dynamic update unit uses the ARIMA model to predict the future trend of job information. Through the ARIMA model, the system can predict the future trend of job information, thereby adjusting the matching strategy in advance.

4. The employment matching and career development planning system as claimed in claim 1, characterized in that: The matching algorithm module adopts a collaborative filtering algorithm, which recommends positions by analyzing the user's historical behavior and the behavior of similar users.

5. The employment matching and career development planning system as claimed in claim 1, characterized in that: In the career planning advice module, a machine learning algorithm is introduced to provide users with personalized career development path suggestions based on their career planning goals and matching results.

6. The employment matching and career development planning system as claimed in claim 1, characterized in that: The career planning suggestion module also includes a user feedback unit for collecting user feedback on the career planning suggestion.

7. A processor, characterized in that: The system is configured to execute the employment matching and career development planning system according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the employment matching and career development planning system according to any one of claims 1 to 6 is implemented.

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