Escort method and device for assisting job seeker in simulated interview

Abstract

The invention discloses an escort method and device for assisting a job seeker in simulated interview. The method is executed based on a pre-processed question database, and comprises the steps of determining an expected position of a job hunting user, an industry to which the expected position belongs, a corresponding function and at least a corresponding first recruitment subject according to job hunting intention and / or resume information of the job hunting user, and performing recruitment on the job hunting user based on historical recruitment behaviors of the first recruitment subject. Determining working skills and working experiences required by the expected position; screening an escort template in a question database; updating the escort template according to the working skill required by the expected position; updating the escort template according to the working experience required by the expected position; and on the basis of the updated escort template group questions, obtaining a plurality of interview questions for the expected position for simulating the interview process of the first recruitment subject. According to the embodiment of the invention, appropriate questions are provided for the job-hunting user, so that the job-hunting user can prepare for interview in a more targeted manner.

Description

Technical Field

[0001] The present application relates to the fields of Internet and artificial intelligence technology, as well as the field of recruitment, and in particular to a method and device for predicting topics for assisting job seekers in conducting simulated interviews. Background Art

[0002] With the rapid development and changes in all walks of life, the demands of both recruiters and job seekers for the workplace are becoming more diversified. The existing industry and job classification system is characterized by a large number of titles but insufficient details. Therefore, it is increasingly difficult for traditional industry classification and job functions to effectively distinguish the specific needs of different industries and positions. In this context, it is becoming increasingly difficult to effectively maintain interview (or written test) question banks for different industries and positions.

[0003] First of all, from the perspective of employers, the current updating efficiency of the interview question bank is low, and questions under certain fixed categories still rely mainly on manual maintenance, which easily leads to the situation where the content of the question bank is out of touch with the job requirements, and cannot meet the precise needs of job seekers and employers.

[0004] At the same time, from the perspective of job seekers, when preparing for interviews, job seekers usually hope to find suitable interview questions related to their desired positions to prepare and practice, so as to increase the chances of a successful interview. However, in reality, it is difficult to find such questions to assist in practice. In the absence of targeted practice, the chances of a successful job search may be reduced; another situation is that some job seekers have a high degree of match between their own abilities and job requirements, but since they do not have much interview experience, they are very likely to miss out on highly matched positions due to insufficient preparation for interviews. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a method and device for predicting topics for assisting job seekers in conducting simulated interviews, which are used to provide job seekers with suitable topics so that job seekers can prepare for interviews more specifically.

[0006] The embodiment of the present application provides a method for predicting topics for assisting job seekers in conducting simulated interviews. The method is executed based on a preprocessed topic database, wherein: a plurality of professional topic libraries are stored in the topic database, different professional topic libraries correspond to different recruitment entities, each professional topic library includes one or more interview topic templates generated according to historical recruitment topics of the recruitment entity, and each interview topic template has at least two tags, and the at least two tags include an industry tag and a function tag; the method comprises: determining the job seeker's desired position and the industry to which the desired position belongs, the corresponding function, and at least a first corresponding recruitment entity according to the job-seeking intention and / or resume information of the job seeker, and determining the work skills and work experience required for the desired position based on the historical recruitment behavior of the first recruitment entity; determining a first professional topic library corresponding to the first recruitment entity in the topic database, and generating a search result for the first professional topic library for the first professional topic library; The multiple interview question templates in the industry question database are screened, and the interview question templates that do not match at least one of the industry and function of the expected position are filtered out during the screening, and the interview question templates that match both the industry and function of the expected position are retained and determined as the predicted question templates; the predicted question templates are updated according to the work skills required for the expected position. During the update, if the work skills in the job seeker's resume information do not meet the work skills required for the expected position, at least one question about work skills is added to the predicted question template; according to the work experience required for the expected position, the predicted question template is updated. During the update, if the work experience in the job seeker's resume information does not meet the work experience required for the expected position, at least one question about work experience is added to the predicted question template; questions are grouped based on the updated predicted question template to obtain multiple interview questions for the expected position, which are used to simulate the interview process of the first recruitment entity.

[0007] Optionally, according to the method of the embodiment of the present application, the interview process includes a first interview process and a second interview process; based on the updated predicted topic template, multiple interview questions for the expected position are obtained to simulate the interview process of the first recruitment entity, including: based on the predicted topic template updated with the work skills required for the expected position, multiple first interview questions about work skills are obtained; based on the first interview questions, the first interview process of the first recruitment entity is simulated; the answer information of the job seeker in the first interview process is obtained, and the evaluation results of the answer information are generated through the answer evaluation model, wherein different evaluation results are associated with different difficulty levels of the second interview process; according to the difficulty level associated with the evaluation result, the prompt information in the questions about work experience in the predicted topic template is adjusted to obtain multiple second interview questions about work experience, wherein the difficulty level is positively correlated with the amount of prompt information; based on the second interview questions, the second interview process of the first recruitment entity is simulated.

[0008] Optionally, the method according to the embodiment of the present application also includes: determining the weights corresponding to the work skills and work experience required for the desired position based on the historical recruitment behavior of the first recruiting entity; determining the number of questions about work skills in the topic template and the number of questions about work experience in the topic template based on the weights corresponding to the work skills and work experience respectively.

[0009] Optionally, according to the method of the embodiment of the present application, an industry question database corresponding to multiple specific industries is stored, and the industry question database includes multiple industry-specific knowledge test questions with different difficulty levels; the method also includes: based on a request for an industry knowledge simulation interview, determining a first industry question database corresponding to the industry to which the expected position belongs, and identifying the industry matching degree between the user's resume information and the industry to which the expected position belongs through an industry identification model, wherein different industry matching degrees are associated with different difficulty levels; and selecting industry-specific knowledge test questions corresponding to the difficulty levels associated with the industry matching degrees in the first industry question database as industry knowledge interview simulation questions, and the industry knowledge interview simulation questions are used to simulate the interview process of an industry knowledge interview.

[0010] Optionally, the method according to the embodiment of the present application also includes: obtaining historical recruitment titles of multiple recruiting entities, multiple positions corresponding to the historical recruitment titles, and industry labels and functional labels corresponding to each position; cross-combining the industry labels and functional labels to obtain multiple label groups; corresponding to each label group, obtaining functional information of the positions corresponding to the labels in the label group; generating an interview question template based on the functional information of the positions corresponding to the labels in the label group and the historical recruitment titles, and establishing an association between the interview question template and the labels in the label group.

[0011] Optionally, the method according to the embodiment of the present application further includes: when the position corresponding to the historical recruitment question belongs to a specific industry, adding the interview question template generated based on the functional information of the position and the historical recruitment question to the industry question library corresponding to the specific industry.

[0012] The embodiment of the present application provides a device for predicting topics for assisting job seekers in conducting simulated interviews, including: a pre-processed topic database, wherein the topic database stores a plurality of professional topic libraries, wherein different professional topic libraries correspond to different recruitment entities, and each professional topic library includes one or more interview topic templates generated based on historical recruitment topics of the recruitment entity, and each interview topic template has at least two tags, wherein the at least two tags include an industry tag and a function tag; the device also includes: a data processing module, which is used to determine the job seeker's desired position and the industry to which the desired position belongs, the corresponding function, and at least a first recruitment entity corresponding to the job seeker's job-seeking intention and / or resume information, and to determine the work skills and work experience required for the desired position based on the first recruitment entity's historical recruitment behavior; the data processing module is also used to determine a first professional topic library corresponding to the first recruitment entity in the topic database, and to process the content in the first professional topic library. A plurality of interview question templates are screened, and during the screening, the interview question templates that do not match at least one of the industry and function of the expected position are filtered out, and the interview question templates that match both the industry and function of the expected position are retained and determined as predicted question templates; a predicted question template is used to update the predicted question template according to the work skills required for the expected position. During the update, if the work skills in the resume information of the job seeker do not meet the work skills required for the expected position, at least one question about the work skills is added to the predicted question template; the predicted question module is also used to update the predicted question template according to the work experience required for the expected position. During the update, if the work experience in the resume information of the job seeker does not meet the work experience required for the expected position, at least one question about the work experience is added to the predicted question template; an interview simulation module is used to compose questions based on the updated predicted question template to obtain multiple interview questions for the expected position, which are used to simulate the interview process of the first recruitment entity.

[0013] An embodiment of the present application provides an electronic device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the steps of the above method are implemented.

[0014] An embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the above method are implemented.

[0015] An embodiment of the present application provides a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the steps of the above method are implemented.

[0016] By adopting the embodiment of the present application, interview question templates are generated by obtaining historical recruitment questions of different recruitment entities, and the interview question templates are associated with industry labels and function labels and stored in professional question libraries of different recruitment entities. In this way, the interview question templates in the question library can better adapt to the functional requirements and industry requirements corresponding to each position of the recruitment entity, which is conducive to improving the accuracy of the predicted questions.

[0017] When a job seeker needs a simulated interview, based on the job intention and / or resume information provided by the job seeker, the industry to which the job seeker's desired position belongs, the corresponding functions and at least the corresponding first recruitment entity can be accurately matched. Then, based on the historical recruitment behavior of the first recruitment entity, the job skills and work experience required for the job seeker's desired position are determined; then, in the first professional question bank corresponding to the first recruitment entity, the interview question templates that match the industry and functions of the desired position are retained and determined as the predicted question templates during screening, thereby improving the fit between the predicted question templates and real interview questions and providing job seekers with suitable questions for interview practice.

[0018] When generating specific interview questions, the predicted question template is updated in combination with the job seeker's work skills and work experience. If the work skills in the job seeker's resume information do not meet the work skills required for the expected position, then at least one question about work skills is added to the predicted question template; if the work experience in the job seeker's resume information does not meet the work experience required for the expected position, then at least one question about work experience is added to the predicted question template. In this way, more targeted training can be provided for possible deficiencies of job seekers, helping job seekers to better prepare for interviews. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following briefly introduces the drawings in the embodiments of the present application.

[0020] Figure 1 It is a schematic diagram of the system architecture of an embodiment of the present application.

[0021] Figure 2 It is a flowchart of a method for predicting topics for assisting job seekers in conducting simulated interviews according to an embodiment of the present application.

[0022] Figure 3 The present invention provides a topic prediction system for assisting job seekers in conducting simulated interviews.

[0023] Figure 4 It is a structural block diagram of a topic prediction device for assisting job seekers in conducting simulated interviews according to an embodiment of the present application.

[0024] Figure 5It is a schematic diagram of an electronic device used to implement the method for predicting topics for assisting job seekers in conducting simulated interviews according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirit of the present application clearer and more thorough, so that those skilled in the art can better understand and implement the principles and spirit of the present application. The exemplary embodiments provided herein are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by ordinary technicians of the art without creative work are within the scope of protection of this application.

[0026] It should be noted that the acquisition, storage, use, and processing of data in the embodiments of the present application are in compliance with the relevant provisions of national laws and regulations.

[0027] In this document, terms such as first, second, third, etc. are only used to distinguish one entity (or operation) from another entity (or operation), but not to require or imply any order or relationship between these entities (or operations).

[0028] Embodiments of the present application relate to terminal devices and / or servers. Those skilled in the art will appreciate that the embodiments of the present application may be implemented as a system, apparatus, device, method, computer-readable storage medium, or computer program product. Therefore, the present disclosure may be specifically implemented as at least one of the following forms: complete hardware, complete software, or a combination of hardware and software. According to the embodiments of the present application, the present application seeks protection for a method, apparatus, electronic device, computer-readable storage medium, and computer program product for assisting job seekers in conducting mock interviews. Figure 1 A schematic diagram of a system architecture of an embodiment of the present application is shown. Figure 1 As shown, the system includes a terminal device 102a, a terminal device 102b and a server 104. Through different terminal devices, for example, the terminal device 102a and the terminal device 102b can access the same service device.

[0029] The terminal device may include at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, etc. A client may be installed on the terminal device 102. For example, the client may be a client that specifically performs a specific function (such as an application app), or a client that is embedded with a variety of application applets (different functions), or a client that is logged in through a browser. Job seekers can operate on the terminal device. For example, the user can open the client installed on the terminal device and input instructions through the client operation, or the user can open the browser installed on the terminal device and input instructions through the browser operation. After the terminal device receives the instruction input by the user, the request information containing the instruction is sent to the server 104. After receiving the request information, the server 104 performs corresponding processing and then returns the processing result information to the terminal device. The user instruction is completed through a series of data processing and information interaction.

[0030] The service device 102 includes a question database, in which a plurality of professional question databases are stored. Different professional question databases correspond to different recruitment entities. Each professional question database includes one or more interview question templates generated based on the historical recruitment questions of the recruitment entity. Each interview question template has at least two labels, and the at least two labels include an industry label and a function label.

[0031] Figure 2 A flow chart of a method for assisting job seekers in conducting simulated interviews according to an embodiment of the present application is shown, and the method comprises the following steps:

[0032] Step 201, based on the job-seeking intention and / or resume information of the job-seeker, determine the job-seeker's desired position and the industry to which the desired position belongs, the corresponding function and at least the corresponding first recruiting entity, and determine the work skills and work experience required for the desired position based on the historical recruitment behavior of the first recruiting entity.

[0033] Step 202, determine the first professional question bank corresponding to the first recruiting entity in the question database, and screen multiple interview question templates in the first professional question bank. During the screening, filter out the interview question templates that do not match at least one of the industry and function of the expected position, and retain the interview question templates that match both the industry and function of the expected position and determine them as the predicted question templates.

[0034] Step 203, updating the prediction template according to the job skills required for the desired position. During the update, if the job skills in the job seeker's resume information do not meet the job skills required for the desired position, at least one question about job skills is added to the prediction template.

[0035] Step 204, update the prediction template according to the work experience required for the desired position. During the update, if the work experience in the job seeker's resume information does not meet the work experience required for the desired position, add at least one question about work experience to the prediction template.

[0036] Step 205, composing questions based on the updated predicted topic template to obtain a plurality of interview questions for the desired position, which are used to simulate the interview process of the first recruitment entity.

[0037] The above steps are described in detail below in conjunction with specific embodiments, as shown below.

[0038] The topic database stores multiple professional topic libraries, and different professional topic libraries correspond to different recruitment entities. For example, the recruitment entity may be a recruiter such as an enterprise or an individual economic organization that establishes a labor relationship with a worker.

[0039] In the question database, multiple different professional question banks can be divided according to different recruitment entities. For example, the interview question templates generated by the historical recruitment questions of two companies can be stored separately. When preparing interview questions for job seekers, interview question templates can be obtained from the professional question bank of the recruitment entity corresponding to the expected position. By dividing multiple different professional question banks, it is possible to save time in screening irrelevant questions and directly locate questions related to the job seeker's expected position. For job seekers, there is no need to spend energy manually looking for questions that meet the specific requirements of the company for the expected position, and automatically generate interview question sets that meet the needs for job seekers. For example, a job seeker is preparing for an interview with Company A. The system directly screens questions from Company A's professional question bank, without wasting time dealing with irrelevant questions from Company B or other entities.

[0040] In each professional question database, each interview question template has at least two tags, including an industry tag and a function tag. Therefore, in the process of predicting questions for assisting job seekers in conducting simulated interviews, it is easy to find an interview question template that is more in line with the job seeker's desired position, so as to generate more accurate interview questions.

[0041] In step 201, the job-seeking intention of the job-seeking user may include the current expectation of the position, for example, hoping to find a position of a certain functional category in a certain industry. The job-seeking intention may also include information such as expected salary and expected work location. Therefore, based on the job-seeking intention of the job-seeking user, the expected position can be found for the job-seeking user.

[0042] The resume information of the job seeker may include historical work experience information such as his / her work skills and work experience, and may also include the job seeker's own basic personal information, etc. Based on the resume information of the job seeker, a position matching the resume may be used as the job seeker's desired position.

[0043] Optionally, when a job seeker provides complete job search intentions and resume information, multiple desired positions determined based on the job search intentions can be further streamlined in combination with the job seeker's job search intentions and resume information. During the streamlining process, positions with a higher degree of match with the resume information are retained as desired positions.

[0044] Optionally, the number of desired positions may be one or more, and the multiple desired positions may come from different recruitment entities or from the same recruitment entity. Therefore, in the embodiment of the present application, the number of first recruitment entities may be one or more.

[0045] Historical recruitment behaviors include historical hiring behaviors, job information release behaviors, etc. Among them, historical hiring behaviors may include resume viewing behaviors and resume hiring behaviors. By obtaining the resumes and job information of hired users, it is possible to determine the job skills and work experience that match the job resumes and job information of these hired users as the job skills and work experience required for the desired position. Based on the job skills and work experience required for the desired position, interview questions are provided to job seekers, which can improve the accuracy of the prediction direction.

[0046] Optionally, historical hiring behaviors may include resume viewing behaviors and resume rejection behaviors. For the same position, by comparing the skills and experience in the hired resumes and the rejected resumes, as well as the functional information, it is determined which skills and experiences in the resumes have a higher probability of being hired, and then the skills and experiences with a higher probability of being hired are determined as the work skills and work experience required for the desired position. In this way, interview questions are provided to job seekers based on the work skills and work experience required for the desired position, which can further improve the accuracy of the topic prediction direction.

[0047] When there are multiple desired positions, the job skills and work experience required for each desired position may be determined separately.

[0048] After determining the work skills and work experience required for the desired position, the next step involves step 202, in which the predicted question templates are screened in the question database to find interview question templates that match the industry and function of the desired position, in preparation for generating interview questions.

[0049] For example, the job seeker's desired position is Java backend development engineer, the industry he belongs to is the Internet, the corresponding function is backend development, and the first recruiting entity is a certain Internet company. Combined with the historical recruitment behavior of a certain Internet company, it is determined that the required skills for the desired position are proficiency in Java programming, familiarity with the Spring framework, and familiarity with database operations. The required work experience is more than 2 years of Java backend development experience, and experience in high-concurrency project development is preferred.

[0050] Based on this, we can search the question database for a professional question library corresponding to the above-mentioned Internet company, and filter the question templates according to the industry and function of the desired position. For example, we can filter out the question templates that do not match at least one of the functions of the Internet industry or back-end development, and retain the interview question templates that match both the Internet industry and back-end development functions, and obtain a preliminary prediction question template.

[0051] After determining the template for the predicted topic, the next step is to generate specific interview questions in steps 203 and 204 in combination with the content required for the desired position.

[0052] For example, if the desired position requires certain programming skills (such as Java) and the job seeker has some programming experience, then the question "Please describe your experience of using Java to solve complex problems" will be generated. If the job seeker's experience is not enough to meet the requirements of the desired position, then the question "How do you prepare for this position without certain experience?" can be generated to assess their learning ability and strategies to make up for the deficiencies.

[0053] For example, the prediction template is a template that uses a certain skill to complete a question, comparing the work skills in the job seeker's resume with the skill requirements of the desired position. For example, the job seeker lacks understanding of the Spring framework, and the application skills of the Spring framework do not meet the skill requirement of "familiarity with the Spring framework" required for the desired position. Therefore, the prediction template can be updated to "Describe how to implement dependency injection in Spring" to obtain a question about work skills.

[0054] The prediction templates include templates for those with insufficient experience, such as templates related to how to complete a work project when one lacks experience. For example, if the job seeker has one year of work experience and the experience of each high-concurrency project does not meet the time requirement of two days for the desired position, the template can be updated to "Describe how to optimize database access in a high-concurrency scenario" or "You are prepared to take on this position without experience in handling high-concurrency scenarios", etc., to obtain a topic about work experience.

[0055] After the predicted topic template is updated, questions about work skills and work experience can be obtained, involving step 205, and questions are assembled based on the updated predicted topic template to obtain multiple interview questions for the desired position, for example, interview questions for Java backend development engineers.

[0056] According to the embodiment of the present application, by matching the industry and function, the prediction template is made consistent with the functional requirements of the expected position, thereby improving the accuracy of the prediction direction. By combining the historical behavior of the recruitment subject with the requirements of the expected position, questions that are closer to real interviews are provided to improve the effectiveness of the job seeker's interview preparation. In the process of generating specific questions, the prediction template is dynamically updated in combination with the skills and experience in the job seeker's resume, thereby providing more targeted interview questions for the job seeker's possible deficiencies, especially in the case of lack of relevant experience or skills, which is conducive to helping users identify and make up for shortcomings and improve the effectiveness of their interview preparation. Job seekers can understand the interview style and key content of the interview process of the first recruitment subject through interview question training, which is conducive to getting better interview performance in real interviews and improving the success rate of interviews.

[0057] In some embodiments of the present application, the interview process may include a first interview process and a second interview process; based on this, multiple interview questions for the desired position are obtained based on the updated predicted topic template, and the steps for simulating the interview process of the first recruitment entity may specifically include: obtaining multiple first interview questions about work skills based on the predicted topic template updated based on the work skills required for the desired position; simulating the first interview process of the first recruitment entity based on the first interview questions; obtaining the job seeker's answer information in the first interview process, and generating evaluation results of the answer information through a question evaluation model, wherein different evaluation results are associated with different difficulty levels of the second interview process.

[0058] According to the difficulty level associated with the evaluation result, the prompt information in the question about work experience in the prediction template is adjusted to obtain multiple second interview questions about work experience, wherein the difficulty level is positively correlated with the amount of prompt information; and the second interview process of the first recruitment entity is simulated based on the second interview questions.

[0059] The first interview question is related to work skills, and is mainly used to examine the job seeker's knowledge and ability in work skills, etc.

[0060] The first interview process of the first recruitment subject is simulated based on the first interview question, and the first interview question can be displayed in the interactive interface provided by the terminal device to simulate the first interview process. The job seeker answers the first interview question in the simulated first interview process, and the job seeker's answer information in the first interview process may include the answering time, specific answer content, etc.

[0061] By using the pre-trained answer evaluation model, the accuracy of the answer content, the fluency of the answer process, the degree of technical mastery, etc. can be analyzed to generate evaluation results. The evaluation results can be based on high, medium, low grades, or other grades, which are not specifically limited here.

[0062] The evaluation result is related to the difficulty level of the subsequent second interview process. For example, when the evaluation result is "high", the difficulty of the questions in the second interview process will increase; when the evaluation result is "low", the questions will contain more prompt information to reduce the difficulty of the questions.

[0063] For example, if the evaluation result is "low", you can add hints about work experience in the question. For example, if the original second interview question is "Describe how to optimize database access in high-concurrency scenarios", add a hint of "using Redis" and adjust the question to "How to use Redis to optimize database access in high-concurrency scenarios". If the evaluation result is "high", the hints may be more concise or completely omitted to increase the challenge of the question.

[0064] Optionally, when adjusting the prompt information of the second interview question, additional prompts on how to organize language are provided for job seekers with weaker expressive ability; suggestions on problem decomposition are provided for job seekers with weaker logical ability; and prompts on how to demonstrate skills in conjunction with actual projects are provided for users with stronger skills but less experience.

[0065] According to the embodiment of the present application, by dynamically adjusting the prompt information in the question, the difficulty of the second interview process is adjusted accordingly, thereby flexibly providing personalized training for job seekers of different levels. In particular, by adjusting the difficulty level associated with the evaluation results, each job seeker can obtain training content that matches his or her ability, thereby improving the training efficiency and the pertinence of interview preparation. In addition, through multiple rounds of interview simulation training, job seekers can also become familiar with the interview process, which is conducive to improving the performance of job seekers in real interviews and the success rate of job hunting.

[0066] In some embodiments of the present application, the method further includes controlling the number of interview questions used in the two interview processes. Specifically, when determining the number, the following methods can be combined: according to the historical recruitment behavior of the first recruitment entity, the weights of the work skills and work experience required for the desired position are determined; according to the weights of the work skills and work experience, the number of questions about work skills in the prediction template and the number of questions about work experience in the prediction template are determined.

[0067] Specifically, based on the historical recruitment behavior of the first recruitment entity, information such as the distribution of recruitment topic types and job requirements can be obtained. Based on this information, the degree of dependence of the desired position on work skills and work experience is determined, and weights are assigned to the two. For example, for technical positions such as software engineers, the weight of work skills may be higher, and for management positions such as project managers, work experience may have a greater weight.

[0068] According to the embodiments of the present application, for each desired position, the number of work skills and work experience is dynamically determined based on weights, so that the number and content of interview questions are more in line with the actual needs of the position, thereby adapting to the characteristics of different positions, industries and job seekers, which is conducive to the flexibility and adaptability of interview question generation and improves the targeted nature of interview training.

[0069] In some embodiments of the present application, a question database stores industry question libraries corresponding to multiple specific industries, and the industry question libraries include multiple industry-specific knowledge test questions with different difficulty levels. Based on this, the method for predicting questions to assist job seekers in conducting simulated interviews may also include the following method: based on a request for an industry knowledge simulated interview, determining a first industry question library corresponding to the industry to which the desired position belongs in the question database, and identifying the industry matching degree between the user's resume information and the industry to which the desired position belongs through an industry identification model, wherein different industry matching degrees are associated with different difficulty levels; selecting industry-specific knowledge test questions corresponding to the difficulty levels associated with the industry matching degrees in the first industry question library as industry knowledge interview simulation questions, and the industry knowledge interview simulation questions are used to simulate the interview process of the industry knowledge interview.

[0070] For example, specific industries such as construction, power grid, medical, and law require special expertise. When the desired position involves a specific industry, it is often necessary to face an examination of industry knowledge. Based on the topic database provided in the embodiment of the present application, it is also possible to provide job seekers with simulated training for the interview process of industry knowledge interviews.

[0071] Specifically, the job seeker's work experience, work skills and other information are extracted from the resume information of the job seeker, and the matching degree between the job seeker and the industry of the expected position is analyzed through the industry identification model. For example, if the job seeker has rich experience in the Internet industry, but applies for a position in the medical industry, the industry matching degree is low. If the job seeker's work experience is exactly the same as the industry of the expected position, the industry matching degree is high.

[0072] Industry matching degree can be divided into high, medium, and low categories, or other categories, and there is no specific restriction here.

[0073] For example, if a job seeker has extensive experience in the Internet industry but applies for a position in the medical industry, the difficulty of the industry knowledge interview simulation questions will be reduced due to the low industry match. For example, the questions may include understanding questions about basic industry knowledge such as "Explanation of common terms in the medical industry"; if the job seeker's work experience is exactly the same as the industry of the expected position, the difficulty of the industry knowledge interview simulation questions will increase due to the high industry match, such as more professional questions such as "Preprocessing process design for medical imaging data".

[0074] According to the embodiment of the present application, by providing an industry question library corresponding to specific industries, in this way, during the mock interview, it is easy to screen out questions that are highly relevant to the job seeker's work background and the industry requirements of the desired position, thereby improving the pertinence of the mock interview, and can be especially used in cross-industry job search scenarios to help users quickly adapt to the knowledge assessment requirements of new industries. In addition, industry knowledge interview simulation questions of different difficulty levels provide training for job seekers, thereby helping job seekers consolidate basic industry knowledge and can help job seekers practice their ability to deal with difficult problems.

[0075] In some embodiments of the present application, the method also includes constructing a question data library, which may specifically include the following steps: obtaining historical recruitment questions of multiple recruiting entities, multiple positions corresponding to the historical recruitment questions, and industry labels and functional labels corresponding to each position; cross-combining the industry labels and functional labels to obtain multiple label groups; corresponding to each label group, obtaining functional information of the positions corresponding to the labels in the label group; generating interview question templates based on the functional information of the positions corresponding to the labels in the label group and the historical recruitment questions, and establishing an association between the interview question templates and the labels in the label group.

[0076] Specifically, obtain the historical recruitment titles of multiple recruitment entities, the job information corresponding to the titles, and the industry tags and functional tags associated with the positions. For example: the historical recruitment title corresponding to the software engineer position is "Please describe a solution to improve user login verification", and the recruitment corresponding to the software architect position is "How to design an efficient distributed database architecture". The industry tags associated with the positions are Internet tags, and the functional tags are software development tags and algorithm optimization tags. Cross-combine industry tags and functional tags to generate multiple tag groups. For example, Internet and software development get one tag group, and Internet and algorithm optimization get another tag group.

[0077] For each tag group, extract the functional information of the positions corresponding to the tags in the tag group. For example, for the tag group of Internet and software development, the functional information of the positions in the group includes programming skills, system design capabilities, algorithm optimization, etc. Design interview question templates based on the functional information in the tag group and historical recruitment questions. For the tag group of Internet and software development, generate skill-related templates, such as templates related to using a certain skill to complete a problem. Among them, the template can be filled in with skills required by the Internet industry, as well as technical problems that may exist in the Internet industry. It is also possible to generate experience-related templates, such as templates related to how to complete a work project with insufficient experience.

[0078] Optionally, when generating an interview question template, natural language processing (NLP) technology can be used to analyze the job description and recruitment question text to automatically identify core information related to the tag group, thereby improving the accuracy of associating the tag with the question template.

[0079] Each generated interview question template is associated with the tag group to which it belongs and stored in the question database. The associated tags include the industry tag "Internet" and the functional tag "software development". By closely associating the generated interview question template with the tag, the question content is highly matched with the industry and functional requirements of the position, thereby improving the pertinence of the simulated interview. In the process of storing the interview question template, the tag information is combined for storage, so as to reduce the interference between irrelevant information and the desired position in the subsequent generation of interview questions.

[0080] According to the embodiment of the present application, based on the combination of historical recruitment topics and job function information, a topic template can be quickly generated, which can reduce the difficulty of maintaining the topic database and the workload of manually designing topics. Based on this, it is convenient to regularly check new historical recruitment data, identify new positions or adjust job function information, and add the generated new topic template to the corresponding industry topic library to keep the topic library dynamically updated, so that the topic database can adapt to changes in company and industry requirements in a timely manner and provide better services for job seekers.

[0081] In addition, in some embodiments of the present application, when the position corresponding to the historical recruitment title belongs to a specific industry, the interview question template generated based on the functional information of the position and the historical recruitment title is added to the industry question library corresponding to the specific industry.

[0082] Based on this, the question banks of different recruitment entities are stored separately to avoid mistakenly using one company's historical questions for another company's interview preparation, so as to ensure the legality of the question content and compliance of its use.

[0083] The implementation and advantages of the embodiments of the present application are described above through multiple embodiments. The method for predicting topics for assisting job seekers in conducting simulated interviews in the embodiments of the present application can be implemented based on the predicting topic system for assisting job seekers in conducting simulated interviews. Figure 3 A topic prediction system for assisting job seekers in conducting simulated interviews is provided in an embodiment of the present application. The topic prediction system for assisting job seekers in conducting simulated interviews may include multiple different modules for information collection and information processing.

[0084] Exemplary, combined Figure 3 As shown, the job seeker information processing module 110 is oriented to job seekers and can receive core information filled in by job seekers, such as basic personal information, educational experience, work experience, project experience, job search intentions, and other information.

[0085] Continue to combine Figure 3As shown, the topic prediction system for assisting job seekers in conducting simulated interviews may also include an interview function topic prediction module 120 . Specifically, the intelligent question bank module 120 may include an information collection module 130 and a question bank training and generation module 140 .

[0086] The information collection module 130 can be used as a basic information database to process the collected information and store massive amounts of information.

[0087] Specifically, the information collection module 130 may include a job seeker resume module 131 , a job seeker job intention module 132 , a function classification module 133 , an industry classification module 134 , a company information module 135 and a recruitment position information module 136 .

[0088] The job seeker resume module 131 can obtain resume information from the job seeker information processing module 110 and store and display it, including but not limited to basic personal information, education experience, work experience, project experience, etc.

[0089] The job seeker's job seeking intention module 132 can obtain job seeking intention information from the job seeker information processing module 110 for storage and display, including the job seeking intention corresponding to the desired position's function and industry, wherein the function can be determined based on the function classification module 133, and the industry can be determined based on the industry classification module 134.

[0090] The function classification module 133 can record and store the function information of each published position, and classify the function information. After determining the function category, the function category label can be associated with the function information. For example, the function category can be divided into two levels: major categories and minor categories. Major categories include Internet technology, sales, personnel administration, finance, production and manufacturing, etc. Minor categories include Java, C#, PHP, etc. under Internet technology; and electromechanical engineers, welding engineers, etc. under production and manufacturing.

[0091] The industry classification module 134 can record and store the industry information of each published position. After determining the position category, the category label of the industry can be associated with the job information. The industry category can be divided into two levels: major categories and minor categories. Major categories include computers, Internet, communications, electronics, accounting, finance, banking, insurance, trade, consumption, manufacturing, advertising, media, etc. Minor categories include Internet, e-commerce, online games, computer hardware, etc. under the computer, Internet, communications, and electronics industries.

[0092] The company information module 135 can store a large amount of company information, including company name, introduction, size, industry, etc.

[0093] The job information module 136 can store a large amount of job information and is associated with the company information module 135 .

[0094] Combination Figure 3 As shown, the question bank training and generation module 140 may include a question bank data module 141 , a question bank training module 142 , and an intelligent question bank module 143 .

[0095] The question bank data module 141 may include a basic data module 141-1, a general question bank module 41-2, and an answer and feedback module 141-3. Specifically, the basic data module 141-1 may extract data from the information acquisition module 130, and the data may be cleaned, classified, labeled, and stored through the data preprocessing module 144-1.

[0096] The general question bank module 141-2 is a pre-designed standard interview question bank, each question corresponds to the functional classification module 141. The general question bank module can be further divided into a general question bank, a professional question bank, and an additional question bank.

[0097] The general question bank can include common questions in interviews, such as self-ending, personal experience description, personal strengths and weaknesses, etc.; the professional question bank can include corresponding to the functional module, and each question is associated with the functional category in the functional module. Optionally, each professional question can be set with a difficulty level, such as level 1, level 2, level 3 and other levels. When generating simulated interview questions, the difficulty of the question can be determined according to the actual situation of the job seeker. It is also possible to use questions of different difficulty levels at different interview stages, such as the difficulty of professional questions in the first interview is level 1-3, the difficulty of professional questions in the second interview is level 4-8, etc. The specific difficulty can be flexibly adjusted according to the job seeker or the interview scenario. The additional question bank can include classifications including personality tests, expressions and other questions.

[0098] The answer and feedback module 141 - 3 can obtain the answers and feedback of job seekers from the question bank display module 150 and store them for use in the question bank generation and optimization module 142 - 4 .

[0099] The question bank training module 142 can be divided into a data preprocessing module 142 - 1 , a feature extraction module 142 - 2 , a model building module 142 - 3 , and a question bank generation and optimization module 142 - 4 .

[0100] The data preprocessing module 142 - 1 can obtain the latest data from the information collection module 130 , the general question bank module 141 - 2 , and the answer and feedback module 141 - 3 , and enter the basic data module 141 - 1 after processing.

[0101] Exemplarily, the data preprocessing module 142-1 obtains a large amount of training data from the information acquisition module 130 and performs data cleaning to remove duplicate, incomplete or irrelevant data to improve the quality of the data. The data preprocessing module 142-1 can also perform data integration processing, for example, integrating data from different sources into a unified data warehouse of the training data module 145, such as job requirement data, job seeker resume data, etc. The data preprocessing module 142-1 can also perform data labeling processing, for example, classifying and labeling the data after integration, and storing them in the functional classification module 133 or the industry classification module 134 according to the specific categories corresponding to the data.

[0102] The feature extraction module 142-2 can extract the features of the position and the job seeker's features, for example, extract key information such as the industry, function, skill requirements, experience requirements, etc. of the position from each module 133-136, and extract the job seeker's educational background, work experience, skills, job search intentions, etc.

[0103] The model building module 142-3 can perform job matching and use machine learning algorithms such as decision trees, random forests, gradient boosting machines, etc. to determine suitable positions for job seekers. The model building module 142-3 can generate interview question templates by combining historical recruitment questions, industry labels and functional labels corresponding to each position in the historical recruitment questions.

[0104] The model building module 142-3 can perform weight management, for example, determining the weights of the characteristics of each function corresponding to the position. The advantage of this approach is that important characteristics, such as professional skills, can occupy a larger proportion in the interview questions.

[0105] The question bank generation and optimization module 142-4 can generate interview questions, and obtain feedback information to optimize the questions in the question bank data module 141. During the mock interview, the interview questions for the mock interview are generated specifically according to the characteristics of the position. The specific generation process can refer to the introduction of the method for assisting job seekers in mock interviews provided in the embodiment of the present application, which will not be repeated here. In the process of generating questions, machine learning can also be combined to generate questions. For example, after training, the text generation model based on natural language processing (NLP) technology can generate interview questions according to the characteristics of job seekers and job requirements.

[0106] The question bank generation and optimization module 142-4 can obtain the job seeker's answer feedback information from the answer and feedback module 141-3, evaluate the rationality and effect data of the interview questions, for example, regularly obtain the latest data from the basic data module 141-1 for iteration, so that the interview question template can quickly respond to changes in market demand and improve the accuracy and reliability of the interview questions for job seekers.

[0107] The question bank training and generation module 140 may also include an intelligent question bank module 143 , which may store interview questions generated by the question bank training module 142 and provided to job seekers.

[0108] The prediction system may further include a question bank display module 150, which may obtain interview questions from the intelligent question bank module 142. Job seekers may conduct interview simulations through the job seeker information processing module 110. Managers may manually retrieve and modify interview questions by industry, function, job level, etc. through the manager module 170.

[0109] The topic prediction system may further include a recruiter information processing module 160 , through which the recruiter manages and maintains the company information module 135 and the recruitment position information module 136 .

[0110] The prediction system may further include a management personnel module 170 . Management personnel or maintenance personnel of the prediction system may manage and maintain the function classification module 133 , the industry classification module 134 , the general question bank module 141 , and the model building module 142 - 3 .

[0111] This application example also provides a device for assisting job seekers in conducting simulated interviews. Figure 4 As shown, a question database 401 pre-processed by the device for predicting questions for assisting job seekers in conducting simulated interviews, wherein the question database stores a plurality of professional question libraries, wherein different professional question libraries correspond to different recruitment entities, and each professional question library includes one or more interview question templates generated based on the historical recruitment questions of the recruitment entity, and each interview question template has at least two tags, wherein the at least two tags include an industry tag and a function tag.

[0112] The device also includes: a data processing unit 402, a topic prediction unit 403 and an interview simulation unit 404.

[0113] The data processing unit 402 is used to determine the job seeker's desired position and the industry to which the desired position belongs, the corresponding function and at least the corresponding first recruiting entity based on the job seeker's job-seeking intention and / or resume information, and determine the work skills and work experience required for the desired position based on the historical recruitment behavior of the first recruiting entity.

[0114] The data processing unit 402 is also used to determine a first professional question bank corresponding to the first recruiting entity in the question database, and screen multiple interview question templates in the first professional question bank. During the screening, the interview question templates that do not match at least one of the industry and functions of the expected position will be filtered out, and the interview question templates that match both the industry and functions of the expected position will be retained and determined as the predicted question templates.

[0115] The prediction unit 403 is used to update the prediction template according to the work skills required for the desired position. During the update, if the work skills in the resume information of the job seeker do not meet the work skills required for the desired position, at least one question about work skills is added to the prediction template.

[0116] The prediction unit 403 is also used to update the prediction template according to the work experience required for the desired position. During the update, if the work experience in the resume information of the job seeker does not meet the work experience required for the desired position, at least one question about work experience is added to the prediction template.

[0117] The interview simulation unit 404 is used to compose questions based on the updated predicted question template to obtain multiple interview questions for the desired position, which are used to simulate the interview process of the first recruitment entity.

[0118] Optionally, in some embodiments of the present application, the interview process includes a first interview process and a second interview process. The interview simulation unit 404 is also used to obtain a plurality of first interview questions about work skills based on the prediction template updated based on the work skills required for the desired position. The interview simulation unit 404 is also used to simulate the first interview process of the first recruitment subject based on the first interview questions. The interview simulation unit 404 is also used to obtain the answer information of the job seeker in the first interview process, and generate an evaluation result of the answer information through the answer evaluation model, wherein different evaluation results are associated with different difficulty levels of the second interview process. The interview simulation unit 404 is also used to adjust the prompt information in the question about work experience in the prediction template according to the difficulty level associated with the evaluation result, and obtain a plurality of second interview questions about work experience, wherein the difficulty level is positively correlated with the amount of prompt information. The interview simulation unit 404 is also used to simulate the second interview process of the first recruitment subject based on the second interview questions.

[0119] Optionally, in some embodiments of the present application, the prediction unit 403 is further used to determine the weights corresponding to the work skills and work experience required for the desired position based on the historical recruitment behavior of the first recruitment entity. The prediction unit 403 is also used to determine the number of questions about work skills in the prediction template and the number of questions about work experience in the prediction template based on the weights corresponding to the work skills and work experience, respectively.

[0120] Optionally, in some embodiments of the present application, the question database stores industry question libraries corresponding to multiple specific industries, and the industry question libraries include multiple industry-specific knowledge test questions with different difficulty levels. The question prediction unit 403 is also used to determine the first industry question library corresponding to the industry to which the desired position belongs in the question database based on the request for the industry knowledge simulation interview, and to identify the industry matching degree between the user's resume information and the industry to which the desired position belongs through the industry identification model, wherein different industry matching degrees are associated with different difficulty levels. The question prediction unit 403 is also used to screen industry-specific knowledge test questions corresponding to the difficulty level associated with the industry matching degree in the first industry question library as industry knowledge interview simulation questions, and the industry knowledge interview simulation questions are used to simulate the interview process of the industry knowledge interview.

[0121] Optionally, in some embodiments of the present application, the data processing unit 402 is further used to obtain historical recruitment titles of multiple recruiting entities, multiple positions corresponding to the historical recruitment titles, and industry labels and functional labels corresponding to each position. The data processing unit 402 is also used to cross-combine industry labels and functional labels to obtain multiple label groups. The data processing unit 402 is also used to obtain functional information of positions corresponding to the labels in the label group for each label group. The data processing unit 402 is also used to generate an interview question template based on the functional information of the positions corresponding to the labels in the label group and the historical recruitment titles, and to establish an association between the interview question template and the labels in the label group.

[0122] Optionally, in some embodiments of the present application, the data processing unit 402 is further used to add the interview question template generated based on the functional information of the position and the historical recruitment question to the industry question library corresponding to the specific industry when the position corresponding to the historical recruitment question belongs to a specific industry.

[0123] It can be understood that the topic prediction device for assisting job seekers in conducting simulated interviews in the embodiment of the present application can correspond to the execution entity of the topic prediction method for assisting job seekers in conducting simulated interviews provided in the embodiment of the present application. The specific details of the operation and / or function of each module / unit of the topic prediction device for assisting job seekers in conducting simulated interviews can be found in the description of the corresponding parts of the topic prediction method for assisting job seekers in conducting simulated interviews provided in the above-mentioned embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0124] The electronic device in the embodiment of the present application may be a user terminal device, a server, other computing devices, or a cloud server. Figure 5 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application is shown. The electronic device may include a processor 501 and a memory 502 storing computer program instructions. When the processor 501 executes the computer program instructions, the process or function of any of the above-mentioned embodiments of the method is implemented.

[0125] Specifically, the processor 501 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application. The memory 502 may include a large-capacity memory for data or instructions. For example, the memory 502 may be at least one of the following: a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, a universal serial bus (USB) drive, or other physical / tangible memory storage devices. For another example, the memory 502 may include a removable or non-removable (or fixed) medium. For another example, the memory 502 may be inside or outside the integrated gateway disaster recovery device. The memory 502 may be a non-volatile solid-state memory. In other words, the memory 502 generally includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with computer-executable instructions, and when the software is executed (such as by one or more processors), the operations described in the method of the embodiments of the present application may be performed. The processor 501 implements the process or function of any method in the above embodiments by reading and executing the computer program instructions stored in the memory 502 .

[0126] In one example, Figure 5 The electronic device shown may also include a communication interface 503 and a bus 510. Among them, the processor 501, the memory 502, and the communication interface 503 are connected through the bus 510 and complete the communication between each other. The communication interface 503 is mainly used to realize the communication between each module, device, unit and / or device in the embodiment of the present application. The bus 510 includes hardware, software or both, and can couple the components of the online data traffic billing device to each other. For example, the bus may include at least one of the following: an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-end bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a micro channel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable buses. The bus 510 may include one or more buses. Although the embodiments of the present application describe or illustrate a specific bus, the embodiments of the present application may consider any suitable bus or interconnection method.

[0127] In combination with the method in the above embodiments, an embodiment of the present application also provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the process or function of any method in the above embodiments is implemented.

[0128] In addition, an embodiment of the present application further provides a computer program product, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the process or function of any one of the methods in the above embodiments is implemented.

[0129] The above exemplarily describes the flowcharts and / or block diagrams of the methods, devices, systems and computer program products of the embodiments of the present application, and describes the relevant various aspects. It should be understood that each box or combination thereof in the flowchart and / or block diagram can be implemented by computer program instructions, or by dedicated hardware that performs a specified function or action, or by a combination of dedicated hardware and computer instructions. For example, these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to form a machine that enables these instructions executed by such a processor to enable the implementation of the functions / actions specified in each box or combination thereof in the flowchart and / or block diagram. Such a processor can be a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.

[0130] The functional blocks shown in the structured block diagram of the embodiment of the present application can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in a memory, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0131] It should be noted that the present application is not limited to the specific configurations and processes described above or shown in the figures. The above is only a specific implementation mode of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described system, device, module or unit can refer to the corresponding process in the method embodiment without further description. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with the technical field can think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should be included in the scope of protection of the present application.

Claims

1. A method for predicting questions for assisting job seekers in conducting simulated interviews, characterized in that: The method is performed based on a preprocessed topic database, wherein: The question database stores multiple professional question libraries, and different professional question libraries correspond to different recruitment entities. Each professional question library includes one or more interview question templates generated according to the historical recruitment questions of the recruitment entity, and each interview question template has at least two tags, and the at least two tags include an industry tag and a function tag; The method comprises: Determine the job seeker's desired position and the industry, corresponding function and at least a first corresponding recruiting entity according to the job seeker's job seeking intention and / or resume information, and determine the job skills and work experience required for the desired position based on the first recruiting entity's historical recruiting behavior; Determining a first professional topic library corresponding to the first recruitment entity in the topic database, screening multiple interview topic templates in the first professional topic library, filtering out interview topic templates that do not match at least one of the industry and function of the desired position during screening, retaining interview topic templates that match both the industry and function of the desired position, and determining them as predicted topic templates; The prediction template is updated according to the job skills required for the desired position. During the update, if the job skills in the resume information of the job seeker do not meet the job skills required for the desired position, at least one question about the job skills is added to the prediction template; The prediction template is updated according to the work experience required for the desired position. During the update, if the work experience in the resume information of the job seeker does not meet the work experience required for the desired position, at least one question about work experience is added to the prediction template; Based on the updated predicted topic template, multiple interview questions for the desired position are obtained to simulate the interview process of the first recruitment entity.

2. The method according to claim 1, characterized in that The interview process includes a first interview process and a second interview process; The step of forming questions based on the updated predicted question template to obtain a plurality of interview questions for the desired position, which are used to simulate the interview process of the first recruitment entity, includes: Based on the updated prediction template of the job skills required for the desired position, a plurality of first interview questions about job skills are obtained; Simulating a first interview process of the first recruitment subject based on the first interview questions; Acquire the answer information of the job seeker in the first interview process, and generate an evaluation result of the answer information through an answer evaluation model, wherein different evaluation results are associated with different difficulty levels of the second interview process; According to the difficulty level associated with the evaluation result, the prompt information in the question about work experience in the prediction template is adjusted to obtain a plurality of second interview questions about work experience, wherein the difficulty level is positively correlated with the amount of the prompt information; A second interview process of the first recruitment entity is simulated based on the second interview questions.

3. The method according to claim 1, characterized in that The method further comprises: Determining weights corresponding to the job skills and work experience required for the desired position based on the historical recruitment behavior of the first recruitment entity; According to the weights corresponding to the work skills and the work experience respectively, the number of questions about work skills in the topic prediction template and the number of questions about work experience in the topic prediction template are determined.

4. The method according to claim 1, characterized in that: The question database stores industry question libraries corresponding to multiple specific industries, and the industry question libraries include multiple industry-specific knowledge examination questions with different difficulty levels; The method further comprises: Based on the request for the industry knowledge simulation interview, determine a first industry topic library corresponding to the industry to which the desired position belongs in the topic database, and identify the industry matching degree between the resume information of the user and the industry to which the desired position belongs through an industry identification model, wherein different industry matching degrees are associated with different difficulty levels; Industry-specific knowledge examination questions corresponding to the difficulty level associated with the industry matching degree are selected from the first industry question database as industry knowledge interview simulation questions, and the industry knowledge interview simulation questions are used to simulate the interview process of the industry knowledge interview.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining historical recruitment titles of multiple recruitment entities, multiple positions corresponding to the historical recruitment titles, and industry labels and function labels corresponding to each position; Cross-combining the industry labels and the functional labels to obtain multiple label groups; Corresponding to each of the tag groups, obtaining the functional information of the positions corresponding to the tags in the tag group; An interview question template is generated based on the functional information of the position corresponding to the tag in the tag group and the historical recruitment questions, and an association between the interview question template and the tag in the tag group is established.

6. The method according to claim 5, characterized in that The method further comprises: In the case that the position corresponding to the historical recruitment title belongs to a specific industry, the interview question template generated based on the functional information of the position and the historical recruitment title is added to the industry question library corresponding to the specific industry.

7. A method for assisting job seekers in conducting mock interviews, characterized in that: The device includes a pre-processed question database, wherein the question database stores a plurality of professional question libraries, wherein different professional question libraries correspond to different recruitment entities, and each professional question library includes one or more interview question templates generated according to historical recruitment questions of the recruitment entity, and each interview question template has at least two tags, wherein the at least two tags include an industry tag and a function tag; The device also includes: A data processing unit, configured to determine the job seeker's desired position, the industry to which the desired position belongs, the corresponding function, and at least a first corresponding recruiting entity according to the job seeker's job seeking intention and / or resume information, and determine the work skills and work experience required for the desired position based on the first recruiting entity's historical recruiting behavior; The data processing unit is further used to determine a first professional topic library corresponding to the first recruitment entity in the topic database, and screen multiple interview topic templates in the first professional topic library, filtering out interview topic templates that do not match at least one of the industry and function of the desired position during the screening, and retaining interview topic templates that match both the industry and function of the desired position and determining them as predicted topic templates; A prediction unit is used to update the prediction template according to the job skills required for the desired position. During the update, if the job skills in the resume information of the job seeker do not meet the job skills required for the desired position, at least one question about the job skills is added to the prediction template; The prediction unit is further configured to update the prediction template according to the work experience required for the desired position. During the update, if the work experience in the resume information of the job seeker does not meet the work experience required for the desired position, at least one question about work experience is added to the prediction template. The interview simulation unit is used to compose questions based on the updated predicted question template to obtain multiple interview questions for the desired position, which are used to simulate the interview process of the first recruitment entity.

8. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that It comprises computer program instructions, which implement the method according to any one of claims 1 to 6 when executed by a processor.

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