CN113806516A - Matching degree determination method and device, electronic equipment and computer readable storage medium - Google Patents
Matching degree determination method and device, electronic equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN113806516A CN113806516A CN202111105618.0A CN202111105618A CN113806516A CN 113806516 A CN113806516 A CN 113806516A CN 202111105618 A CN202111105618 A CN 202111105618A CN 113806516 A CN113806516 A CN 113806516A
- Authority
- CN
- China
- Prior art keywords
- interview
- difficulty
- questions
- interviewee
- interviewer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000012360 testing method Methods 0.000 claims abstract description 225
- 230000008569 process Effects 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 12
- 241000209202 Bromus secalinus Species 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000013215 result calculation Methods 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 235000012149 noodles Nutrition 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/338—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/105—Human resources
- G06Q10/1053—Employment or hiring
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Computational Linguistics (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a matching degree determination method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: when the test result of the medium questions does not reach the first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m; if the number of the interviewee answered the interviewee with the medium difficulty is less than m, continuously outputting one interviewee with the medium difficulty, re-acquiring the test result of the medium question after the interviewee completes the answer, and judging whether the re-acquired test result of the medium question reaches a first preset result; if the number of the interviewee answers the interviewee with the medium difficulty is equal to m, outputting interviewee questions with low difficulty, and determining the low-difficulty test result of the interviewee after the interviewee answers all the interviewee questions with low difficulty; and determining the matching degree of the interviewer and the interview theme according to the test result of the medium questions and the test result of the low questions. By the method, the problem that the assessment on the ability of the interviewer is inaccurate in the prior art can be solved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a matching degree determination method, an apparatus, an electronic device, and a computer-readable storage medium.
Background
Currently, when an interviewer is interviewed, the degree of matching between the interviewer and the position of the interview is generally judged by answering several interview questions by the interviewer. However, the ability of the interviewer is evaluated only through a plurality of interview questions, and the problems of incomplete and inaccurate evaluation exist, so that the degree of matching between the interviewer and the interview post is not high.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, an electronic device, and a computer-readable storage medium for determining a matching degree, so as to solve the problem of inaccurate evaluation of an ability of an interviewer in the prior art.
The invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a matching degree determining method, where the method includes: outputting n surface test questions with medium difficulty corresponding to the surface test subjects, wherein n is any positive integer; determining a test result of the medium questions of the interviewee according to the answer of the interviewee to the interviewee; when the test result of the medium questions does not reach a first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m channels or not; if the number of the medium-difficulty interview questions answered by the interviewee is less than m, continuously outputting one medium-difficulty interview question, re-acquiring the medium-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired medium-difficulty interview test result reaches the first preset result; if the number of the medium-difficulty face test questions answered by the interviewee is equal to m, outputting low-difficulty face test questions corresponding to the face test subjects, and determining the low-difficulty test results of the interviewee after the interviewee answers all the low-difficulty face test questions, wherein m is a positive integer larger than n; and determining the matching degree of the interviewer and the interview theme according to the newly acquired test result of the medium question and the test result of the low question.
By the method, after the interviewer answers the interview questions with the difficulty of n tracks and the like, whether the interview questions with the difficulty of intermediate question and the like need to be asked or not can be judged according to the answering condition of the interviewer, namely after the interviewer answers the interview questions with the difficulty of n tracks and the like, if the ability level of the interviewer does not reach the preset ability level, the interview questions with the difficulty of intermediate question and the like are continuously asked, and the situation that the interview questions are too few and the interview of the interviewer is not complete is avoided. Moreover, the ability of the interviewer can be more accurately evaluated by asking the interviewer about difficult interview questions such as the middle interview questions.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, outputting the interview questions with low difficulty corresponding to the interview subjects includes: outputting i low-difficulty face test questions corresponding to the face test subjects, wherein i is any positive integer; determining the low-grade question test result of the interviewee according to the answer of the interviewee to the interviewee with low-grade difficulty; when the low-level question test result reaches a second preset result, representing that the interviewee finishes answering all the low-level difficulty interview questions; when the low-grade test result does not reach the second preset result, judging whether the number of the interview questions with low-grade difficulty answered by the interviewer is less than j paths or not; if the number of the low-difficulty interview questions answered by the interviewee is less than j, continuously outputting one low-difficulty interview question, re-determining the low-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired low-difficulty interview test result reaches the second preset result; and if the number of the low-difficulty surface test questions answered by the interviewee is equal to j, representing that the interviewee finishes answering all the low-difficulty surface test questions, wherein j is a positive integer larger than i.
In the embodiment of the application, whether the interviewee needs to ask the interviewee about the low-grade questions with low difficulty is judged by judging whether the low-grade test result of the interviewee about the i-lane low-grade test question reaches the second preset result, namely if the low-grade test result does not reach the second preset result, the interviewee continues to ask the interviewee about the low-grade test question with low difficulty. By the method, the situation that the questions of the interviewer are not asked comprehensively enough due to the fact that the number of the interview questions with difficulty such as low questions is not enough can be avoided. Moreover, by asking the interviewer about the interview questions with low difficulty, the interviewer can be evaluated more accurately. In addition, after the interviewer answers the interview questions with the i lanes of low difficulty and the like, if the determined test result of the low difficulty questions reaches a second preset result, the interviewer is not asked for the interview questions with the low difficulty and the like, and therefore the interview efficiency is improved.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, before outputting n interview subjects with medium difficulty corresponding to the interview subjects, the method further includes: acquiring x interview subjects according to the resume of the interviewer and the post requirements corresponding to the interview posts of the interviewer, wherein x is any positive integer; selecting any one interview theme from the x interview themes, and acquiring corresponding interview questions from a preset knowledge map according to the interview themes selected from the x interview themes, wherein the knowledge map is an interview question library designed according to all interview themes, and the interview questions comprise a plurality of interview questions with medium difficulty and a plurality of interview questions with low difficulty; the n pieces of the medium-difficulty interview questions corresponding to the interview subjects are the plurality of pieces of the medium-difficulty interview questions.
In the embodiment of the application, a plurality of interview subjects corresponding to interviewers can be acquired through the method. Moreover, any one interview theme is selected from the acquired interview themes, and then the corresponding multiple interview questions of different types are acquired in the preset knowledge map according to the selected interview theme, so that the interview questions of various difficulties required by an interviewer in interview can be acquired more conveniently and more conveniently.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the obtaining x interview subjects according to the resume of the interviewer and the post requirements corresponding to the interview posts of the interviewer includes: acquiring skill keywords in the resume; and matching the post requirements with the skill keywords to obtain the x surface test subjects.
In the embodiment of the application, a plurality of interview subjects are obtained by obtaining the skill keywords in the resume and matching the position requirement with the skill keywords, so that the interview subjects corresponding to the interviewer can be obtained more accurately and more quickly.
With reference to the technical solution provided by the first aspect, in some possible implementation manners, the determining an intermediate question test result of the interviewer according to the answer of the interviewer to the interview question includes: receiving voice information of the interviewer answering the interview questions; and converting the voice information into text information, and performing semantic similarity matching on the text information and a standard answer to obtain the test result of the intermediate questions.
In the embodiment of the application, the medium-subject test result of the interviewer can be obtained more conveniently and more quickly by the mode.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method further includes: in the process that the interviewer answers all the interview questions, a video monitoring picture of the interviewer is obtained in real time; and detecting whether the interviewer cheats according to the video monitoring picture.
In the embodiment of the application, the video monitoring picture of the interviewer is obtained in real time in the process that the interviewer answers all interview questions, whether the interviewer cheats is detected according to the video monitoring picture, and therefore the interviewer is ensured not to seek other help in the interview process, and the matching degree of the interviewer and the interview theme can be truly and accurately determined.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method further includes: when the cheating of the interviewer is detected, judging whether the number of cheated questions of the interviewer is less than y channels; if yes, the cheated interview questions are not included in the test result calculation corresponding to the interview questions; if not, the interview of the interview subject is ended, wherein y is any positive integer.
In the embodiment of the application, the cheating behaviors of the interviewee are processed differently according to the number of cheating topics of the interviewee, so that reasonable consideration can be given to the interviewee.
With reference to the technical solution provided by the first aspect, in some possible implementations, the method further includes: when the interviewer answers the interview questions with the medium difficulty, the obtained test results of the medium questions reach a first preset result, outputting p lines of the interview questions with the high difficulty corresponding to the interview subjects, wherein p is any positive integer; determining a high-grade question test result of the interviewee according to the answer of the interviewee to the interviewee with the high difficulty; when the high-grade test result reaches a third preset result, representing that the interviewer finishes answering all interview questions with high equal difficulty; when the high-grade test result does not reach the third preset result, judging whether the number of interview questions with high difficulty answered by the interviewee is less than k paths or not; if the number of the interviewee answered high-difficulty interviewee questions is less than k, continuously outputting one high-difficulty interviewee question, after the interviewee answers, re-determining the high-difficulty test result of the interviewee, and judging whether the re-acquired high-difficulty test result reaches the third preset result; if the number of the interviewer answered interview questions with high equal difficulty is equal to k, representing that the interviewer finishes answering all the interviewer questions with high equal difficulty, wherein k is a positive integer larger than p; after the interviewer answers all interview questions with high difficulty and the like, determining a high-question test result of the interviewer, and determining the matching degree of the interviewer and the interview theme according to the newly obtained medium-question test result and high-question test result.
In the embodiment of the application, when the test result of the medium questions of the interviewee reaches the first preset result, the interviewee is asked about the interviewee with high difficulty, so that the questions of the interviewee are more in line with the capability level of the interviewee, and the matching degree of the interviewee and the interviewed position is more accurately evaluated. In addition, the situation that the questions of the interviewer are not complete due to insufficient number of interview questions with difficulty such as high questions can be avoided through the method. In addition, after the interviewer answers p high-difficulty interview questions, if the determined high-difficulty interview test result reaches a third preset result, the interviewer is not asked for the high-difficulty interview questions, and therefore the interview efficiency is improved.
In a second aspect, an embodiment of the present application provides a matching degree determination apparatus, where the apparatus includes: the processing module is used for outputting n pieces of face test questions with medium difficulty corresponding to the face test subjects, wherein n is any positive integer; determining a test result of the medium questions of the interviewee according to the answer of the interviewee to the interviewee; when the test result of the medium questions does not reach a first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m channels or not; if the number of the medium-difficulty interview questions answered by the interviewee is less than m, continuously outputting one medium-difficulty interview question, re-acquiring the medium-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired medium-difficulty interview test result reaches the first preset result; if the number of the medium-difficulty face test questions answered by the interviewee is equal to m, outputting low-difficulty face test questions corresponding to the face test subjects, and determining the low-difficulty test results of the interviewee after the interviewee answers all the low-difficulty face test questions, wherein m is a positive integer larger than n; and the determining module is used for determining the matching degree of the interviewer and the interview theme according to the newly acquired test result of the medium question and the test result of the low question.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory, the processor and the memory connected; the memory is used for storing programs; the processor is configured to invoke a program stored in the memory to perform a method as provided in the above-described first aspect embodiment and/or in combination with some possible implementations of the above-described first aspect embodiment.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, performs the method as set forth in the above first aspect embodiment and/or in combination with some possible implementations of the above first aspect embodiment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart illustrating steps of a matching degree determining method according to an embodiment of the present disclosure.
Fig. 2 is a detailed flowchart of a matching degree determining method according to an embodiment of the present disclosure.
Fig. 3 is a detailed flowchart of a matching degree determining method according to an embodiment of the present disclosure.
Fig. 4 is a block diagram of a matching degree determination apparatus according to an embodiment of the present application.
Fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
In view of the inaccurate evaluation of the ability of the testers in the prior art, the inventors of the present application have conducted research and research to provide the following embodiments to solve the above problems.
The following describes a specific flow and steps of a matching degree determination method with reference to fig. 1. It should be noted that the matching degree determination method provided in the embodiment of the present application is not limited to the order shown in fig. 1 and below.
Step S101: outputting n pieces of face test questions with medium difficulty corresponding to the face test subjects.
Optionally, as shown in fig. 2, before step S101, x interview topics are obtained according to the resume of the interviewer and the post requirements corresponding to the post interviewed by the interviewer, where x is any positive integer; selecting any one interview theme from x interview themes, and acquiring corresponding interview questions from a preset knowledge map according to the interview theme selected from the x interview themes, wherein the knowledge map is an interview question library designed according to all interview themes, and the interview questions comprise multiple interview questions with medium difficulty and multiple interview questions with low difficulty; n pieces of medium-difficulty interview questions corresponding to the interview subjects are the interview questions in the multiple pieces of medium-difficulty interview questions, wherein n is any positive integer.
In the embodiment of the application, a plurality of interview subjects corresponding to interviewers can be acquired through the method. In addition, any one interview theme is selected from the obtained interview themes, and the corresponding interview questions are obtained in the preset knowledge map according to the selected interview theme, so that the interview questions with different difficulties required by an interviewer in interviewing can be obtained more conveniently and more quickly.
Specifically, acquiring skill keywords in the resume; and matching the post requirements with the skill keywords to obtain x surface test topics. By obtaining the skill keywords in the resume and matching the post requirements with the skill keywords, a plurality of interview themes are obtained, and a plurality of interview themes corresponding to the interviewer can be obtained more accurately and more quickly.
For example: the interviewer's interview post is a Java development engineer, and the skills keywords in the interviewer's resume are: java, Servlet, RESTful, database, AngularJS, wherein, Java development engineer's post demand is: the Java programming and design are well mastered, the common data structure is familiar, the distributed algorithm and the mainstream distributed system are deeply understood, the RESTful, JSON, Web Service and other related technologies are familiar, and the message middleware, database operation and the like are familiar. Matching the skill keywords in the resume of the interviewer with the post requirements of the Java development engineer to obtain 3 interview themes, wherein the 3 interview themes are respectively as follows: java, RESTful, database.
It should be noted that, the way of outputting n interview questions with medium difficulty corresponding to the interview theme may be to play the interview questions in a voice manner; or may be displayed on the interviewer's display screen, which is not limited herein. In addition, the above-mentioned n pieces of medium-difficulty interview questions can be output separately for each question, namely, one interview question is output first, and after the interviewer finishes answering, the next interview question is output; or directly and completely outputting n-channel test questions, such as: the n-channel test questions are directly displayed on the display screen of the interviewer.
After outputting the n-channel medium difficulty interview questions, the method can continue to step S102.
Step S102: and determining the test result of the medium questions of the interviewee according to the answers of the interviewee to the face test questions.
Wherein, the mesopic test result can be a specific score, such as: the test result of the medium questions is the total score of n test questions answered by the interviewer, such as: the test result of the medium questions is the average score of n test questions answered by the interviewer; it can also be a grade obtained after the interviewer answers n interview questions, such as: the test result of the test on the mesoscale test is B grade.
Optionally, step S102 may specifically include: receiving voice information of the interviewer answering the interview questions; and converting the voice information into character information, and performing semantic similarity matching on the character information and the standard answers to obtain a test result of the medium questions. By the mode, the medium-grade test result of the interviewer can be obtained more conveniently and more quickly.
After determining the subject test results of the interviewer, the method can continue to step S103.
Step S103: when the test result of the medium questions does not reach the first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m; if yes, go to step S104; if not, go to step S105.
It should be noted that the first preset result is consistent with the type of the test result of the middle topic, such as: if the test result of the question with the middle degree is a score, the first preset result is also a preset score, for example: if the test result of the middle topic is a grade, the first prediction result is also a preset grade. Further, m is a positive integer greater than n. Wherein, if the result of the examination of the subject in the middle level does not reach the first preset result, it means that the ability level of the subject does not reach the preset ability level.
Step S104: and continuously outputting one interview question with the medium difficulty, re-acquiring the test result of the interviewer with the medium difficulty after the interviewer completes the answer, and judging whether the re-acquired test result of the medium question reaches a first preset result.
In the embodiment of the application, a middle-difficulty interview question is output, after the interviewer answers the questions, the middle-difficulty interview question test result of the interviewer is obtained again according to all middle-difficulty interview questions answered by the interviewer, and whether the obtained middle-difficulty interview question test result reaches a first preset result is judged. If the re-acquired test result of the middle questions does not reach the first preset result, it indicates that the ability level of the interviewer does not reach the preset ability level, and at this time, it is determined again whether the number of the interview questions with the middle difficulty answered by the interviewer is less than m (i.e., step S103 is executed).
By the method, under the condition that the test result of the medium degree of difficulty of the interviewer does not reach the first preset result and the quantity of the interview questions with the medium degree of difficulty asked by the interviewer does not exceed the preset maximum quantity value, the interviewer can continuously ask the interview questions with the medium degree of difficulty, so that the situation that the interview questions with the medium degree of difficulty are too few and the questions asked by the interviewer are not comprehensive is avoided. Moreover, the ability of the interviewer is more accurately evaluated by asking the interviewer about the interview questions with medium difficulty.
Step S105: outputting the low-difficulty interview questions corresponding to the interview subjects, and determining the low-difficulty test results of the interviewer after the interviewer answers all the low-difficulty interview questions.
Specifically, outputting i lanes of low-difficulty face test questions corresponding to the face test subjects, wherein i is any positive integer; determining the low-grade question test result of the interviewee according to the answer of the interviewee to the low-grade test question; when the test result of the low-level questions reaches a second preset result, representing that the interviewee finishes answering all the low-level difficulty interview questions; when the test result of the low-level questions does not reach a second preset result, judging whether the number of the interview questions with low-level difficulty answered by the interviewer is less than j paths or not; if the number of the low-difficulty interview questions answered by the interviewee is less than j, continuously outputting one low-difficulty interview question, re-determining the low-difficulty interview question test result of the interviewee after the interviewee completes the answer, and judging whether the re-obtained low-difficulty interview test result reaches a second preset result; if the number of the low-difficulty surface test questions answered by the interviewer is equal to j, representing that the interviewer finishes answering all the low-difficulty surface test questions, wherein j is a positive integer larger than i.
By the method, the situation that the questions of the interviewer are not asked comprehensively enough due to the fact that the number of the interview questions with difficulty such as low questions is not enough can be avoided. Moreover, by asking the interviewer about the interview questions with low difficulty, the interviewer can be evaluated more accurately. In addition, after the interviewer answers the interview questions with the i lanes of low difficulty and the like, if the determined test result of the low difficulty questions reaches a second preset result, the interviewer is not asked for the interview questions with the low difficulty and the like, and therefore the interview efficiency is improved.
Optionally, step S105 may also be: directly outputting h lines of low-difficulty interview questions corresponding to interview subjects, and determining the low-difficulty interview test results of the interviewers after the interviewers answer all the low-difficulty interview questions, wherein h is any positive integer.
After determining the interviewer' S low-grade test results, the method can continue to step S106.
Step S106: and determining the matching degree of the interviewer and the interview theme according to the newly acquired test result of the medium question and the test result of the low question.
Specifically, according to the preset weight relationship between the test result of the medium question and the test result of the low question, the matching degree between the interviewer and the interview subject is determined, for example: if the test result of the medium questions and the test result of the low questions are both a fraction, the matching degree is the sum of the test result of the medium questions with 0.7 and the test result of the low questions with 0.3.
It should be noted that, in the process of comparing the intermediate subject test result obtained from the interview subject who has answered a plurality of intermediate difficulty subjects with the first preset result (after step S102 or in step S104), the above-mentioned intermediate subject test result may reach the first preset result. As shown in fig. 3, fig. 3 is a detailed flowchart of a matching degree determining method, wherein step a in fig. 3 is step a in fig. 2, and the following describes a case where the result of the mesopic test reaches a first preset result.
Specifically, when a middle question test result obtained by the interviewer who answers the interview questions with the middle difficulty reaches a first preset result, outputting p lines of the interview questions with the high difficulty corresponding to the interview theme, wherein p is any positive integer; determining the test result of the high-grade questions of the interviewee according to the answers of the interviewee to the high-grade test questions; when the test result of the high questions reaches a third preset result, representing that the interviewer finishes answering all interview questions with high difficulty; when the test result of the high questions does not reach a third preset result, judging whether the number of the interview questions with high difficulty answered by the interviewer is less than k paths or not; if the number of the interviewee answered high-difficulty interviewee questions is less than k, continuously outputting one high-difficulty interviewee question, re-determining the high-difficulty test result of the interviewee after the interviewee answers, and judging whether the re-obtained high-difficulty test result reaches a third preset result; if the number of the interviewer answers the interviewer with the high difficulty is equal to k, representing that the interviewer finishes answering all the interviewer with the high difficulty, wherein k is a positive integer larger than p; after the interviewer answers all interview questions with high difficulty and the like, determining the test result of the high questions of the interviewer, and determining the matching degree of the interviewer and the interview subject according to the newly obtained test result of the medium questions and the test result of the high questions.
By the method, when the medium question test result of the interviewer reaches the first preset result, the interviewer can be asked questions with high difficulty and the like, so that the questions of the interviewer are more in line with the capability level of the interviewer, and the matching degree of the interviewer and the interviewed position is more accurately evaluated. Moreover, the situation that the questions of the opposite testers are not complete due to insufficient number of the difficult interview questions such as high questions can be avoided. In addition, after the interviewer answers p high-difficulty interview questions, if the determined high-difficulty interview test result reaches a third preset result, the interviewer is not asked for the high-difficulty interview questions, and therefore the interview efficiency is improved.
Optionally, when the interviewer finishes answering the interview questions with the medium difficulty and obtains a medium question test result reaching a first preset result, directly outputting w channels of interview questions with high difficulty corresponding to the interview subjects; after the interviewer answers all interview questions with high difficulty and the like, determining the test result of the high questions of the interviewer; and then, according to the medium question test result and the high question test result of the interviewer, obtaining the matching degree of the interviewer and the interview subject, wherein w is any positive integer. By the method, when the medium question test result of the interviewer reaches the first preset result, a plurality of high-difficulty interview questions can be asked for the interviewer, so that the questions of the interviewer are more in line with the capability level of the interviewer, and the matching degree of the interviewer and the interviewed position can be evaluated more accurately.
It should be further noted that, in the process of answering all the interviewer questions, the interviewer video monitoring picture is obtained in real time; and detecting whether the interviewer cheats according to the video monitoring picture.
Specifically, in the process that an interviewer answers all interview questions, a camera on electronic equipment of the interviewer is called to obtain a video monitoring picture of the interviewer in real time, videos of 20s adjacent to the video monitoring picture are captured, the similarity of the captured pictures is calculated through a Hamming distance, and whether the captured picture has large change or not is judged according to the similarity; if the screenshot interface is judged to have a large change, the interviewer is judged to have cheating behaviors, wherein the hamming distance is a method for calculating the similarity of the pictures, which is well known to those skilled in the art, and is not described herein too much. In addition, the screenshot can be input into a preset network model so as to judge the micro expression of the interviewer; and judging whether the interviewer has cheating behaviors according to the micro expression of the interviewer.
Optionally, when the cheating of the interviewer is detected, judging whether the number of cheated questions of the interviewer is less than y channels; if yes, the cheated interview questions are not included in the test result calculation corresponding to the interview questions; if not, the interview of the interview subject is ended, wherein y is any positive integer.
Optionally, when the cheating of the interviewer is detected, cheating prompt information is output to the interviewer. The cheating prompt message can be output on the display interface of the interviewer or can be output by voice. By the method, the interviewer can be reminded when cheating so as to pay attention to the interviewer not to cheat.
In addition, it should be noted that, in the process of conducting interviewing on an interviewer, the interviewer can be interviewed only for one interview subject, that is, only the steps S101 to S106 are executed once; the interviewer can also be interviewed for a plurality of interview subjects, i.e., steps S101-S106 are performed a plurality of times. When the interviewer is interviewed according to a plurality of interview subjects, the matching degree of the interviewer and the interview subjects can be determined; and aiming at the determined matching degrees, calculating the matching degrees according to a preset weight relation so as to obtain a final interview matching degree.
A matching degree determination method will be described below using an example.
The position of interviewer interviewing is an algorithm engineer, and the position requirements of the algorithm engineer are as follows: the probability mathematical statistics, operational research, graph theory and optimization theory are completely mastered; refining one or more languages such as Python, R, Matlab and the like; skillfully using a deep learning framework such as tensorflow, pyrrch and the like; the method is mastered by computer vision, NLP and knowledge graph related technologies. Skill keywords are extracted from the resumes of the interviewer, and the following skill keywords can be obtained: python, Matlab, supervised learning, unsupervised learning, computer vision. According to the skill keywords and the post requirements corresponding to the algorithm engineers, 3 interview subjects can be obtained, namely Python, Matlab and computer vision. And then, selecting any interview theme from the 3 interview themes, for example, selecting Matlab as the interview theme, and acquiring the corresponding interview theme in a preset knowledge graph according to the Matlab theme.
Then, 5 lines of interview questions with medium difficulty corresponding to Matlab are output according to the interview questions obtained in the preset knowledge map, after the interviewer answers the 5 lines of interview questions with medium difficulty, the score of each line of interview questions of the interviewer is determined according to the answer of the interviewer, and one average score of the medium questions (namely the test result of the medium questions) is obtained according to the score of each line of interview questions.
If the interviewer answers 5 middle difficulty interview questions, the scores are respectively as follows: 40. 55, 45, 60 and 50, calculating the average score of the above-mentioned mesoproblems to be 50 points; if the first preset result is 60 minutes, 50 minutes is less than 60 minutes, namely the average score of the middle degree test does not reach the first preset result, then, whether the number of the interview questions with the middle degree of difficulty answered by the interviewee is less than 10 is judged, namely, the interviewee has answered 5 lines of the interview questions with the middle degree of difficulty and has answered less than 10 lines of the interview questions with the middle degree of difficulty, at the moment, one line of the interview questions with the middle degree of difficulty is continuously output, and after the interviewee finishes answering, the score of the interview question with the middle degree of difficulty in the 6 th line is determined; and then, according to the score of the test question of the 6 th lane and the score of the test question of the first 5 lanes, the average score of the medium questions is recalculated, whether the recalculated average score of the medium questions is more than or equal to 60 is judged, and if the recalculated average score of the medium questions is less than 60, the steps are repeated.
If the interviewer answers 10 middle difficulty interview questions, the newly acquired middle difficulty interview questions are averagely divided into 58 minutes, the 58 minutes are smaller than 60 minutes, namely the newly acquired middle difficulty interviewer average score still does not reach the first preset result, and 5 low difficulty interview questions corresponding to Matlab are output to the interviewer. After the interviewer answers the 5 low-difficulty interview questions, determining the score of each low-difficulty interview question of the interviewer according to the answer of the interviewer, and obtaining an average score of the low-difficulty interview questions (namely a low-difficulty test result) according to the score of each low-difficulty interview question.
If the interviewer answers 5 low difficulty interview questions, the scores are respectively as follows: 77. 80, 69, 73 and 75, the average score of the low-grade questions can be calculated to be 74.8; if the second preset result is 80 minutes, 74.8 minutes is less than 80 minutes, namely the average score of the low-level questions does not reach the second preset result, then, whether the number of the interview questions with low difficulty answered by the interviewee is less than 10 is judged, namely, the interviewee has answered 5 interview questions with low difficulty and has answered the number of the interview questions with low difficulty less than 10, at the moment, one interview question with low difficulty continues to be output, and after the interviewee finishes answering, the score of the interview question with low difficulty of the 6 th lane is determined; and then, according to the score of the 6 th low-grade difficulty face test question and the score of the 5 th low-grade difficulty face test question, recalculating the average score of the low-grade questions, judging whether the recalculated average score of the low-grade questions is greater than or equal to 80 minutes, and if the recalculated average score of the low-grade questions is not less than 80 minutes, repeating the steps.
If the average score of the low-grade questions of the interviewee is greater than or equal to 80 minutes in the process, representing that the interviewee finishes answering all the interview questions with low-grade difficulty, and determining the matching degree of the interviewee and Matlab according to the newly acquired average score of the medium-grade questions and the average score of the low-grade questions.
If the average score of the newly acquired low-grade questions is still less than 80 after the interviewer answers 10 interview questions with low-grade difficulty, namely the average score of the newly acquired low-grade questions of the interviewer still does not reach a second preset result, representing that the interviewer answers all the interview questions with low-grade difficulty, and determining the matching degree of the interviewer and Matlab according to the average score of the newly acquired medium-grade questions and the average score of the low-grade questions.
If the average score of the medium questions is more than or equal to 60 scores during the process that the interviewer answers the interview questions with medium difficulty, 5 lines of the interview questions with high difficulty corresponding to the interview subjects are output to the interviewer. After the interviewer answers the 5 high and equal difficulty interview questions, determining the score of each high and equal difficulty interview question of the interviewer according to the answer of the interviewer, and obtaining a high and equal question average score (namely a high and equal question test result) according to the score of each high and equal difficulty interview question.
If the interviewer answers 5 high-difficulty interview questions with the scores of: 89. 70, 66, 82 and 62, calculating the average score of the high-grade questions to be 73.8; if the third preset result is 80 minutes, 74 minutes is smaller than 80 minutes, namely the average score of the high-level questions does not reach the third preset result, then, whether the number of the interview questions with high equal difficulty answered by the interviewee is smaller than 10 is judged, namely, the interviewee has answered 5 lines of the interview questions with high equal difficulty and the number of the interview questions with high equal difficulty answered is smaller than 10, at the moment, one line of the interview questions with high equal difficulty is continuously output, and after the interviewee finishes answering, the score of the interview question with high difficulty at the 6 th line is determined; and then, according to the score of the 6 th high-difficulty noodle test question and the score of the first 5 high-difficulty noodle test questions, recalculating the average score of the high-difficulty questions, judging whether the recalculated average score of the high-difficulty questions is greater than or equal to 80 minutes, and if the recalculated average score of the high-difficulty questions is less than 80 minutes, repeating the steps.
If the average score of the high questions of the interviewee reaches 80 points in the process, representing that the interviewee finishes answering all the interview questions with high difficulty, and determining the matching degree of the interviewee and Matlab according to the newly obtained average score of the medium questions and the average score of the high questions.
If the average score of the newly acquired high-grade questions is still less than 80 minutes after the interviewer answers 10 interview questions with high-grade difficulty, namely the average score of the newly acquired high-grade questions of the interviewer does not reach a third preset result, representing that the interviewer answers all the interview questions with high-grade difficulty, and determining the matching degree of the interviewer and Matlab according to the average score of the newly acquired medium questions and the average score of the high questions.
Referring to fig. 4, based on the same inventive concept, an embodiment of the present application further provides a matching degree determining apparatus 100, where the apparatus 100 includes: a processing module 101 and a determining module 102.
The processing module 101 is configured to output n trial questions with medium difficulty corresponding to the trial topics, where n is any positive integer; determining a test result of the medium questions of the interviewee according to answers of the interviewee to the face test questions; when the test result of the medium questions does not reach the first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m; if the number of the middle-difficulty interview questions answered by the interviewee is less than m, continuously outputting one middle-difficulty interview question, re-acquiring the middle-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired middle-difficulty interview test result reaches a first preset result; if the number of the interviewer answering the interviewer with the medium difficulty is equal to m, outputting the interviewer questions with the low difficulty corresponding to the interviewer theme, and determining the low-difficulty test result of the interviewer after the interviewer answers all the interviewer questions with the low difficulty, wherein m is a positive integer larger than n.
The determining module 102 is configured to determine a matching degree between the interviewer and the interview theme according to the latest obtained test result of the medium question and the test result of the low question.
Optionally, the processing module 101 is specifically configured to output i lanes of low-level difficulty face test questions corresponding to the face test subjects, where i is any positive integer; determining the low-grade question test result of the interviewee according to the answer of the interviewee to the low-grade test question; when the test result of the low-level questions reaches a second preset result, representing that the interviewee finishes answering all the low-level difficulty interview questions; when the test result of the low-level questions does not reach a second preset result, judging whether the number of the interview questions with low-level difficulty answered by the interviewer is less than j paths or not; if the number of the low-difficulty interview questions answered by the interviewee is less than j, continuously outputting one low-difficulty interview question, re-determining the low-difficulty interview question test result of the interviewee after the interviewee completes the answer, and judging whether the re-obtained low-difficulty interview test result reaches a second preset result; if the number of the low-difficulty surface test questions answered by the interviewer is equal to j, representing that the interviewer finishes answering all the low-difficulty surface test questions, wherein j is a positive integer larger than i.
Optionally, the processing module 101 is further configured to, before outputting n interview questions with medium difficulty corresponding to the interview theme, obtain x interview themes according to the resume of the interviewer and the post requirements corresponding to the post interviewed by the interviewer, where x is any positive integer; selecting any one interview theme from x interview themes, and acquiring corresponding interview questions from a preset knowledge map according to the interview theme selected from the x interview themes, wherein the knowledge map is an interview question library designed according to all interview themes, and the interview questions comprise multiple interview questions with medium difficulty and multiple interview questions with low difficulty; the n pieces of the face test questions with the medium difficulty corresponding to the face test subjects are face test questions in the plurality of pieces of the face test questions with the medium difficulty.
Optionally, the processing module 101 is specifically configured to obtain a skill keyword in the resume; and matching the post requirements with the skill keywords to obtain x surface test topics.
Optionally, the processing module 101 is specifically configured to receive voice information of an interviewer answering an interview question; and converting the voice information into character information, and performing semantic similarity matching on the character information and the standard answers to obtain a test result of the medium questions.
Optionally, the processing module 101 is further configured to obtain a video monitoring picture of the interviewer in real time during the process that the interviewer answers all the interview questions; and detecting whether the interviewer cheats according to the video monitoring picture.
Optionally, the processing module 101 is further configured to, when detecting that the interviewee cheats, determine whether the number of cheated questions of the interviewee is less than y tracks; if yes, the cheated interview questions are not included in the test result calculation corresponding to the interview questions; if not, the interview of the interview subject is ended, wherein y is any positive integer.
Optionally, the processing module 101 is further configured to output p high-difficulty interview questions corresponding to the interview topic when the interviewer answers the interview questions with the medium difficulty to obtain a first preset result, where p is any positive integer; determining the test result of the high-grade questions of the interviewee according to the answers of the interviewee to the high-grade test questions; when the test result of the high questions reaches a third preset result, representing that the interviewer finishes answering all interview questions with high difficulty; when the test result of the high questions does not reach a third preset result, judging whether the number of the interview questions with high difficulty answered by the interviewer is less than k paths or not; if the number of the interviewee answered high-difficulty interviewee questions is less than k, continuously outputting one high-difficulty interviewee question, re-determining the high-difficulty test result of the interviewee after the interviewee answers, and judging whether the re-obtained high-difficulty test result reaches a third preset result; if the number of the interviewer answers the interviewer with the high difficulty is equal to k, representing that the interviewer finishes answering all the interviewer with the high difficulty, wherein k is a positive integer larger than p; after the interviewer answers all interview questions with high difficulty and the like, determining the test result of the high questions of the interviewer, and determining the matching degree of the interviewer and the interview subject according to the newly obtained test result of the medium questions and the test result of the high questions.
Referring to fig. 5, based on the same inventive concept, an exemplary structural block diagram of an electronic device 200 is provided in the embodiment of the present application, and the electronic device 200 is used in the matching degree determination method. In the embodiment of the present application, the electronic Device 200 may be, but is not limited to, a Personal Computer (PC), a smart phone, a tablet Computer, a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), and the like. Structurally, electronic device 200 may include a processor 210 and a memory 220.
The processor 210 and the memory 220 are electrically connected, directly or indirectly, to enable data transmission or interaction, for example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 210 may be an integrated circuit chip having signal processing capabilities. The Processor 210 may also be a general-purpose Processor, for example, a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a discrete gate or transistor logic device, or a discrete hardware component, which can implement or execute the methods, steps, and logic blocks disclosed in the embodiments of the present Application. Further, a general purpose processor may be a microprocessor or any conventional processor or the like.
The Memory 220 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), and an electrically Erasable Programmable Read-Only Memory (EEPROM). The memory 220 is used for storing a program, and the processor 210 executes the program after receiving the execution instruction.
It should be understood that the structure shown in fig. 5 is merely an illustration, and the electronic device 200 provided in the embodiments of the present application may have fewer or more components than those shown in fig. 5, or may have a different configuration than that shown in fig. 5. Further, the components shown in fig. 5 may be implemented by software, hardware, or a combination thereof.
It should be noted that, as those skilled in the art can clearly understand, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Based on the same inventive concept, embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the computer program performs the methods provided in the above embodiments.
The storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of one logic function, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A method for determining a degree of matching, the method comprising:
outputting n surface test questions with medium difficulty corresponding to the surface test subjects, wherein n is any positive integer;
determining a test result of the medium questions of the interviewee according to the answer of the interviewee to the interviewee;
when the test result of the medium questions does not reach a first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m channels or not; if the number of the medium-difficulty interview questions answered by the interviewee is less than m, continuously outputting one medium-difficulty interview question, re-acquiring the medium-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired medium-difficulty interview test result reaches the first preset result; if the number of the medium-difficulty face test questions answered by the interviewee is equal to m, outputting low-difficulty face test questions corresponding to the face test subjects, and determining the low-difficulty test results of the interviewee after the interviewee answers all the low-difficulty face test questions, wherein m is a positive integer larger than n;
and determining the matching degree of the interviewer and the interview theme according to the newly acquired test result of the medium question and the test result of the low question.
2. The method of claim 1, wherein outputting the interview questions of low difficulty corresponding to the interview topic comprises:
outputting i low-difficulty face test questions corresponding to the face test subjects, wherein i is any positive integer;
determining the low-grade question test result of the interviewee according to the answer of the interviewee to the interviewee with low-grade difficulty;
when the low-level question test result reaches a second preset result, representing that the interviewee finishes answering all the low-level difficulty interview questions;
when the low-grade test result does not reach the second preset result, judging whether the number of the interview questions with low-grade difficulty answered by the interviewer is less than j paths or not; if the number of the low-difficulty interview questions answered by the interviewee is less than j, continuously outputting one low-difficulty interview question, re-determining the low-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired low-difficulty interview test result reaches the second preset result; and if the number of the low-difficulty surface test questions answered by the interviewee is equal to j, representing that the interviewee finishes answering all the low-difficulty surface test questions, wherein j is a positive integer larger than i.
3. The method of claim 1, wherein prior to said outputting n interview questions of intermediate difficulty corresponding to interview subjects, the method further comprises:
acquiring x interview subjects according to the resume of the interviewer and the post requirements corresponding to the interview posts of the interviewer, wherein x is any positive integer;
selecting any one interview theme from the x interview themes, and acquiring corresponding interview questions from a preset knowledge map according to the interview themes selected from the x interview themes, wherein the knowledge map is an interview question library designed according to all interview themes, and the interview questions comprise a plurality of interview questions with medium difficulty and a plurality of interview questions with low difficulty; the n pieces of the medium-difficulty interview questions corresponding to the interview subjects are the plurality of pieces of the medium-difficulty interview questions.
4. The method of claim 3, wherein the obtaining x interview topics according to the interviewer's resume and the position requirements corresponding to the interviewed positions comprises:
acquiring skill keywords in the resume;
and matching the post requirements with the skill keywords to obtain the x surface test subjects.
5. The method of claim 1, wherein said determining the interviewer's intermediate question test results based on the interviewer's answers to the interviewer's questions comprises:
receiving voice information of the interviewer answering the interview questions;
and converting the voice information into text information, and performing semantic similarity matching on the text information and a standard answer to obtain the test result of the intermediate questions.
6. The method of claim 1, further comprising:
in the process that the interviewer answers all the interview questions, a video monitoring picture of the interviewer is obtained in real time;
and detecting whether the interviewer cheats according to the video monitoring picture.
7. The method of claim 6, further comprising:
when the cheating of the interviewer is detected, judging whether the number of cheated questions of the interviewer is less than y channels; if yes, the cheated interview questions are not included in the test result calculation corresponding to the interview questions; if not, the interview of the interview subject is ended, wherein y is any positive integer.
8. The method of claim 1, further comprising:
when the interviewer answers the interview questions with the medium difficulty, the obtained test results of the medium questions reach a first preset result, outputting p lines of the interview questions with the high difficulty corresponding to the interview subjects, wherein p is any positive integer;
determining a high-grade question test result of the interviewee according to the answer of the interviewee to the interviewee with the high difficulty;
when the high-grade test result reaches a third preset result, representing that the interviewer finishes answering all interview questions with high equal difficulty;
when the high-grade test result does not reach the third preset result, judging whether the number of interview questions with high difficulty answered by the interviewee is less than k paths or not; if the number of the interviewee answered high-difficulty interviewee questions is less than k, continuously outputting one high-difficulty interviewee question, after the interviewee answers, re-determining the high-difficulty test result of the interviewee, and judging whether the re-acquired high-difficulty test result reaches the third preset result; if the number of the interviewer answered interview questions with high equal difficulty is equal to k, representing that the interviewer finishes answering all the interviewer questions with high equal difficulty, wherein k is a positive integer larger than p;
after the interviewer answers all interview questions with high difficulty and the like, determining a high-question test result of the interviewer, and determining the matching degree of the interviewer and the interview theme according to the newly obtained medium-question test result and high-question test result.
9. A matching degree determination apparatus, characterized in that the apparatus comprises:
the processing module is used for outputting n pieces of face test questions with medium difficulty corresponding to the face test subjects, wherein n is any positive integer; determining a test result of the medium questions of the interviewee according to the answer of the interviewee to the interviewee; when the test result of the medium questions does not reach a first preset result, judging whether the number of the interview questions with medium difficulty answered by the interviewer is less than m channels or not; if the number of the medium-difficulty interview questions answered by the interviewee is less than m, continuously outputting one medium-difficulty interview question, re-acquiring the medium-difficulty interview question test result of the interviewee after the interviewee completes answering, and judging whether the re-acquired medium-difficulty interview test result reaches the first preset result; if the number of the medium-difficulty face test questions answered by the interviewee is equal to m, outputting low-difficulty face test questions corresponding to the face test subjects, and determining the low-difficulty test results of the interviewee after the interviewee answers all the low-difficulty face test questions, wherein m is a positive integer larger than n;
and the determining module is used for determining the matching degree of the interviewer and the interview theme according to the newly acquired test result of the medium question and the test result of the low question.
10. An electronic device, comprising: a processor and a memory, the processor and the memory connected;
the memory is used for storing programs;
the processor is configured to execute a program stored in the memory to perform the method of any of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored which, when executed by a computer, performs the method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111105618.0A CN113806516B (en) | 2021-09-22 | 2021-09-22 | Matching degree determining method and device, electronic equipment and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111105618.0A CN113806516B (en) | 2021-09-22 | 2021-09-22 | Matching degree determining method and device, electronic equipment and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113806516A true CN113806516A (en) | 2021-12-17 |
| CN113806516B CN113806516B (en) | 2024-06-14 |
Family
ID=78939946
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111105618.0A Active CN113806516B (en) | 2021-09-22 | 2021-09-22 | Matching degree determining method and device, electronic equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113806516B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160253626A1 (en) * | 2015-02-26 | 2016-09-01 | Chung-Nan Tien | Network Interview System and Interview Method Thereof |
| CN106202453A (en) * | 2016-07-13 | 2016-12-07 | 网易（杭州）网络有限公司 | A kind of multimedia resource recommends method and apparatus |
| CN107680019A (en) * | 2017-09-30 | 2018-02-09 | 百度在线网络技术（北京）有限公司 | A kind of implementation method of Examination Scheme, device, equipment and storage medium |
| CN108831229A (en) * | 2018-03-30 | 2018-11-16 | 上海乂学教育科技有限公司 | A kind of Chinese automatic grading method |
| CN109785698A (en) * | 2017-11-13 | 2019-05-21 | 上海流利说信息技术有限公司 | Method, apparatus, electronic equipment and medium for spoken language proficiency evaluation and test |
| US20190163807A1 (en) * | 2017-11-29 | 2019-05-30 | International Business Machines Corporation | Feature vector profile generation for interviews |
| CN111445200A (en) * | 2020-02-25 | 2020-07-24 | 平安国际智慧城市科技股份有限公司 | Interviewing method and device based on artificial intelligence, computer equipment and storage medium |
| CN111506770A (en) * | 2020-04-22 | 2020-08-07 | 新华智云科技有限公司 | Interview video collection generation method and system |
| WO2021000408A1 (en) * | 2019-07-04 | 2021-01-07 | 平安科技（深圳）有限公司 | Interview scoring method and apparatus, and device and storage medium |
| CN112418779A (en) * | 2020-10-30 | 2021-02-26 | 济南浪潮高新科技投资发展有限公司 | Online self-service interviewing method based on natural language understanding |
| CN112434144A (en) * | 2020-11-23 | 2021-03-02 | 京东数字科技控股股份有限公司 | Method, device, electronic equipment and computer readable medium for generating target problem |
-
2021
- 2021-09-22 CN CN202111105618.0A patent/CN113806516B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160253626A1 (en) * | 2015-02-26 | 2016-09-01 | Chung-Nan Tien | Network Interview System and Interview Method Thereof |
| CN106202453A (en) * | 2016-07-13 | 2016-12-07 | 网易（杭州）网络有限公司 | A kind of multimedia resource recommends method and apparatus |
| CN107680019A (en) * | 2017-09-30 | 2018-02-09 | 百度在线网络技术（北京）有限公司 | A kind of implementation method of Examination Scheme, device, equipment and storage medium |
| CN109785698A (en) * | 2017-11-13 | 2019-05-21 | 上海流利说信息技术有限公司 | Method, apparatus, electronic equipment and medium for spoken language proficiency evaluation and test |
| US20190163807A1 (en) * | 2017-11-29 | 2019-05-30 | International Business Machines Corporation | Feature vector profile generation for interviews |
| CN108831229A (en) * | 2018-03-30 | 2018-11-16 | 上海乂学教育科技有限公司 | A kind of Chinese automatic grading method |
| WO2021000408A1 (en) * | 2019-07-04 | 2021-01-07 | 平安科技（深圳）有限公司 | Interview scoring method and apparatus, and device and storage medium |
| CN111445200A (en) * | 2020-02-25 | 2020-07-24 | 平安国际智慧城市科技股份有限公司 | Interviewing method and device based on artificial intelligence, computer equipment and storage medium |
| CN111506770A (en) * | 2020-04-22 | 2020-08-07 | 新华智云科技有限公司 | Interview video collection generation method and system |
| CN112418779A (en) * | 2020-10-30 | 2021-02-26 | 济南浪潮高新科技投资发展有限公司 | Online self-service interviewing method based on natural language understanding |
| CN112434144A (en) * | 2020-11-23 | 2021-03-02 | 京东数字科技控股股份有限公司 | Method, device, electronic equipment and computer readable medium for generating target problem |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113806516B (en) | 2024-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Murtin et al. | Trust and its determinants: Evidence from the Trustlab experiment | |
| Laupichler et al. | Development of the “Scale for the assessment of non-experts’ AI literacy”–An exploratory factor analysis | |
| US20130246290A1 (en) | Machine-Assisted Legal Assessments | |
| Perlitz et al. | Efficient benchmarking (of language models) | |
| CN109472457B (en) | Loan application online reviewing method and terminal equipment | |
| Birnbaum | Base rates in bayesian inference | |
| Fauzi et al. | Effect of social media addiction on academic performance among nursing students | |
| US9547995B1 (en) | Dynamic instructional course | |
| Aycinena et al. | Measuring norms: Assessing the threat of social desirability bias to the Bicchieri and Xiao elicitation method | |
| Lebrun et al. | Detecting the corruption of online questionnaires by artificial intelligence | |
| CN109003492B (en) | Topic selection device and terminal equipment | |
| Yackee | Does experiential learning improve JD employment outcomes | |
| WO2013126782A1 (en) | Outlier detection tool | |
| CN113806516A (en) | Matching degree determination method and device, electronic equipment and computer readable storage medium | |
| CN110674632A (en) | Method and device for determining security level, storage medium and equipment | |
| CN110782119A (en) | Insurance agent selection method, device and equipment based on artificial intelligence | |
| JP6599534B1 (en) | Information processing apparatus, information processing method, and program | |
| CN114037358A (en) | Academic situation analysis method and system based on college interactive classroom activity data | |
| US9355373B2 (en) | Outlier detection tool | |
| CN113821443A (en) | Application program function detection method, device, equipment and storage medium | |
| Coali et al. | Understanding probabilistic reasoning in innovation | |
| CN113987328A (en) | Topic recommendation method, equipment, server and storage medium | |
| Terveen et al. | Don't Just Add Women and Stir: How Judge Characteristics Affect Outcomes for Female Competitors in High School Debate | |
| US20240221523A1 (en) | Pre-training modeling system and method for predicting educational element | |
| Brandts et al. | Pictures are worth many words: effectiveness of visual communication in dispelling the rent-control misconception |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |