Interview method and device, storage medium and computer program product

Abstract

The embodiment of the invention provides an interview method and device, a storage medium and a computer program product. In the embodiment of the invention, in response to an interview request initiated by an interviewer, a post type applied by the interviewer can be determined, and resume data and historical interview data of the interviewer and an investigation dimension needing to be concerned under the post type are provided for a first large model, so that the first large model can perform interview according to first prompt information. And generating an interview question adapted to the investigation dimensions for the interviewer, and providing the interview question for the interviewer to guide the interviewer to provide key information under each investigation dimension corresponding to the post type. Therefore, on the basis of the resume data and the historical interview data of the interviewer and the investigation dimension needing to be concerned by the post type, the actual requirements of the interviewer and the recruiter can be comprehensively considered, and the interview problem is generated for the interviewer in real time, so that the interview problem related to the post type is more in line with the actual situation of the interviewer, and the interview efficiency is improved. Therefore, the interview experience of the interviewer can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to an interview method, device, storage medium and computer program product. Background Art

[0002] AI interview, or artificial intelligence interview, is a new type of interview method that has emerged with the rapid development of artificial intelligence technology and the changing recruitment needs. It uses artificial intelligence technology to conduct automated interview assessments of job seekers through preset algorithms and data analysis models.

[0003] At present, AI interviews often adopt standardized questioning methods and lack personalized adjustments for different job seekers. This mechanized questioning method may cause some questions to not match the interviewee's actual experience and skills, thus affecting the interviewee's interview experience. Summary of the invention

[0004] Multiple aspects of the present application provide an interview method, device, storage medium and computer program product to improve the interview experience of the interviewer during the interview process.

[0005] The present application embodiment provides an interview method, including:

[0006] In response to an interview request initiated by an interviewer, determining the type of position applied for by the interviewer;

[0007] Obtain the assessment dimensions that need to be focused on under the position type;

[0008] Obtaining the resume data and historical interview data of the interviewee;

[0009] The inspection dimension, the resume data and the historical interview data are constructed as input data of the first large model;

[0010] Obtaining preset first prompt information, wherein the first prompt information is used to instruct the first large model to generate interview questions adapted to the examination dimension according to the resume data and the historical interview data in the input data, wherein the first prompt information also includes resume data examples, historical interview data examples, examination dimension examples, and corresponding interview question examples;

[0011] providing the input data and the first prompt information to the first large model to generate interview questions for the interview request using the first large model;

[0012] The interview questions are provided to the interviewee in the form of a dialogue to guide the interviewee to provide key information under each assessment dimension corresponding to the position type.

[0013] The embodiment of the present application also provides a computing device, including: a memory, a processor, and a communication component;

[0014] The memory is used to store one or more computer instructions;

[0015] The processor is coupled to the memory and the communication component, and is configured to execute the one or more computer instructions to perform the aforementioned interview method.

[0016] An embodiment of the present application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed by one or more processors, the one or more processors are caused to execute the aforementioned interview method.

[0017] An embodiment of the present application also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the processor is caused to execute the aforementioned interview method.

[0018] In an embodiment of the present application, in response to an interview request initiated by an interviewee, the type of position applied for by the interviewee can be determined, and the examination dimensions that need to be paid attention to under the position type can be obtained. The interviewee's resume data and historical interview data are obtained, and the examination dimensions, resume data, and historical interview data are provided to the first large model, so that the first large model can generate interview questions adapted to the examination dimensions for the interviewee according to the first prompt information, and the interview questions are provided to the interviewee to guide the interviewee to provide key information under each examination dimension for the position type. In this way, based on the interviewee's resume data and historical interview data, and the examination dimensions that need to be paid attention to under the position type, the actual needs of both the interviewer and the recruiter can be comprehensively considered, and interview questions can be generated for the interviewee in real time, so that the interview questions related to the position type are more in line with the interviewee's actual situation, thereby improving the interview experience of the interviewee. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A system architecture diagram is provided for an exemplary embodiment of the present application;

[0021] Figure 2 A flowchart of an interview method provided for an exemplary embodiment of the present application;

[0022] Figure 3 A schematic diagram of a process for extracting key information provided by another exemplary embodiment of the present application;

[0023] Figure 4A schematic diagram of a flow chart for generating follow-up questions provided by another exemplary embodiment of the present application;

[0024] Figure 5 A logic diagram of an interview in an exemplary application scenario provided for another exemplary embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of a computing device is provided for yet another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0027] In the prior art, only fixed preset interview questions can be shown to the interviewee, and the interview questions that are prone to appear do not match the actual experience and skills of the interviewee, resulting in a poor interview experience for the interviewee. To improve this technical problem, the embodiment of the present application provides a solution, and the basic idea is: in response to the interview request initiated by the interviewee, determine the type of position applied for by the interviewee, and obtain the inspection dimension that needs to be paid attention to under the position type. Obtain the resume data and historical interview data of the interviewee, and provide the inspection dimension, resume data and historical interview data to the first large model, so that the first large model generates interview questions adapted to the inspection dimension for the interviewee according to the first prompt information, and provides the interview questions to the interviewee to guide the interviewee to provide key information under each inspection dimension of the position type. In this way, based on the resume data and historical interview data of the interviewee, and the inspection dimension that needs to be paid attention to by the position type, the actual needs of both the interviewer and the recruiter can be comprehensively considered, and interview questions can be generated for the interviewee in real time, so that the interview questions related to the position type are more in line with the actual situation of the interviewee, thereby improving the interview experience of the interviewee.

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0029] Figure 1A system architecture diagram is provided for an exemplary embodiment of the present application. The system architecture may include a client 101 and a server 102. A communication connection is established between the client 101 and the server 102. The communication connection method includes but is not limited to wired, wireless communication links or optical fiber cables.

[0030] Among them, the client 101 can be an APP (Application), a web application, a light application (also known as a mini-program, a lightweight application) or a cloud application, etc. The client 101 can be deployed in an electronic device and needs to rely on the electronic device to run. The electronic device may include but is not limited to mobile phones, tablet computers, personal computers, desktop computers, smart speakers, smart watches, etc. The server 102 may include servers that provide various services, such as a server for background training that supports the model used on the client 101, and a server that processes the interactive information sent by the client, etc. The server 102 can be implemented as a single server, a distributed server cluster consisting of multiple servers, a server or cloud server combined with blockchain, etc.

[0031] It should be noted that the interview questioning method provided in the embodiment of the present application is generally executed by the server 102, and the corresponding data processing device is generally arranged in the server 102. However, in other embodiments of the present application, the client 101 may also have similar functions as the server 102, so as to execute the interview questioning method provided in the embodiment of the present application. In other embodiments, the interview questioning method provided in the embodiment of the present application may also be jointly executed by the client 101 and the server 102.

[0032] The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below.

[0033] Figure 2 A flowchart of an interview method provided by an exemplary embodiment of the present application. The method can be executed by a data processing device, which can be implemented as a combination of software and / or hardware, and the data processing device can be integrated in a computing device. Figure 2 , the method comprising:

[0034] Step 200, in response to an interview request initiated by the interviewee, determining the type of position applied for by the interviewee;

[0035] Step 201, obtaining the assessment dimensions that need to be paid attention to under the position type;

[0036] Step 202, obtaining the interviewee's resume data and historical interview data;

[0037] Step 203 constructs the inspection dimension, resume data and historical interview data as input data of the first model;

[0038] Step 204, obtaining preset first prompt information, the first prompt information is used to instruct the first large model to generate interview questions adapted to the examination dimension according to the resume data and the historical interview data in the input data;

[0039] The first prompt information also includes resume data examples, historical interview data examples, assessment dimension examples, and corresponding interview question examples;

[0040] Step 205, providing the input data and the first prompt information to the first large model, so as to generate interview questions for the interview request using the first large model;

[0041] Step 206 provides interview questions to the interviewee in the form of a dialogue, so as to guide the interviewee to provide key information under each assessment dimension corresponding to the job type.

[0042] In step 200, the type of position applied for by the interviewer may be determined in response to the interview request initiated by the interviewer. Position types may be used to describe different job categories divided according to work factors, including but not limited to the nature of work, work content, work environment, scope of responsibilities, etc. Among them, according to the nature of work, the position types may include management positions, technical positions, sales positions, administrative positions, etc.; according to the work content, the position types may include front-end engineers, test engineers, back-end engineers, project managers, etc.; according to the work environment, the position types may include workshop positions, office positions, outdoor positions, etc.; according to the scope of responsibilities, the position types may include employees, team leaders, store managers, managers, etc.

[0043] Optionally, a recruitment interface may be displayed to the interviewer, which includes multiple recruitment information. In response to an interview request initiated by the interviewer on the recruitment interface, the target recruitment information corresponding to the interview request is determined, and the position type associated with the target recruitment information is used as the position type applied for by the interviewer. For example, in actual applications, multiple recruitment information may be displayed on the recruitment interface, and the recruitment information includes but is not limited to the company name, company address, position name, position description, job requirements, salary and benefits, etc.; each recruitment information is associated with a position type, and in response to the interviewer clicking the application button corresponding to the recruitment information, the position type associated with the recruitment information corresponding to the application button is obtained as the position type applied for by the interviewer. For the interviewer, it is only necessary to initiate a private chat with the recruiter of the target recruitment information according to the original interview initiation process to trigger the recruiter to generate interview questions for the interviewer according to the position type associated with the target recruitment information. The interviewer is unaware of the generation of the interview questions, which can effectively improve the interview experience of the interviewer.

[0044] In steps 201-202, the inspection dimensions that need to be paid attention to under the job type can be obtained, and the resume data and historical interview data of the interviewee can be obtained. The inspection dimension is the information dimension that the recruiter needs to focus on by definition, and the information dimension includes but is not limited to work experience, age, education, height, work location, etc. In response to the interview request of the interviewer, the identity of the interviewer can be determined, so that the resume data and historical interview data corresponding to the identity can be found from the local database according to the identity. Among them, the initiation of the interview request and the delivery of the resume are carried out synchronously, and the resume data of the interviewer can be saved in the interview database in response to the resume delivery instruction. In the process of interviewing the interviewer, the data generated during the interview process can be stored in the interview database as historical interview data, and the historical interview data of the interviewer can be empty. In addition, the interview database can store the data generated during the interview process according to the job type, and each job type contains the interview data corresponding to multiple interviewers. The interview data includes but is not limited to resume data, interview questions, answer content, key information, evaluation level, etc. These interview data can provide important references for the recruiter to make recruitment decisions.

[0045] Based on this, the inspection dimensions, resume data and historical interview data can be constructed as the input data of the first large model. The first large model is an artificial intelligence large model used to generate interview questions. Artificial intelligence large models refer to machine learning models with ultra-large-scale parameters and complex computing structures. These models can process large-scale data sets and complete various complex tasks, such as natural language processing, image recognition, speech recognition, etc. through deep learning and neural network technology.

[0046] Continue to refer Figure 2 In step 204, the preset first prompt information can be obtained, and the first prompt information is used to instruct the first large model to generate interview questions adapted to the examination dimension according to the resume data and historical interview data in the input data. The first prompt information also includes resume data examples, historical interview data examples, examination dimension examples, and corresponding interview question examples. The first prompt information is a prompt template (promt template) preset for the first large model. The prompt template is a text template used to guide the large model to generate specific outputs or perform specific tasks. The prompt template can guide the model to generate specific outputs that meet the requirements by providing detailed context, instructions or questions to the model.

[0047] For example, the first prompt information preset for the first large model may be:

[0048] "You now need to generate interview questions for the interviewee. Please generate interview questions that match the dimension based on the basic (resume data) and data (historical interview data) in the input."

[0049] “Requirements: 1. The questions you output must be reliable, compliant, and objective. Content that violates laws and regulations is not allowed.

[0050] 2. Output of other irrelevant results is not allowed. "

[0051] Please output the corresponding questions according to the following case examples and requirements:

[0052] "case1:'input:{"basic":"Zhang San, male, worked in a software company for two years","data":"None","dimension":"Work experience"}'

[0053] 'output: What kind of work have you done before? '"

[0054] "case2:'input:{"basic":"Wang Yi, male, hardware engineer, three years of work experience","data":"None","dimension":"Work location"}'

[0055] 'output: The job location for this position is in Beijing, is this acceptable to you? '"

[0056] "case3:'input:{"basic":"Li Si, female, 25 years old, senior sales","data":"question: What kind of working hours do you accept, answer: work six days and rest one day","dimension":"working hours"}'

[0057] 'output: May I ask how many years you have worked in this industry? '"

[0058] "Please complete your inquiry according to the above case examples and requirements. No other irrelevant results are allowed."

[0059] On this basis, the input data and the first prompt information are provided to the first large model, so as to generate interview questions for the interview request using the first large model. The specific implementation process of generating interview questions using the first large model will be described in detail below, and will not be elaborated here.

[0060] In step 206, the interview questions are provided to the interviewee in the form of a dialogue to guide the interviewee to provide key information under each assessment dimension corresponding to the job type. In the process of interviewing the interviewee, multiple interview questions can be generated for the interviewee, and there is a display order between the multiple interview questions. During the interview process, dialogues can be opened for multiple interview questions in turn according to the display order to conduct multiple rounds of dialogue with the interviewee. In addition, the display order of post-disaster interview questions can be flexibly adjusted according to the answers to the previous interview questions.

[0061] Optionally, when multiple interview questions are generated for an interview request, the display order of the multiple interview questions can be obtained. For any interview question, key information can be extracted from the answer to the interview question. If the extracted key information contains key information required for subsequent interview questions, the display of subsequent interview questions is deleted from the display order. In this way, the display order of interview questions can be adjusted in real time according to the interviewee's answer content during the interview process, so that the connection between interview questions is smoother, and repeated questions can be avoided to cause the interviewee's disgust, thereby effectively improving the interviewee's interview experience.

[0062] Accordingly, in this embodiment, the assessment dimensions that need to be paid attention to under the job type are obtained according to the job type applied for by the interviewee, and the obtained assessment dimensions are the assessment dimensions for the interviewee. Based on the assessment dimensions for the interviewee, the interviewee's resume data, and the interviewee's historical interview data, at least one interview question adapted to the assessment dimensions is generated for the interviewee in real time, so as to guide the interviewee to provide key information under each assessment dimension corresponding to the job type through the interview questions. In this way, according to the actual situation of each interviewee, corresponding interview questions are customized for each interviewee, which can effectively reduce the occurrence of mismatches between interview questions and the interviewee's actual experience or skills, so that the interview questions provided to the interviewee are more in line with the interviewee's actual situation, thereby effectively improving the interview experience of the interviewee.

[0063] In the above or following embodiments, a variety of implementation methods may be used to generate interview questions for interview requests. In an optional implementation method, the step of generating interview questions for interview requests in the first large model may include: extracting basic information of the interviewee from the interviewee's resume data and historical interview data. Based on the basic information of the interviewee, interview questions are constructed for the interviewee according to the assessment dimensions that need to be paid attention to under the job type. This comprehensively considers the actual needs of both the interviewer and the recruiter, generates interview questions for the interviewee in real time, and makes the interview questions related to the job type more in line with the interviewee's actual situation, thereby improving the interview experience of the interviewee.

[0064] Optionally, in the process of constructing interview questions for the interviewee based on the basic information of the interviewee and the examination dimensions that need to be paid attention to under the job type, the steps performed by the first large model may include: based on the basic information of the interviewee, constructing interview questions corresponding to the examination dimensions for each examination dimension as interview questions constructed for the interviewee. Alternatively, the first large model is used to determine the correlation between multiple examination dimensions based on the examination dimensions that need to be paid attention to under the job type, and at least one examination dimension group is obtained; based on the basic information of the interviewee, constructing interview questions corresponding to the examination dimension group for each examination dimension group as interview questions constructed for the interviewee. In the process of generating interview questions, corresponding interview questions can be generated for each examination dimension, or one interview question can be generated for multiple examination dimensions. The first model can flexibly adjust the generation strategy of interview questions according to the examination dimensions and the basic information of the interviewee, which greatly improves the flexibility of generating interview questions, thereby further improving the generation efficiency of interview questions.

[0065] Of course, other implementations may also be used to generate interview questions for an interview request, which is not limited in this embodiment.

[0066] In this embodiment, the first large model can also be fine-tuned in real time: the assessment dimensions and historical interview records under any job type can be obtained, and for any interviewee in the historical interview records, the interviewee's resume information, historical interview data, and interview questions that are adapted to each assessment dimension can be obtained. The interviewee's resume data, the interviewee's historical interview data, the assessment dimensions under the job type, the interview questions that are adapted to each assessment dimension, and the first prompt information are constructed as a data sample, and the data sample is input into the first large model to train the first large model, thereby achieving fine-tuning of the first large model. In the process of fine-tuning the first large model, the first prompt information can be flexibly adjusted according to the performance of the first large model, data feedback, etc., so as to achieve iterative optimization of the first large model, which can effectively ensure the accuracy of the first large model, thereby effectively improving the accuracy of the generated interview questions.

[0067] After generating interview questions for the interview request, the interview questions can be provided to the interviewer. After any interview question is issued to the interviewer, the interviewer's answer status to the interview question can be monitored in real time, and different operation actions can be performed based on the monitored answer status. The monitored answer status includes but is not limited to answered status, unanswered status, etc.

[0068] When the answer status is monitored as answered, the answer content provided by the interviewee to the interview question can be obtained, and key information can be extracted from the answer content provided to the interview question. If it is determined based on the extracted key information that any key information required for the interview question is missing in the answer content, a follow-up question is constructed for the interview question, and the follow-up question is sent to the interviewee to guide the interviewee to provide the key information required for the follow-up question. When the answer content provided by the interviewee to the interview question cannot provide the recruiter with comprehensive and effective interviewee information, a follow-up question is constructed for the interview question, so that the interviewee can more accurately understand the recruiter's question intention through the follow-up question and give a more comprehensive and accurate answer, thereby further improving the efficiency of the interview.

[0069] When an unanswered state is detected, the duration of the unanswered state is recorded. If the duration of the unanswered state exceeds the preset waiting threshold, that is, no answer to the interview question is received, the interview question is sent repeatedly to the interviewee. When the number of repeated interview questions exceeds the preset repetition threshold, follow-up questions are constructed for the interview questions and sent to the interviewee to guide the interviewee to provide key information required for the follow-up questions.

[0070] In the case where no answer is received, the interview question can be sent repeatedly to give the interviewee more time to think and prepare, so that the interviewee can understand the question more deeply and give a more comprehensive answer. In addition, in order to avoid excessive repetition that may cause the interviewee to be disgusted, a repetition threshold can be preset. When the number of times the interview question is sent repeatedly exceeds the repetition threshold, the interviewee will be sent a follow-up question for the interview question, providing the interviewee with a new perspective, so that the interviewee can understand the interview intention more accurately and give a more accurate answer.

[0071] Among them, when the interview question corresponds to one examination dimension, the examination dimension corresponding to the interview question is exactly the same as the examination dimension corresponding to the follow-up question; when the interview question corresponds to multiple examination dimensions, the multiple examination dimensions corresponding to the interview question include at least one examination dimension corresponding to the follow-up question.

[0072] After asking the interviewee a follow-up question, the answer content submitted by the interviewee to the follow-up question can be obtained, and key information can be extracted from the answer content corresponding to the follow-up question to detect the answer content corresponding to the follow-up question. If it is determined based on the extracted key information that the answer content corresponding to the follow-up question lacks any key information required for the follow-up question, the follow-up question is reconstructed for the interview question until the answer content to the follow-up question contains all the key information required for the interview question or the number of times the follow-up question is constructed exceeds the preset question threshold.

[0073] Based on this, if all the key information required for the interview question is obtained based on the interview question and / or one or more follow-up questions constructed for the interview question, or if all the key information required for the interview question is not obtained after the number of follow-up questions constructed for the interview question exceeds the preset follow-up threshold, it is determined whether the interview question is the last interview question. If the interview question is not the last interview question, a target follow-up phrase is selected from at least one preset follow-up phrase to be displayed to the interviewee, and the target follow-up phrase is used to prompt the interviewee to enter a new interview question. If the interview question is the last interview question, a closing phrase is provided to the interviewee, and the closing phrase is used to prompt the end of the interview.

[0074] In the above or below embodiments, multiple implementations may be used to extract key information from the answer content.

[0075] Figure 3 A schematic diagram of a process for extracting key information provided by another exemplary embodiment of the present application. Figure 3 In an optional implementation, the answers provided by the interviewee to the interview questions can be obtained, the type of position applied for by the interviewee can be obtained, and the interview questions, the position type and the answers can be constructed as input data for the second largest model. The preset second prompt information is obtained, and the second prompt information and the input data are provided to the second largest model, so as to use the second largest model to extract key information from the answers provided by the interviewee.

[0076] Among them, the second prompt information is used to instruct the second largest model to extract key information from the answer content according to the interview questions and job types in the input data, and the second prompt information also includes interview question examples, job type examples, answer content examples and corresponding key information examples. The second largest model is an artificial intelligence large model for extracting key information from the answer content. The second prompt information is a prompt template (promt template) configured for the second largest model, which is used to instruct the second largest model to extract key information from the answer content according to the interview questions, job types and answer content.

[0077] Of course, other implementation methods may also be used to extract key information from the answer content. For example, the key information may be manually extracted by the staff of the recruiting party, and this embodiment does not limit this.

[0078] Among them, the first model can extract key information from the answer content according to the following steps: perform semantic extraction operations on interview questions to obtain semantic information of interview questions, and search for target inspection dimensions that are compatible with the semantic information of interview questions from the inspection dimensions associated with the job type. Perform semantic extraction operations on the answer content to obtain semantic information of the answer content, and search for target semantic information that is compatible with any target inspection dimension from the semantic information of the answer content. Combine the found target semantic information and the target inspection dimension to which it is compatible as the key information extracted from the answer content, and the target inspection dimension and the target semantic information can be stored in the key information in the form of key-value pairs.

[0079] For example, if the interview question is "What kind of work have you done before?" and the answer is "I worked as a machine tool worker for 2 years", the semantic information extracted from the interview question is "What kind of work?", and the semantic information extracted from the answer is "I worked as a machine tool worker." The position type applied for by the interviewee is a technical position. The assessment dimensions associated with the technical position include age, education, work experience, working hours, etc. Based on this, the target assessment dimension found from the assessment dimensions associated with the technical position that is adapted to the semantic information of the interview question is "work experience", and the target semantic information found from the semantic information of the answer content that is adapted to the target assessment dimension is "machine tool worker." By combining the target assessment dimension and the target semantic information in the form of a key-value pair, it can be determined that the key information extracted from the answer content is {key: "work experience", value: "machine tool worker"}.

[0080] In this embodiment, by detecting the content of the answer, it is possible to determine in real time whether the key information required for the interview questions is obtained, and if the required key information is not obtained, timely follow-up questions can be asked to the interviewee to effectively ensure the accuracy and completeness of the collected key information, facilitate rapid screening of the interviewees, and eliminate the need to contact the interviewees again, thereby effectively improving the efficiency of the interview.

[0081] In the above or below embodiments, various implementations may be used to construct follow-up questions for interview questions.

[0082] Figure 4 A flowchart of constructing a follow-up question is provided for another exemplary embodiment of the present application. Figure 4 As shown, in an optional implementation, the interview questions and answers can be constructed as input data of a third model, preset third prompt information is obtained, and the input data and the third prompt information are provided to the third model to use the third model to generate follow-up questions for the interview questions.

[0083] Among them, the third prompt information is used to instruct the third model to ask follow-up questions based on the interview questions and answers in the input data, and the third prompt information also includes interview question examples, answer content examples, and corresponding follow-up question examples. The third model is an artificial intelligence model for generating follow-up questions for interview questions. The third prompt information is a prompt template (promt template) configured for the third model, which is used to instruct the third model to generate follow-up questions for interview questions based on interview questions and answer contents.

[0084] Of course, other implementations may also be used to construct follow-up questions. For example, at least one follow-up question may be constructed for the interview question by manual configuration, which is not limited in this embodiment.

[0085] Among them, the third model can generate follow-up questions for interview questions according to the following steps: perform semantic extraction operations on interview questions to obtain semantic information of interview questions, and find target inspection dimensions that are compatible with the semantic information of interview questions from the inspection dimensions associated with the type of position applied for by the interviewee. Perform semantic extraction operations on the answer content to obtain semantic information of the answer content, and find target semantic information that is compatible with the target inspection dimension from the semantic information of the answer content. For target inspection dimensions that do not have compatible target semantic information, generate follow-up questions for interview questions according to the follow-up question generation knowledge learned from the third prompt information. In this way, only generating follow-up questions for interview questions that have not been answered in the interview questions can not only reduce the workload of generating follow-up questions, but also improve the interview efficiency. It can also avoid asking repeated questions to the interviewee, and improve the interview experience of the interviewee.

[0086] In this embodiment, when the answer provided by the interviewee is not sufficient to answer the interview intention of the interview question, a new interview question is regenerated for the interview intention, and the interviewee is asked follow-up questions using the new interview question so that the interviewee can supplement the information in a timely manner, so that the employer can quickly and accurately obtain more effective information for the interviewee, thereby improving the employer's recruitment experience.

[0087] It should be noted that, in this embodiment, the form of the interview with the interviewee includes but is not limited to text form and voice form. If the interview process is in voice form, the interview questions, follow-up questions, and follow-up words can be converted from text form to voice form after the interview questions and follow-up questions are generated, or before the follow-up words are sent, and the converted voice is used to interact with the interviewee. Among them, the text form can be converted into voice form by using text-to-speech (TTS) technology. The form conversion of interview questions and follow-up questions is real-time conversion, and the interview questions and follow-up questions are converted into voice in real time, and sent directly to the interviewee after conversion; the form conversion of the follow-up words is cache conversion, and the follow-up words can be converted into voice in advance and continue to be cached so that they can be directly called when needed.

[0088] Figure 5 This is a schematic diagram of an interview logic in an exemplary application scenario provided by another exemplary embodiment of the present application. Figure 5 As shown, the interview logic in this exemplary application scenario includes the following steps:

[0089] Step 501, displaying multiple recruitment information to the interviewer;

[0090] Step 502, in response to an interview request initiated by the interviewer for the target recruitment information, the position type associated with the target recruitment information may be used as the position type applied for by the interviewer;

[0091] Step 503, obtaining the assessment dimensions required for the position type, the interviewee's resume data, and the interviewee's historical interview data;

[0092] Step 504, inputting the inspection dimension, the resume data and the historical interview data into the first large model, so that the first large model generates interview questions adapted to the inspection dimension according to the resume data and the historical interview data and the first prompt information;

[0093] Step 505, sending the interview question to the interviewee, and obtaining the answer provided by the interviewee to the interview question; wherein, if the interview question is the first interview question sent to the interviewee, the opening remarks of this interview may be shown to the interviewee before sending the first interview question;

[0094] Step 506, inputting the interview questions, answer content and job type into the second largest model, so that the second largest model extracts key information from the answer content according to the interview questions and job type and the second prompt information;

[0095] Step 507, judging whether the answer lacks any key information required for the interview question based on the extracted key information;

[0096] If it is determined in step 507 that the answer contains all the key information required for the interview question, then jump to step 508 to determine whether the current interview question is the last interview question;

[0097] If the current interview question is not the last interview question, then jump to step 509 and send the target follow-up words to the interviewee to prompt him to enter a new interview question;

[0098] If the current interview question is the last interview question, then jump to step 510 to provide the interviewee with a closing statement to end the interview;

[0099] If it is determined in step 507 that the answer lacks any key information required for the interview question, then jump to step 511 to determine whether the key information extraction time exceeds a preset time threshold;

[0100] If the preset time threshold is exceeded, the process jumps back to step 508 .

[0101] If the preset time threshold is not exceeded, jump to step 512 and input the interview questions and answers into the third model so that the third model can generate follow-up questions for the interview questions according to the interview questions and answers and the third prompt information.

[0102] Step 513, sending follow-up questions to the interviewee, and obtaining answers provided by the interviewee to the follow-up questions;

[0103] Step 514, determining whether the answer provided for the follow-up question lacks any key information required for the follow-up question;

[0104] If the answer provided for the follow-up question contains all the key information required for the follow-up question, then jump to step 508;

[0105] If the answer provided for the follow-up question lacks any key information required for the follow-up question, then jump to step 515 to determine whether the number of follow-up questions constructed for the interview question exceeds a preset follow-up threshold;

[0106] If it exceeds the preset inquiry threshold, jump to step 508;

[0107] If the preset inquiry threshold is not exceeded, the process jumps to step 512 .

[0108] Accordingly, in the embodiments of the present application, interview questions can be customized for each interviewee based on the interviewee's actual situation, and the answers returned by the interviewee can be detected in real time, so that when the answers lack any key information required for the interview questions, follow-up questions can be generated in time for the interview questions to guide the interviewee to provide the missing key information. This can provide the interviewee with a more realistic interview experience and a faster and more accurate understanding of the interviewee's abilities; and, corresponding response measures are provided for various situations that may arise during the interview process, so that the interview process can proceed smoothly, thereby effectively improving the interview experience and efficiency of both parties.

[0109] It should be noted that the execution subject of each step of the method provided in the above embodiment can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 200 to 203 can be device A; for another example, the execution subject of steps 201 and 202 can be device A, and the execution subject of step 203 can be device B; and so on.

[0110] In addition, in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel, and the sequence numbers of the operations, such as 201, 202, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0111] Figure 6 A schematic diagram of a computing device provided as another exemplary embodiment of the present application. Figure 6 As shown, the computing device includes: a memory 60 , a processor 61 and a communication component 62 .

[0112] The processor 61 is coupled to the memory 60 and the communication component 62, and is used to execute the computer program in the memory 60, so as to:

[0113] In response to an interview request initiated by an interviewer, determine the type of position the interviewer is applying for;

[0114] Obtain the assessment dimensions that need to be focused on under the position type;

[0115] Obtain the interviewee's resume data and historical interview data;

[0116] The inspection dimensions, resume data, and historical interview data are constructed as the input data for the first model;

[0117] Obtaining preset first prompt information, the first prompt information is used to instruct the first large model to generate interview questions adapted to the inspection dimension according to the resume data and historical interview data in the input data, the first prompt information also including resume data examples, historical interview data examples, inspection dimension examples, and corresponding interview question examples;

[0118] providing the input data and the first prompt information to the first large model to generate interview questions for the interview request using the first large model;

[0119] Interview questions are provided to the interviewee in the form of a dialogue to guide the interviewee to provide key information under each assessment dimension corresponding to the job type.

[0120] In an optional embodiment, in the process of generating interview questions for the interview request using the first large model, the processor 61 is further configured to:

[0121] Use the first model to extract the basic information of the interviewee from the interviewee's resume data and historical interview data;

[0122] The first model is used to construct interview questions for the interviewee based on the basic information of the interviewee and the dimensions that need to be considered under the job type.

[0123] In an optional embodiment, in the process of constructing interview questions for the interviewer based on the basic information of the interviewer using the first model and according to the examination dimensions that need to be paid attention to under the position type, the processor 61 is further used to:

[0124] Using the first model based on the basic information of the interviewee, construct interview questions corresponding to each assessment dimension as interview questions constructed for the interviewee; or,

[0125] The first model is used to determine the correlation between multiple assessment dimensions based on the assessment dimensions that need to be paid attention to under the job type, and to obtain at least one assessment dimension group; based on the basic information of the interviewee, interview questions corresponding to the assessment dimension group are constructed for each assessment dimension group as interview questions constructed for the interviewee.

[0126] In an optional embodiment, in response to an interview request initiated by an interviewer, in the process of determining the type of position applied for by the interviewer, the processor 61 is further configured to:

[0127] Show the interviewer a recruitment interface, which contains multiple recruitment information;

[0128] In response to an interview request initiated by an interviewer on a recruitment interface, determining a target recruitment information corresponding to the interview request;

[0129] The position type associated with the target recruitment information is used as the position type applied for by the interviewee.

[0130] In an optional embodiment, the processor 61 is further configured to:

[0131] After asking any interview question to the interviewee, obtain the answer provided by the interviewee to the interview question;

[0132] Extract key information from responses provided to interview questions;

[0133] If, based on the extracted key information, it is determined that the answer lacks any key information required for the interview question, then a follow-up question is constructed for the interview question;

[0134] Ask the interviewee follow-up questions to elicit the interviewee's key information needed for the follow-up questions.

[0135] In an optional embodiment, the processor 61 is further configured to:

[0136] In the case where multiple interview questions are generated for an interview request, obtaining a presentation order between the multiple interview questions;

[0137] For any interview question, extract key information from the responses provided to the interview question;

[0138] If the extracted key information contains key information required for subsequent interview questions, the presentation of subsequent interview questions will be deleted from the presentation order.

[0139] In an optional embodiment, in the process of extracting key information from the answers provided to the interview questions, the processor 61 is further configured to:

[0140] Interview questions, job types, and answer content were constructed as input data for the second largest model;

[0141] Obtaining preset second prompt information, the second prompt information is used to instruct the second largest model to extract key information from the answer content according to the interview questions and job types in the input data, the second prompt information also including interview question examples, job type examples, answer content examples and corresponding key information examples;

[0142] The second prompt information and the input data are provided to the second largest model, so as to utilize the second largest model to extract key information from the answer content provided by the interviewee.

[0143] In an optional embodiment, the processor 61 is further configured to:

[0144] After sending any interview question to the interviewee, if no answer is received to the interview question, the interview question will be sent again;

[0145] Construct follow-up questions for interview questions when the number of times the interview questions are sent repeatedly exceeds a preset repetition threshold;

[0146] Provide follow-up questions to the interviewee to guide the interviewee to provide key information required for the follow-up questions.

[0147] In an optional embodiment, the processor 61 is further configured to:

[0148] When all key information required for the interview question is obtained based on the interview question and / or one or more follow-up questions constructed for the interview question, or when all key information required for the interview question is still not obtained after the number of follow-up questions constructed for the interview question exceeds a preset follow-up threshold, determine whether the interview question is the last interview question;

[0149] If the interview question is not the last interview question, a target transition phrase is selected from at least one preset transition phrase and displayed to the interviewee. The target transition phrase is used to prompt the interviewee to proceed to a new interview question.

[0150] If the interview question is the last interview question, provide the interviewee with a closing statement, which is used to prompt the end of the interview.

[0151] In an optional embodiment, during the process of constructing follow-up questions for interview questions, the processor 61 is further configured to:

[0152] The interview questions and answers are used as input data for the third model;

[0153] Obtaining preset third prompt information, the third prompt information is used to instruct the third model to ask follow-up questions based on the interview questions and answers in the input data, and the third prompt information also includes interview question examples, answer content examples, and corresponding follow-up question examples;

[0154] The input data and the third prompt information are provided to the third model to generate follow-up questions for the interview questions using the third model.

[0155] Further, if Figure 6 As shown, the computing device also includes: a display 63, a power component 64, an audio component 65 and other components. Figure 6 Only some components are shown schematically, and it does not mean that the computing device only includes Figure 6 Components shown.

[0156] It is worth noting that the technical details in the above-mentioned embodiments of the computing device can refer to the relevant descriptions of the actions of the computing device in the aforementioned embodiments of the interview method. In order to save space, they will not be repeated here, but this should not cause a loss in the scope of protection of this application.

[0157] The embodiment of the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps in the above-mentioned interview method can be implemented. It should be noted that the technical solution of the computer program and the technical solution of the above-mentioned interview method belong to the same concept, and the details of the technical solution of the computer program that are not described in detail can be found in the description of the technical solution of the above-mentioned interview method.

[0158] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed, can implement the steps that can be executed by a computing device in the above method embodiment.

[0159] Above Figure 6 The memory in the computer is used to store computer programs and can be configured to store various other data to support operations on the computing platform. Examples of such data include instructions for any application or method operating on the computing platform, contact data, phone book data, messages, pictures, videos, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0160] Above Figure 6 The communication component in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0161] Above Figure 6The display in the embodiment includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0162] Above Figure 6 The power supply component in the device provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0163] Above Figure 6 The audio component in can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting an audio signal.

[0164] 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 adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0165] 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 the 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 generate 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.

[0166] 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.

[0167] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0169] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0170] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0171] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0172] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An interview method, It is characterized in that include: In response to an interview request initiated by an interviewer, determining the type of position applied for by the interviewer; Obtain the assessment dimensions that need to be focused on under the position type; Obtaining the resume data and historical interview data of the interviewee; The inspection dimension, the resume data and the historical interview data are constructed as input data of the first large model; Obtaining preset first prompt information, wherein the first prompt information is used to instruct the first large model to generate interview questions adapted to the examination dimension according to the resume data and the historical interview data in the input data, wherein the first prompt information also includes resume data examples, historical interview data examples, examination dimension examples, and corresponding interview question examples; providing the input data and the first prompt information to the first large model to generate interview questions for the interview request using the first large model; The interview questions are provided to the interviewee in the form of a dialogue to guide the interviewee to provide key information under each assessment dimension corresponding to the position type.

2. The method according to claim 1, It is characterized in that Generating interview questions for the interview request using the first model includes: Extracting basic information of the interviewee from the resume data of the interviewee and the historical interview data using the first large model; The first model is used to construct interview questions for the interviewee based on the basic information of the interviewee and in accordance with the examination dimensions that need to be focused on under the position type.

3. The method according to claim 2, It is characterized in that The first model is used to construct interview questions for the interviewee according to the assessment dimensions that need to be paid attention to under the position type and based on the basic information of the interviewee, including: constructing interview questions corresponding to each inspection dimension based on the basic information of the interviewee using the first large model, as interview questions constructed for the interviewee; or, The first model is used to determine the correlation between multiple examination dimensions based on the examination dimensions that need to be paid attention to under the job type, and obtain at least one examination dimension group; based on the basic information of the interviewee, interview questions corresponding to the examination dimension group are constructed for each examination dimension group as interview questions constructed for the interviewee.

4. The method according to claim 1, It is characterized in that The step of determining, in response to an interview request initiated by an interviewer, the type of position applied for by the interviewer comprises: Displaying a recruitment interface to the interviewer, wherein the recruitment interface includes multiple recruitment information; In response to the interview request initiated by the interviewer on the recruitment interface, determining target recruitment information corresponding to the interview request; The position type associated with the target recruitment information is used as the position type applied for by the interviewer.

5. The method according to claim 1, It is characterized in that Also includes: After issuing any interview question to the interviewee, obtaining the answer content provided by the interviewee to the interview question; Extract key information from the responses provided to the interview questions; If it is determined based on the extracted key information that the answer lacks any key information required for the interview question, constructing a follow-up question for the interview question; The follow-up questions are issued to the interviewer to guide the interviewer to provide key information required by the follow-up questions.

6. The method according to claim 1, It is characterized in that Also includes: In the case where multiple interview questions are generated for the interview request, obtaining a presentation order among the multiple interview questions; For any interview question, extract key information from the answer provided to the interview question; If the extracted key information includes key information required for subsequent interview questions, the presentation of the subsequent interview questions is deleted from the presentation order.

7. The method according to claim 5 or 6, It is characterized in that The extracting key information from the answers provided to the interview questions includes: constructing the interview questions, the job types and the answer contents as input data of the second largest model; Obtaining preset second prompt information, where the second prompt information is used to instruct the second large model to extract key information from the answer content according to the interview questions and job types in the input data, and the second prompt information also includes interview question examples, job type examples, answer content examples, and corresponding key information examples; The second prompt information and the input data are provided to the second large model, so as to utilize the second large model to extract key information from the answer content provided by the interviewee.

8. The method according to claim 1, It is characterized in that Also includes: After sending any interview question to the interviewer, if no answer to the interview question is received, the interview question is sent again; When the number of times the interview question is repeatedly sent exceeds a preset repetition threshold, constructing a follow-up question for the interview question; The follow-up questions are provided to the interviewer to guide the interviewer to provide key information required by the follow-up questions.

9. The method according to claim 8, It is characterized in that Also includes: In the case where all key information required for the interview question is obtained based on the interview question and / or one or more follow-up questions constructed for the interview question, or in the case where all key information required for the interview question is still not obtained after the number of follow-up questions constructed for the interview question exceeds a preset follow-up threshold, determining whether the interview question is the last interview question; If the interview question is not the last interview question, a target connecting phrase is selected from at least one preset connecting phrase and displayed to the interviewee, wherein the target connecting phrase is used to prompt the interviewee to proceed to a new interview question; If the interview question is the last interview question, a closing statement is provided to the interviewer, wherein the closing statement is used to prompt the end of the interview.

10. The method according to claim 5 or 8, It is characterized in that Construct follow-up questions for the interview questions, including: The interview questions and the answers are constructed as input data for the third model; Obtaining preset third prompt information, wherein the third prompt information is used to instruct the third model to ask follow-up questions based on the interview questions and answers in the input data, and the third prompt information also includes interview question examples, answer content examples, and corresponding follow-up question examples; The input data and the third prompt information are provided to the third model to generate follow-up questions for the interview question using the third model.

11. A computing device, It is characterized in that Includes memory, processor, and communication components; The memory is used to store one or more computer instructions; The processor is coupled to the memory and the communication component, and is configured to execute the one or more computer instructions to execute the interview method according to any one of claims 1 to 10.

12. A computer-readable storage medium storing computer instructions, It is characterized in that When the computer instructions are executed by one or more processors, the one or more processors are caused to execute the interview method according to any one of claims 1 to 10.

13. A computer program product, It is characterized in that The invention comprises a computer program, wherein when the computer program is executed by a processor, the processor is caused to execute the interview method according to any one of claims 1 to 10.

Images