Position matching platform, position matching method, position matching system and computer readable storage medium

Abstract

The invention relates to the technical field of computers, in particular to a position matching platform, a position matching method and system and a computer readable storage medium. A position matching platform comprising a job hunting end, a background end and a job supply end is built, a job hunter inputs personal professional information of the job hunter at the job hunting end and initiates an ability evaluation instruction, and the platform end generates test content according with the professional field and the professional level of the job hunter according to the personal professional information and / or the ability evaluation instruction. The background end generates an ability evaluation report of the job seeker and a position matching result generated according to the ability evaluation report according to evaluation condition data generated in the test process and multi-dimensional ability evaluation data provided by the job seeker; and providing the job matching result meeting the conditions of the job seeker to the job seeker, and sending the ability evaluation report of the job seeker to the supplier / job seeking end to ensure that the information of the two parties is symmetrical. The invention aims to solve the problem of how to accurately match the supply and demand of talents of both parties of a supplier / a job seeker.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a job matching platform, a job matching method, a system and a computer-readable storage medium. Background Art

[0002] With the increasingly fierce market competition and the growing demand for professional talents, both job recruitment and internal talent allocation are facing problems such as low efficiency and inaccurate matching. Traditional methods often rely on manual judgment and experience, which makes it difficult to comprehensively and objectively evaluate the ability and potential of talents, and it is also difficult to achieve accurate matching of talents and positions.

[0003] In the related technical solutions, usually after extracting information from the job seeker's resume based on natural language processing, the information is identified and processed according to the corresponding algorithm, and the job information with the highest matching degree with the information is matched in the database, and finally the job information is integrated and pushed to the job seeker. For example: The Chinese patent with application number CN202410107519.3 proposes a B-side AI recruitment method developed based on a large language model. The solution mainly uses a large language model technology based on the Transformer architecture to identify and analyze key sentences of the keywords of the recruiter's information and the job seeker's information, and then pushes the selected optimal matching results to both users.

[0004] The Chinese patent application number CN201811075813.1 proposes a resume search / delivery method, device, system and electronic device, and its solution also mainly achieves the matching of position and resume information by extracting keywords in the resume.

[0005] However, in the process of conceiving and implementing this application, the applicant discovered that the current job matching scheme ignores the verification of the authenticity and objectivity of the job seeker's resume information, and only relies on the resume information unilaterally provided by the job seeker for job matching. The entire matching process is prone to introduce more subjective factors and affect the accuracy of the match - and this lack of accuracy is not caused by the algorithm itself, but by the lack of verification of the source of information.

[0006] In view of this, it is necessary to provide a system and method that can perform job matching not only by relying on resume information provided by job seekers, so as to achieve accurate talent supply and demand matching between job seekers and job seekers. Summary of the invention

[0007] The main purpose of this application is to provide a job matching platform, aiming to solve the problem of how to accurately match the supply and demand of talents between job seekers and job seekers.

[0008] To achieve the above objectives, the present application provides a job matching platform, which includes: The job-seeking end includes a first display interface for allowing job seekers to input personal professional information and / or generate ability assessment instructions, a second display interface for allowing the job seekers to conduct multi-dimensional ability assessments based on the assessment content sent by the back-end, a third display interface for displaying the position matching results and / or ability assessment reports sent by the back-end, and an assessment situation recording module for recording the assessment situation data and multi-dimensional ability assessment data generated by the job seekers during the assessment process; The backend includes an ability assessment module for generating assessment content according to the ability assessment instruction sent by the job-seeking end, the personal professional information and / or the assessment situation data, a multi-dimensional ability assessment module for generating the ability assessment report according to the multi-dimensional ability assessment data sent by the job-seeking end, and a position matching module for generating a position matching result according to the ability assessment report; The service end includes a fourth display interface for displaying the evaluation report sent by the back-end. Optionally, the multi-dimensional capability evaluation module of the back-end includes: A personal qualification evaluation unit, used to extract the target field in the personal professional information and calculate the personal qualification ability factor index score associated with the target field; A professional ability evaluation unit, used to determine the professional ability evaluation data in the multi-dimensional ability evaluation data, and calculate the professional ability factor indicator score associated with the professional ability evaluation data; A work experience evaluation unit, used to determine the work experience data in the multi-dimensional ability assessment data, and calculate the work experience factor index score associated with the work experience data; A soft capability evaluation unit, used to determine the soft capability data in the multi-dimensional capability evaluation data, and calculate the soft capability factor indicator score associated with the soft capability data; The personality trait evaluation unit is used to determine the personality trait data in the multi-dimensional ability assessment data and calculate the soft ability factor index score associated with the personality trait data.

[0009] Optionally, the capability assessment module in the backend further includes: A test question generating unit, used to determine the assessment field information and assessment level information contained in the capability assessment instruction and / or the personal professional information, and generate assessment content that meets the assessment field information and the assessment level information, wherein the assessment content includes five dimensions: personal qualification, professional ability, work experience, soft ability and personality traits; The test question optimization unit is used to receive the test question difficulty adjustment instruction generated by the job seeker, and when receiving the test question difficulty adjustment instruction, call or generate the evaluation content of the next / previous evaluation level and send it to the job seeker as the next batch of evaluation questions displayed in sequence on the second display interface.

[0010] Optionally, the assessment situation recording module in the job-seeking terminal includes: An answering speed recording unit, used to record the time interval of the answering instructions of the job seeker responded to in the second display interface, and estimate the current answering speed of the job seeker according to the time interval; A correctness recording unit, used to calculate the current correctness of the answers to the test questions input by the job seeker in the second display interface; The test question difficulty assessment unit is used to calculate the current difficulty value of the assessment content according to the current answering speed and the current accuracy, and send a test question difficulty adjustment instruction to increase / decrease the difficulty to the back-end according to the difficulty value, so that when the back-end receives the test question difficulty adjustment instruction, it calls the test content of the next / previous assessment level in the database and sends it to the job-seeking end as the next batch of test content to be displayed sequentially on the second display interface.

[0011] Optionally, the backend further includes an optimization module, and the optimization module includes: A matching effect data collection unit, configured to collect flag data indicating a successful interview when the job seeker matches a target position included in the position matching result, wherein the flag data comes from the job providing end; A user feedback data collection unit, used to collect user feedback data generated when the job seeker evaluates the job matching result; The job matching result optimization unit is used to optimize the generation of job matching results according to the flag data and / or the user feedback data.

[0012] In addition, to achieve the above purpose, the present application also provides a job matching method, which is applied to the job matching platform as described in any one of the above items, and the method comprises the following steps: Step S10, the job-seeking end displays a first display interface for the job seeker to input personal professional information, and responds to the ability assessment instruction and / or information sending instruction triggered by the job seeker in the first display interface, and sends the personal professional information and / or the ability assessment instruction to the backend; Step S20, the backend generates evaluation content according to the personal professional information and / or the ability evaluation instruction, and sends it to the second display interface of the job-seeking terminal for display; Step S30, the job seeker terminal collects the multi-dimensional ability assessment data input by the job seeker in the second display interface and the assessment situation data generated according to the assessment situation of the job seeker, and responds to the information sending instruction triggered in the second display interface to send the multi-dimensional ability assessment data and the assessment situation data to the backend terminal; In step S40, the backend generates an ability assessment report based on the multi-dimensional ability assessment data and / or the personal professional information, generates a job matching result based on the job matching result, and sends it to the third display interface of the job seeker for display; and, in response to the job seeker's report viewing instruction triggered by the job supplying end, sends the ability assessment report to the job supplying end and displays it on the fourth display interface.

[0013] Optionally, the step of generating the evaluation data includes: The job seeker terminal records the time interval of the job seeker's answering instructions in response to the second display interface, and estimates the job seeker's current answering speed according to the time interval; and The job seeker terminal calculates the current correctness rate of the answer to the test question input by the job seeker in the second display interface; The job seeker terminal calculates the difficulty value of the current assessment content according to the current answering speed and the current correct rate, and sends a test question difficulty adjustment instruction to the backend terminal according to the difficulty value; When receiving the test question difficulty adjustment instruction, the backend calls the assessment content of the next / previous assessment level in the database and sends it to the job-seeking end as the assessment questions to be displayed in the next batch in sequence on the second display interface.

[0014] Optionally, after step S40, the method further includes: The job seeker end collects flag data indicating a successful interview when the job seeker matches the target job included in the job matching result, and collects user feedback data generated when the job seeker evaluates the job matching result, and sends the flag data and the user feedback data to the backend end; The backend determines whether the job matching result needs to be optimized according to the flag data and / or the user feedback data, and if so, optimizes the job matching result.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a job matching system, which includes: a memory, a processor, and a job matching program stored on the memory and executable on the processor, and when the job matching program is executed by the processor, the steps of the job matching method described in any one of the above items are implemented.

[0016] An embodiment of the present application provides a computer-readable storage medium, on which a job matching program is stored. When the job matching program is executed by a processor, the steps of the job matching method as described in any one of the above items are implemented.

[0017] This application has at least the following beneficial effects: 1. By building a job matching platform including the job seeker end, the backend end and the supply end, the job seeker can input his / her personal professional information and initiate the ability assessment instruction on the job seeker end. The platform end generates the test content that meets his / her professional field and professional level according to the personal professional information and / or the ability assessment instruction, and then sends it to the job seeker end for the job seeker to take the test. The backend end generates the job seeker's ability assessment report and the job matching results generated according to the ability assessment report based on the assessment data generated during the test and the multi-dimensional ability assessment data provided by the job seeker. While providing the job seeker with the job matching results that meet his / her own conditions, the job seeker's ability assessment report is sent to the supply / job seeker end to ensure information symmetry between the two parties. 2. Introduce a test phase to test the authenticity of the resume information provided by job seekers, ensuring that the authenticity of the resume information provided by job seekers meets the needs of job seekers; 3. By generating competency assessment reports that match the applicants’ abilities and job matching results that meet the applicants’ expectations, the applicants can have a clearer understanding of their own abilities, values ​​and suitable positions, and plan their careers more reasonably. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the architecture of the job matching platform involved in the embodiment of the present application; Figure 2 This is a schematic diagram of the architecture of the capability assessment module in the backend involved in the embodiment of the present application; Figure 3 This is a schematic diagram of the architecture of the assessment situation recording module in the job-seeking terminal involved in the embodiment of the present application; Figure 4 This is a schematic diagram of the architecture of the optimization module in the backend involved in the embodiment of the present application; Figure 5 A flowchart of a job matching method according to an embodiment of the present application; Figure 6 Another flowchart of the job matching method according to the embodiment of the present application is shown; Figure 7 Another flowchart of the job matching method according to the embodiment of the present application is shown; Figure 8 This is a schematic diagram of the architecture of the hardware operating environment of the job matching system involved in the embodiment of the present application.

[0019] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. Example

[0021] Reference Figure 1 In a first embodiment, the job matching platform includes: The job-seeking end includes a first display interface for allowing job seekers to input personal professional information and / or generate ability assessment instructions, a second display interface for allowing the job seekers to conduct multi-dimensional ability assessments based on the assessment content sent by the back-end, a third display interface for displaying the position matching results and / or ability assessment reports sent by the back-end, and an assessment situation recording module for recording the assessment situation data and multi-dimensional ability assessment data generated by the job seekers during the assessment process; The backend includes an ability assessment module for generating assessment content according to the ability assessment instruction sent by the job-seeking end, the personal professional information and / or the assessment situation data, a multi-dimensional ability assessment module for generating the ability assessment report according to the multi-dimensional ability assessment data sent by the job-seeking end, and a position matching module for generating a position matching result according to the ability assessment report; The service end includes a fourth display interface for displaying the evaluation report sent by the backend.

[0022] In this embodiment, the job-seeking terminal refers to a terminal used by a user whose identity is a job seeker, and the terminal includes three display interfaces and a functional module.

[0023] The first display interface is used for the user to input personal professional information and / or generate capability assessment instructions.

[0024] The second display interface shows the assessment content sent by the backend to the job-seeking end, and users can take tests and answer the test content on the second display interface.

[0025] In this embodiment, the test content includes but is not limited to video interviews, voice calls and test questions. Among them, video interviews and voice calls are communication sessions generated by pre-trained AI big model technology. The test content covers multiple dimensions.

[0026] Optionally, during the video interview, an AI digital person is generated by the backend, which interacts with the job seeker through the camera on the job seeker end. The camera collects the job seeker's facial expressions, body movements and appearance to conduct a preliminary interview with the job seeker. After the AI ​​large model on the backend determines that the job seeker's emotional expression and body movement ability are normal based on the video content, the video interview is considered to have passed.

[0027] Optionally, the voice call is conducted by AI dialing the user and asking questions to the user in the form of a voice conversation. After the user's answer is recorded, the AI ​​big model will judge the user's language conversation ability based on the recording. If the user's understanding of the question is correct, the voice call interview will be considered passed.

[0028] Optionally, the test questions are pre-made and stored in a database on the backend. The test questions corresponding to each field are different. The backend calls up a target test question set that meets the assessment field and the assessment level based on the assessment field information and assessment level information contained in the ability assessment instructions and / or personal professional information, and sends it to the second display interface for job seekers to answer.

[0029] Exemplarily, if the target test question set is called according to the ability assessment instruction, the user selects the professional field and assessment level to be assessed in the first display interface, and clicks a button to generate the ability assessment instruction corresponding to the professional field and assessment level.

[0030] Exemplarily, if the target test set is called based on personal professional information, the semantic model in the AI ​​big model will capture the key fields in the personal professional information, match the field and assessment level that best matches the key fields, and automatically call the test questions that match the field and assessment level. For example, AI extracts fields such as "programmer" and "5 years of experience" from the user's personal professional information, calls the programmer field and the test questions that match the difficulty level corresponding to 5 years of experience, and sends them to the second display interface for the user to test.

[0031] After the assessment is completed, the third display interface will show the assessment report and / or job matching results generated by the backend through the AI ​​model, so that users can view the assessment results and suitable jobs with their current professional skills.

[0032] In this embodiment, the evaluation situation recording module is responsible for collecting two types of data generated when the job seeker performs an evaluation in the second display interface of the job seeker terminal: evaluation situation data and multi-dimensional ability evaluation data.

[0033] The multi-dimensional ability assessment data is the data generated by job seekers when they respond to test content, and is used to reflect the user's abilities in multiple dimensions when responding to the test content.

[0034] The assessment data is data collected based on the real-time performance of the assessor during the assessment process. In this embodiment, in order to more accurately recommend positions that meet the professional level of job seekers, the job seeker terminal collects the assessment data to determine whether the difficulty of the current test question is suitable for the job seeker. If the test question is judged to be relatively simple, the difficulty of the next batch of test questions sent to the job seeker terminal will be increased.

[0035] In this embodiment, the part of the platform involving large computing power overhead is mainly placed on the backend, which can be a terminal such as a server cluster. The backend mainly includes a pre-trained AI model and a test content database. The AI ​​model includes a capability assessment module, a multi-dimensional capability assessment module, and a position matching module. The capability assessment module is used to generate or call the assessment content, the multi-dimensional capability assessment module is used to analyze the response made by the job seeker based on the assessment content to obtain an assessment report, and the position matching module provides positions / positions suitable for the current level of the job seeker's ability based on the assessment report.

[0036] The service end is a terminal used by a user whose user identity is an enterprise. The service end can receive the evaluation report generated by the background end and display it in the fourth display interface.

[0037] The first display interface, the second display interface, the third display interface and the fourth display interface referred to in this embodiment are the operating interfaces of the terminal when the user uses the program or system of the job matching platform. It can be understood that the display interface defined in this embodiment does not only include the display screen of the interface, but also should include the functions that can be implemented in the interface.

[0038] It should be noted that the specific generation method of the evaluation report and position matching results involved in this embodiment is not the focus of the disclosure of this application, and can be implemented by referring to the relevant existing technology. This application focuses on disclosing the architecture of a position matching platform, introducing a test link to evaluate the multi-dimensional capabilities of job seekers on the job search side, and the evaluation results are visible and transparent to both the supply and job search sides to ensure information symmetry between the supply and job search sides; on the other hand, the architecture can also generate an ability evaluation report that meets the job seeker's capabilities, as well as a position matching result that meets the job seeker's expectations, so that job seekers have a clearer understanding of their own abilities, values, and suitable positions, and help job seekers plan their careers more reasonably.

[0039] In the technical solution provided in this embodiment, a job matching platform including a job-seeking end, a backend end and a supply end is built. The job-seeking end allows job seekers to input their personal professional information and initiate ability assessment instructions. The platform end generates test content that meets their professional field and professional level based on the personal professional information and / or ability assessment instructions, and then sends it to the job seeker end for the job seeker to take the test. The backend end generates an ability assessment report for the job seeker and a job matching result generated based on the ability assessment report based on the assessment data generated during the test and the multi-dimensional ability assessment data provided by the job seeker. While providing the job seeker with a job matching result that meets their own conditions, the job seeker's ability assessment report is sent to the supply / job-seeking end to ensure information symmetry between the two parties. Example

[0040] In the second embodiment, based on the first embodiment, the multi-dimensional capability evaluation module of the backend includes: A personal qualification evaluation unit, used to extract the target field in the personal professional information and calculate the personal qualification ability factor index score associated with the target field; A professional ability evaluation unit, used to determine the professional ability evaluation data in the multi-dimensional ability evaluation data, and calculate the professional ability factor indicator score associated with the professional ability evaluation data; A work experience evaluation unit, used to determine the work experience data in the multi-dimensional ability assessment data, and calculate the work experience factor index score associated with the work experience data; A soft capability evaluation unit, used to determine the soft capability data in the multi-dimensional capability evaluation data, and calculate the soft capability factor indicator score associated with the soft capability data; The personality trait evaluation unit is used to determine the personality trait data in the multi-dimensional ability assessment data and calculate the soft ability factor index score associated with the personality trait data.

[0041] In this embodiment, the personal qualification assessment unit is characterized by assessing the job applicant's education background and work background in two dimensions.

[0042] Optionally, the score of the personal qualification and ability factor index may include but is not limited to four types of ability factors: academic qualifications, school, years of work experience and employed company. The personal qualification assessment unit recognizes that the higher the academic qualifications in the personal professional information, the higher the school ranking, the longer the years of work experience and the higher the status of the employed company in the industry, the higher the score of the personal qualification and ability factor index.

[0043] In this embodiment, the professional competence assessment unit is characterized by assessing the professional skills and knowledge mastery of the job seeker by calling the professional field and professional level selected by the job seeker. The professional competence assessment in different fields focuses on different assessment directions, and the professional competence assessment unit has a built-in professional question bank covering common fields on the market.

[0044] For example, taking a job applicant as a programmer as an example, in a specific implementation, we examine the professional ability from the following dimensions: professional knowledge, programming ability, architectural ability and engineering ability.

[0045] Taking professional knowledge as an example, we divide professional knowledge into six professional ability assessment factors: programming languages, computer science, computer networks, databases, frameworks and tools, and cutting-edge technologies and trends. We obtain the professional ability assessment data generated by job seekers when they answer questions in these six ability factor indicators, and calculate their ability factor indicator scores in the professional knowledge dimension based on the correct answer rate contained in the professional ability assessment data.

[0046] Similarly, we have also configured multiple professional ability assessment factors in the three dimensions of programming ability, architectural ability and engineering ability. We also calculate the corresponding ability factor indicator scores in these dimensions based on the correct answer rate contained in the professional ability assessment data.

[0047] In this embodiment, the work experience evaluation unit is characterized by evaluating the job applicant's abilities in three dimensions: project experience, project management and teamwork, and problem-solving ability.

[0048] Optionally, in a specific implementation, the work experience capability factor in the project experience dimension includes, but is not limited to: project scale, project type and technology stack, project role and project contribution.

[0049] Optionally, in a specific implementation, the work experience ability factors in the project management and teamwork dimensions include, but are not limited to: project planning, resource management, schedule management, risk management, communication management, conflict management, quality management and coordination ability.

[0050] Optionally, in a specific implementation, the work experience ability factor in the problem-solving ability dimension includes, but is not limited to: identifying and analyzing problems and problem-solving strategies.

[0051] In this embodiment, the soft ability assessment unit is characterized by assessing the job applicant's abilities in the dimensions of communication ability, learning ability, logical thinking ability, innovation ability and leadership.

[0052] Optionally, in a specific implementation, the soft ability factors in the communication ability dimension include but are not limited to: expression ability, communication ability, listening ability, persuasion and influence, and communication style; Optionally, in a specific implementation, the soft ability factors in the learning ability dimension include, but are not limited to: learning attitude, learning style, learning speed, and self-reflection growth; Optionally, in a specific implementation, the soft ability factors in the dimension of logical thinking ability include, but are not limited to: understanding ability, analytical ability, program formulation, decision-making ability, abstract ability and logical expression; Optionally, in a specific implementation, the soft capability factors in the innovation capability dimension include, but are not limited to: innovative thinking, technical vision, technical exploration and practice, and innovative achievements; Optionally, in a specific embodiment, the soft ability factors in the leadership dimension include, but are not limited to: strategic planning, team motivation, decision-making ability and guidance ability; In this embodiment, the personality trait assessment unit is characterized by assessing the job applicant's abilities in the dimensions of responsibility, openness, extroversion, agreeableness, and emotional stability.

[0053] Optionally, in a specific embodiment, the personality trait factors in the dimension of responsibility include, but are not limited to: reliability, self-discipline, organization, goal orientation, rigor and work enthusiasm; Optionally, in a specific embodiment, the personality trait factors on the openness dimension include, but are not limited to: curiosity, imagination, aesthetic sensitivity, and acceptance of new things; Optionally, in a specific embodiment, the personality trait factors on the extraversion dimension include, but are not limited to: sociability, self-confidence, agreeableness, adventurousness, and energy; Optionally, in a specific embodiment, the personality trait factors on the agreeableness dimension include but are not limited to: cooperation, trust, sharing, empathy and humility; Optionally, in a specific embodiment, the personality trait factors in the emotional stability dimension include, but are not limited to: stress resistance, adaptability, sense of security, self-regulation and optimism.

[0054] It should be noted that the purpose of this embodiment is to explain the characteristics covered by the ability factors of job seekers in the five major dimensions of personal qualifications, professional abilities, work experience, soft skills and personality traits. When setting the test content, the test questions can be set with reference to the characteristic directions given above. The specific question content is not the focus of this embodiment. Example

[0055] Reference Figure 2In a third embodiment, based on any of the foregoing embodiments, the capability assessment module further includes: A test question generating unit, used to determine the assessment field information and assessment level information contained in the capability assessment instruction and / or the personal professional information, and generate assessment content that meets the assessment field information and the assessment level information, wherein the assessment content includes five dimensions: personal qualification, professional ability, work experience, soft ability and personality traits; The test question optimization unit is used to receive the test question difficulty adjustment instruction generated by the job seeker, and when receiving the test question difficulty adjustment instruction, call or generate the evaluation content of the next / previous evaluation level and send it to the job seeker as the next batch of evaluation questions displayed in sequence on the second display interface.

[0056] In this embodiment, the ability assessment module at the backend generates assessment content including five dimensions of personal qualifications, professional ability, work experience, soft ability and personality traits according to the assessment field information and assessment level information selected or automatically identified by the job seeker, so as to conduct a comprehensive and accurate test on the job seeker.

[0057] In addition, the ability assessment module can also adjust the difficulty of questions during the assessment process in real time based on the test difficulty adjustment instructions sent by the job seeker. If the test taker performs well during the test, an adjustment instruction to increase the difficulty of the test questions will be generated, and the assessment content of the next assessment level will be sent to the second display interface of the job seeker.

[0058] This module is mainly called when testing the professional ability dimension of job applicants, and other ability dimensions are also called after applicable configuration.

[0059] It should be noted that when the backend sends the test content to the job seeker, it does not send all the test content to the job seeker at once, but sends it in batches. The batches include at least one question and at most one entire sub-dimension of questions. Example

[0060] Reference Figure 3 In a fourth embodiment, based on any of the foregoing embodiments, the test content includes professional ability test questions, and the assessment situation recording module in the job seeker terminal includes: An answering speed recording unit, used to record the time interval of the answering instructions of the job seeker responded to in the second display interface, and estimate the current answering speed of the job seeker according to the time interval; A correctness recording unit, used to calculate the current correctness of the answers to the test questions input by the job seeker in the second display interface; The test question difficulty assessment unit is used to calculate the current difficulty value of the assessment content according to the current answering speed and the current accuracy, and send a test question difficulty adjustment instruction to increase / decrease the difficulty to the back-end according to the difficulty value, so that when the back-end receives the test question difficulty adjustment instruction, it calls the test content of the next / previous assessment level in the database and sends it to the job-seeking end as the next batch of test content to be displayed sequentially on the second display interface.

[0061] In this embodiment, in order to avoid the phenomenon of "memorizing questions" caused by repetition of questions during multiple assessments by job seekers, the job matching platform can adjust the difficulty of the assessment content in real time.

[0062] Specifically, the assessment situation recording module in the job search end can record the triggering moment every time the job seeker enters an answer instruction, and thus estimate the job seeker's current answering speed based on the time interval between the triggering of the two answer instructions. The shorter the time interval, the faster the current answering speed.

[0063] For each test question answered by the job seeker, the correctness is judged in real time on the backend, and the job seeker's current accuracy is calculated in real time.

[0064] The difficulty value of the current assessment content is calculated based on the current answering speed and the current accuracy. If the current answering speed is higher than the preset speed threshold and the current accuracy is higher than the accuracy threshold, the calculated difficulty value will decrease, otherwise it will increase.

[0065] Optionally, an interval can be set for each difficulty value. When the difficulty value changes to another interval, a test question difficulty adjustment instruction corresponding to the difficulty level of the interval is generated, so that the background end calls the assessment content of the next assessment level in the database and sends it to the job seeker end as the next batch of assessment content displayed sequentially on the second display interface.

[0066] It should be noted that this module is mainly called when testing the professional ability dimension of job seekers, and the other ability dimensions can also be called after applicability configuration.

[0067] In addition, the batches include at least one question and at most one entire sub-dimension of questions. Example

[0068] Reference Figure 4 In a fifth embodiment, based on any of the foregoing embodiments, the backend further includes an optimization module, and the optimization module includes: A matching effect data collection unit, configured to collect flag data indicating a successful interview when the job seeker matches a target position included in the position matching result, wherein the flag data comes from the job providing end; A user feedback data collection unit, used to collect user feedback data generated when the job seeker evaluates the job matching result; The job matching result optimization unit is used to optimize the generation of job matching results according to the flag data and / or the user feedback data.

[0069] In this embodiment, in order to ensure the accuracy of the job matching results, the backend in this embodiment also includes an optimization module, which can intelligently determine the reliability of the job matching results based on feedback from users (including job seekers and job applicants).

[0070] In this embodiment, the job matching platform will push the jobs posted by the job supplying side on the platform to the job seeking side, and push the jobs in descending order of matching degree. When the job seeker selects the pushed job on the job matching platform, a conversation interface of the job supplying side corresponding to the job will pop up, and the supply and demand parties will conduct preliminary communication, interviews, etc. in the conversation interface. If the interview is passed, the job supplying side will send flag data indicating a successful interview to the background side through the job supplying side. After the matching effect data collection unit of the background side captures the flag data, it is determined that the matching effect pushed to the job seeking side is better. If the flag data indicating a successful interview is not captured, it is determined that the interview is unsuccessful, and it is determined that the matching effect pushed to the job seeking side needs to be optimized.

[0071] In this embodiment, the job seeker can also directly evaluate the job matching results pushed by the platform, for example, by clicking the "Yes" or "No" button under "Are you satisfied with the position" in the push interface to generate user feedback data.

[0072] After receiving user feedback data and / or flag data indicating a successful interview, the backend intelligently determines whether the job matching result needs to be optimized through the job matching result optimization unit. Example

[0074] In addition, as another implementation scheme, refer to Figure 5 The flowchart of the job matching method shown is applied to the job matching platform as described in any of the previous embodiments, and the method includes the following steps: Step S10, the job-seeking end displays a first display interface for the job seeker to input personal professional information, and responds to the ability assessment instruction and / or information sending instruction triggered by the job seeker in the first display interface, and sends the personal professional information and / or the ability assessment instruction to the backend; Step S20, the backend generates evaluation content according to the personal professional information and / or the ability evaluation instruction, and sends it to the second display interface of the job-seeking terminal for display; Step S30, the job seeker terminal collects the multi-dimensional ability assessment data input by the job seeker in the second display interface and the assessment situation data generated according to the assessment situation of the job seeker, and responds to the information sending instruction triggered in the second display interface to send the multi-dimensional ability assessment data and the assessment situation data to the backend terminal; In step S40, the backend generates an ability assessment report based on the multi-dimensional ability assessment data and / or the personal professional information, generates a job matching result based on the job matching result, and sends it to the third display interface of the job seeker for display; and, in response to the job seeker's report viewing instruction triggered by the job supplying end, sends the ability assessment report to the job supplying end and displays it on the fourth display interface. Example

[0075] Reference Figure 6 In the seventh embodiment, based on the sixth embodiment, the step of generating the evaluation data includes: The job seeker terminal records the time interval of the job seeker's answering instructions in response to the second display interface, and estimates the job seeker's current answering speed according to the time interval; and The job seeker terminal calculates the current correctness rate of the answer to the test question input by the job seeker in the second display interface; The job seeker terminal calculates the difficulty value of the current assessment content according to the current answering speed and the current correct rate, and sends a test question difficulty adjustment instruction to the backend terminal according to the difficulty value; When receiving the test question difficulty adjustment instruction, the backend calls the assessment content of the next / previous assessment level in the database and sends it to the job-seeking end as the assessment questions to be displayed in the next batch in sequence on the second display interface. Example

[0076] Reference Figure 7 In the eighth embodiment, based on the sixth embodiment, after step S40, the following steps are further included: Step S50, the job seeker end collects flag data indicating a successful interview when the job seeker matches the target job included in the job matching result, and collects user feedback data generated when the job seeker evaluates the job matching result, and sends the flag data and the user feedback data to the backend end; Step S60: the backend determines whether the job matching result meets the need to optimize the matching algorithm based on the flag data and / or the user feedback data, and if so, optimizes the job matching result.

[0077] In addition, as another implementation scheme, Figure 8This is a schematic diagram of the architecture of the hardware operating environment of the job matching system involved in the embodiment of the present application.

[0078] like Figure 8 As shown, the job matching system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0079] Those skilled in the art will understand that Figure 8 The job matching system architecture shown in the figure does not constitute a limitation on the job matching system, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0080] like Figure 8 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module and a job matching program. The operating system is a program that manages and controls the hardware and software resources of the job matching system, the job matching program and the operation of other software or programs.

[0081] exist Figure 8 In the job matching system shown, the user interface 1003 is mainly used to connect to the terminal and communicate data with the terminal; the network interface 1004 is mainly used for the background server and communicates data with the background server; the processor 1001 can be used to call the job matching program stored in the memory 1005.

[0082] In this embodiment, the job matching system includes: a memory 1005, a processor 1001, and a job matching program stored in the memory and executable on the processor, wherein: When the processor 1001 calls the job matching program stored in the memory 1005, the following operations are performed: Step S10, the job-seeking end displays a first display interface for the job-seeker to input personal professional information, and responds to an information sending instruction triggered by the job-seeker in the first display interface to send the personal professional information to the backend end; Step S20, the backend generates evaluation content according to the personal professional information, and sends it to the second display interface of the job-seeking terminal for display; Step S30, the job seeker terminal collects the test answer information input by the job seeker in the second display interface and the evaluation data generated according to the evaluation of the job seeker, and responds to the information sending instruction triggered in the second display interface to send the test answer information and the evaluation data to the backend terminal; Step S40, the backend generates a job matching result and / or an ability assessment report based on the test answer information and / or the personal professional information, and sends it to the third display interface of the job-seeking end for display; and, in response to the job seeker's report viewing instruction triggered by the job-providing end, sends the ability assessment report to the fourth display interface of the job-providing end.

[0083] When the processor 1001 calls the job matching program stored in the memory 1005, the following operations are performed: The job seeker terminal records the time interval of the job seeker's answering instructions in response to the second display interface, and estimates the job seeker's current answering speed according to the time interval; and The job seeker terminal calculates the current correctness rate of the answer to the test question input by the job seeker in the second display interface; The job seeker terminal calculates the difficulty value of the current assessment content according to the current answering speed and the current correct rate, and sends a test question difficulty adjustment instruction to the backend terminal when the difficulty value is lower than a preset difficulty threshold; When receiving the test question difficulty adjustment instruction, the backend calls the evaluation content of the next evaluation level in the database and sends it to the job-seeking end as the evaluation content displayed in the next batch in sequence on the second display interface.

[0084] When the processor 1001 calls the job matching program stored in the memory 1005, the following operations are performed: The job seeker end collects flag data indicating a successful interview when the job seeker matches the target job included in the job matching result, and collects user feedback data generated when the job seeker evaluates the job matching result, and sends the flag data and the user feedback data to the backend end; The backend determines whether the job matching result meets the need to optimize the matching algorithm based on the flag data and / or the user feedback data, and if so, optimizes the job matching result.

[0085] In addition, it can be understood by a person skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the job matching system to implement the process steps of the embodiment of the above method.

[0086] Therefore, the present application also provides a computer-readable storage medium, which stores a job matching program. When the job matching program is executed by a processor, the various steps of the job matching method described in the above embodiment are implemented.

[0087] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0088] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0089] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may 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 codes.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0094] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0095] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A job matching platform, characterized in that: The job matching platform includes: The job-seeking end includes a first display interface for allowing job seekers to input personal professional information and / or generate ability assessment instructions, a second display interface for allowing the job seekers to conduct multi-dimensional ability assessments based on the assessment content sent by the back-end, a third display interface for displaying the position matching results and / or ability assessment reports sent by the back-end, and an assessment situation recording module for recording the assessment situation data and multi-dimensional ability assessment data generated by the job seekers during the assessment process; The backend includes an ability assessment module for generating assessment content according to the ability assessment instruction sent by the job-seeking end, the personal professional information and / or the assessment situation data, a multi-dimensional ability assessment module for generating the ability assessment report according to the multi-dimensional ability assessment data sent by the job-seeking end, and a position matching module for generating a position matching result according to the ability assessment report; The service end includes a fourth display interface for displaying the evaluation report sent by the backend.

2. The platform according to claim 1, characterized in that The multi-dimensional capability assessment module of the backend includes: A personal qualification evaluation unit, used to extract the target field in the personal professional information and calculate the personal qualification ability factor index score associated with the target field; A professional ability evaluation unit, used to determine the professional ability evaluation data in the multi-dimensional ability evaluation data, and calculate the professional ability factor indicator score associated with the professional ability evaluation data; A work experience evaluation unit, used to determine the work experience data in the multi-dimensional ability assessment data, and calculate the work experience factor index score associated with the work experience data; A soft capability evaluation unit, used to determine the soft capability data in the multi-dimensional capability evaluation data, and calculate the soft capability factor indicator score associated with the soft capability data; The personality trait evaluation unit is used to determine the personality trait data in the multi-dimensional ability assessment data and calculate the soft ability factor index score associated with the personality trait data.

3. The platform according to claim 1, characterized in that The capability assessment module in the backend also includes: A test question generating unit, used to determine the assessment field information and assessment level information contained in the capability assessment instruction and / or the personal professional information, and generate assessment content that meets the assessment field information and the assessment level information, wherein the assessment content includes five dimensions: personal qualification, professional ability, work experience, soft ability and personality traits; The test question optimization unit is used to receive the test question difficulty adjustment instruction generated by the job seeker, and when receiving the test question difficulty adjustment instruction, call or generate the evaluation content of the next / previous evaluation level and send it to the job seeker as the next batch of evaluation questions displayed in sequence on the second display interface.

4. The platform according to claim 1 or 3, characterized in that: The assessment situation recording module in the job-seeking terminal includes: An answering speed recording unit, used to record the time interval of the answering instructions of the job seeker responded to in the second display interface, and estimate the current answering speed of the job seeker according to the time interval; A correctness recording unit, used to calculate the current correctness of the answers to the test questions input by the job seeker in the second display interface; The test question difficulty assessment unit is used to calculate the current difficulty value of the assessment content according to the current answering speed and the current accuracy, and send a test question difficulty adjustment instruction to increase / decrease the difficulty to the back-end according to the difficulty value, so that when the back-end receives the test question difficulty adjustment instruction, it calls the test content of the next / previous assessment level in the database and sends it to the job-seeking end as the next batch of test content to be displayed sequentially on the second display interface.

5. The platform according to claim 1, characterized in that The backend also includes an optimization module, which includes: A matching effect data collection unit, configured to collect flag data indicating a successful interview when the job seeker matches a target position included in the position matching result, wherein the flag data comes from the job providing end; A user feedback data collection unit, used to collect user feedback data generated when the job seeker evaluates the job matching result; The job matching result optimization unit is used to optimize the generation of job matching results according to the flag data and / or the user feedback data.

6. A job matching method, characterized in that: Applied to the job matching platform according to any one of claims 1 to 5, the method comprises the following steps: Step S10, the job-seeking end displays a first display interface for the job seeker to input personal professional information, and responds to the ability assessment instruction and / or information sending instruction triggered by the job seeker in the first display interface, and sends the personal professional information and / or the ability assessment instruction to the backend; Step S20, the backend generates evaluation content according to the personal professional information and / or the ability evaluation instruction, and sends it to the second display interface of the job-seeking terminal for display; Step S30, the job seeker terminal collects the multi-dimensional ability assessment data input by the job seeker in the second display interface and the assessment situation data generated according to the assessment situation of the job seeker, and responds to the information sending instruction triggered in the second display interface to send the multi-dimensional ability assessment data and the assessment situation data to the backend terminal; In step S40, the backend generates an ability assessment report based on the multi-dimensional ability assessment data and / or the personal professional information, generates a job matching result based on the job matching result, and sends it to the third display interface of the job seeker for display; and, in response to the job seeker's report viewing instruction triggered by the job supplying end, sends the ability assessment report to the job supplying end and displays it on the fourth display interface.

7. The job matching method according to claim 6, characterized in that: The step of generating the evaluation data comprises: The job seeker terminal records the time interval of the job seeker's answering instructions in response to the second display interface, and estimates the job seeker's current answering speed according to the time interval; and The job seeker terminal calculates the current correctness rate of the answer to the test question input by the job seeker in the second display interface; The job seeker terminal calculates the difficulty value of the current assessment content according to the current answering speed and the current correct rate, and sends a test question difficulty adjustment instruction to the backend terminal according to the difficulty value; When receiving the test question difficulty adjustment instruction, the backend calls the assessment content of the next / previous assessment level in the database and sends it to the job-seeking end as the assessment questions to be displayed in the next batch in sequence on the second display interface.

8. The method according to claim 6, characterized in that After step S40, the method further includes: The job seeker end collects flag data indicating a successful interview when the job seeker matches the target job included in the job matching result, and collects user feedback data generated when the job seeker evaluates the job matching result, and sends the flag data and the user feedback data to the backend end; The backend determines whether the job matching result needs to be optimized according to the flag data and / or the user feedback data, and if so, optimizes the job matching result.

9. A job matching system, characterized in that: The job matching system comprises: a memory, a processor and a job matching program stored in the memory and executable on the processor. When the job matching program is executed by the processor, the steps of the job matching method as claimed in any one of claims 6 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a position matching program, and when the position matching program is executed by the processor, the steps of the position matching method according to any one of claims 6 to 8 are implemented.

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