Test method, device and equipment for recruitment recommendation system and storage medium

Abstract

The application discloses a test method, device and equipment for a recruitment recommendation system and a storage medium. The method comprises the following steps: obtaining a target request parameter of a user; calling interfaces of first and second versions of the recruitment recommendation system based on the target request parameter to obtain a plurality of first recommendation results and a plurality of second recommendation results; calculating a first proportion of the first recommendation results that are inconsistent with a reference recommendation result, a second proportion of the second recommendation results that are inconsistent with the reference recommendation result, and a third proportion of the second recommendation results that are inconsistent with the first recommendation result; when the missing second recommendation result is expected to be missing, and the third proportion is less than a first preset threshold, if the reference recommendation result is of interest to the user, and the second proportion is less than or equal to the first proportion, the test is passed; if the reference recommendation result is not of interest to the user, and the second proportion is greater than the first proportion, the test is passed. The application can improve the test efficiency and test accuracy of the recruitment recommendation system.

Description

Technical Field

[0001] The present application relates to the field of big data service technology, and in particular to a testing method, apparatus, device, and storage medium for a recruitment recommendation system. Background Art

[0002] Recruitment recommendation systems can recommend suitable resumes based on the company's needs and the talent's skills, experience, interests, and other characteristics, helping companies find suitable talent more quickly and accurately. At the same time, recruitment recommendation systems can also recommend suitable positions based on the job seekers' needs, helping job seekers find suitable positions more quickly and accurately.

[0003] As user needs continue to evolve, recruitment recommendation systems are being updated and iterated at an increasingly rapid pace. This necessitates extensive regression testing for numerous iterations of recruitment recommendation systems. Manual testing, however, is no longer sufficient to meet the demands of rapid iteration and continuous optimization. It is labor-intensive and time-consuming, with low returns. Furthermore, some iterations may only require a few code optimizations that don't impact business logic, yet still require testing, consuming significant testers' time.

[0004] In addition, it is usually difficult to determine whether the effectiveness of the new version of the recruitment recommendation system has improved, which may result in a large deviation between the final test results and the user's expected results, and the test accuracy is low. Summary of the Invention

[0005] In view of this, embodiments of the present application provide a testing method, device, electronic device, computer-readable storage medium, and computer program product for a recruitment recommendation system, which are used to solve at least one of the above technical problems.

[0006] An embodiment of the present application provides a testing method for a recruitment recommendation system, the testing method for a recruitment recommendation system comprising: obtaining a target request parameter of a user in the recruitment recommendation system, the target request parameter including a screening condition input by the user and a reference recommendation result that the user is interested in or not interested in; calling an interface of a first version of the recruitment recommendation system based on the target request parameter to obtain a plurality of first recommendation results, and calling an interface of a second version of the recruitment recommendation system based on the target request parameter to obtain a plurality of second recommendation results; calculating a first proportion of first recommendation results that are inconsistent with the reference recommendation results among the plurality of first recommendation results, and calculating a second proportion of second recommendation results that are inconsistent with the reference recommendation results among the plurality of second recommendation results; comparing the plurality of first recommendation results with the plurality of second recommendation results, And based on the comparison results, determine whether there is data missing in the multiple second recommendation results compared with the multiple first recommendation results, and calculate the third proportion of the second recommendation results that are inconsistent with the first recommendation results in the multiple second recommendation results; if the missing second recommendation result in the multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold, then determine the size relationship between the first proportion and the second proportion, where, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then determine that the second version of the recruitment recommendation system test has passed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then determine that the second version of the recruitment recommendation system test has passed.

[0007] According to some embodiments of the present application, optionally, the testing method for the recruitment recommendation system also includes: if the missing second recommendation result in multiple second recommendation results is inconsistent with the expected missing recommendation result, and the number of missing second recommendation results is greater than a second preset threshold, then it is determined that the second version of the recruitment recommendation system test has failed; if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is greater than the first proportion, then it is determined that the second version of the recruitment recommendation system test has failed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is less than the first proportion, then it is determined that the second version of the recruitment recommendation system test has failed.

[0008] According to some embodiments of the present application, optionally, the reference recommendation result is a reference recommended position, the first recommendation result is the first recommended position, and the second recommendation result is the second recommended position; or, the reference recommendation result is a reference recommended resume, the first recommendation result is the first recommended resume, and the second recommendation result is the second recommended resume; wherein, in the case where the reference recommendation result is a reference recommended resume that the user is interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, major, education, work experience and / or personality and hobby description that the user is interested in; in the case where the reference recommendation result is a reference recommended resume that the user is not interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, major, education, work experience and / or personality and hobby description that the user is not interested in.

[0009] According to some embodiments of the present application, optionally, before calling the interface of the first version of the recruitment recommendation system based on the target request parameters to obtain multiple first recommendation results, the testing method for the recruitment recommendation system also includes: turning off the switch for updating the recommended data in the target database, the recommended data including position data or resume data; wherein the first version of the recruitment recommendation system is used to query multiple first recommendation results that meet the target request parameter requirements from the recommendation data stored in the target database, and the second version of the recruitment recommendation system is used to query multiple second recommendation results that meet the target request parameter requirements from the recommendation data stored in the target database.

[0010] According to some embodiments of the present application, optionally, before turning off the switch for updating recommended data in the target database, the testing method for the recruitment recommendation system also includes: synchronizing recommended data in a source data table from at least one recruitment channel to a target data table in the target database, wherein each piece of recommended data includes multiple fields arranged in columns; setting multiple second target data columns in the target data table that need to be compared with multiple first target data columns in the source data table, and aligning the multiple first target data columns with the multiple second target data columns; comparing the multiple first target data columns with the multiple second target data columns to obtain difference results, the difference results including first data unique to the multiple first target data columns, second data unique to the multiple second target data columns, and at least one item in the second target data columns that is different from the data in the first target data columns; performing a quality score on this data synchronization based on the difference results, and analyzing problems existing in this data synchronization; outputting an evaluation report, the evaluation report showing the difference results, the quality score of this data synchronization, and problems existing in this data synchronization.

[0011] According to some embodiments of the present application, optionally, a quality score is performed for this data synchronization, and problems existing in this data synchronization are analyzed, including: determining whether the first data missing in the target data table is the expected missing data; if not, deducting points according to the number or proportion of the first data missing in the target data table according to a preset deduction rule, and determining that the problems existing in this data synchronization at least include missing data; if there are differences between the data in the second target data column and the corresponding first target data column, deducting points according to the number or proportion of the data with differences in the second target data column according to a preset deduction rule, and determining that the problems existing in this data synchronization at least include data synchronization rule errors; wherein the preset deduction rule is that starting from a preset total score, if the number or proportion of the first data missing in the target data table is x times the first preset deduction threshold, x times the first preset score is deducted; if the number or proportion of the data with differences in the second target data column is y times the second preset deduction threshold, y times the second preset score is deducted; the multiples are rounded up, and both x and y are positive integers.

[0012] According to some embodiments of the present application, optionally, the testing method for the recruitment recommendation system also includes: after finding the second target data column that has data different from the first target data column, executing the test case corresponding to the second target data column that has data different from the first target data column to find the cause of the data difference.

[0013] An embodiment of the present application provides a testing device for a recruitment recommendation system, and the testing device for a recruitment recommendation system includes: an acquisition module for acquiring a target request parameter of a user in the recruitment recommendation system, the target request parameter including a screening condition input by the user and a reference recommendation result that the user is interested in or not interested in; a calling module for calling an interface of a first version of the recruitment recommendation system based on the target request parameter to obtain a plurality of first recommendation results, and calling an interface of a second version of the recruitment recommendation system based on the target request parameter to obtain a plurality of second recommendation results; a first calculation module for calculating a first proportion of first recommendation results that are inconsistent with the reference recommendation results in the plurality of first recommendation results, and calculating a second proportion of second recommendation results that are inconsistent with the reference recommendation results in the plurality of second recommendation results; a second calculation module for comparing the plurality of first recommendation results with the plurality of second recommendation results. The second recommendation results are compared, and it is determined whether the multiple second recommendation results are missing data compared with the multiple first recommendation results based on the comparison results, and the third proportion of the second recommendation results that are inconsistent with the first recommendation results in the multiple second recommendation results is calculated; a judgment module is used to judge the size relationship between the first proportion and the second proportion if the missing second recommendation result in the multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold, wherein if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed.

[0014] An embodiment of the present application provides an electronic device, which includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the steps of the method described above are implemented.

[0015] An embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the steps of the method described above are implemented.

[0016] An embodiment of the present application provides a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the steps of the method described above are implemented.

[0017] The testing method, device, electronic device, computer-readable storage medium and computer program product for a recruitment recommendation system provided by the embodiments of the present application, on the one hand, for the second version (i.e., new version) of the recruitment recommendation system, by comparing the multiple second recommendation results obtained by the second version of the recruitment recommendation system with the multiple first recommendation results obtained by the first version of the recruitment recommendation system and the reference recommendation results that the user is interested in or not interested in, and obtaining the recruitment recommendation system test results based on the multiple comparison results, it is possible to achieve automated testing of the new version of the recruitment recommendation system, significantly improve test efficiency, reduce the influence of human factors, and help improve the accuracy and reliability of test results, and reduce test costs; on the other hand, by comparing the multiple second recommendation results with the multiple first recommendation results, the data missing situation of the second recommendation results and the data difference with the first recommendation results are taken into account. When the data missing situation and the data difference situation are both as expected, the effect of the second version of the recruitment recommendation system is evaluated by comparing the size relationship between the first proportion and the second proportion, so that the final test result is more in line with user expectations, further improving the test accuracy and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings in the embodiments of the present application.

[0019] Figure 1 A flowchart of a testing method for a recruitment recommendation system provided in an embodiment of the present application.

[0020] Figure 2 A flowchart of step 14 in the testing method for a recruitment recommendation system provided in an embodiment of the present application.

[0021] Figure 3 A schematic diagram of the structure of a testing device for a recruitment recommendation system provided in an embodiment of the present application.

[0022] Figure 4 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirit of the present application clearer and more thorough, so that those skilled in the art can better understand and implement the principles and spirit of the present application. The exemplary embodiments provided herein are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments herein, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of this application.

[0024] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0025] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0026] It will be apparent to those skilled in the art that various modifications and variations can be made in this application without departing from the spirit or scope of this application. Therefore, this application is intended to cover modifications and variations of this application that fall within the scope of the corresponding claims (technical solutions claimed for protection) and their equivalents. It should be noted that the embodiments provided in the examples of this application can be combined with each other without contradiction.

[0027] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate understanding of the embodiments of the present application, the present application first specifically describes the problems existing in the related art:

[0028] Recruitment recommendation systems can recommend suitable resumes based on the company's needs and the talent's skills, experience, interests, and other characteristics, helping companies find suitable talent more quickly and accurately. At the same time, recruitment recommendation systems can also recommend suitable positions based on the job seekers' needs, helping job seekers find suitable positions more quickly and accurately.

[0029] As user needs continue to evolve, recruitment recommendation systems are being updated and iterated at an increasingly rapid pace. This necessitates extensive regression testing for numerous iterations of recruitment recommendation systems. Manual testing, however, is no longer sufficient to meet the demands of rapid iteration and continuous optimization. It is labor-intensive and time-consuming, with low returns. Furthermore, some iterations may only require a few code optimizations that don't impact business logic, yet still require testing, consuming significant testers' time.

[0030] In addition, it is usually difficult to determine whether the effectiveness of the new version of the recruitment recommendation system has improved, which may result in a large deviation between the final test results and the user's expected results, and the test accuracy is low.

[0031] In view of the above research findings of the inventors, the embodiments of the present application provide a testing method, apparatus, device and computer-readable storage medium for a recruitment recommendation system, which can solve at least one of the above technical problems existing in the related art.

[0032] The following first introduces the testing method for the recruitment recommendation system provided in the embodiment of the present application.

[0033] Figure 1 A flow chart of a testing method for a recruitment recommendation system provided in an embodiment of the present application. Figure 1 As shown, the testing method for the recruitment recommendation system provided in the embodiment of the present application may include the following steps S101 to S105.

[0034] S101: Obtain target request parameters of the user in the recruitment recommendation system. The target request parameters include the screening conditions input by the user and the reference recommendation results that the user is interested in or not interested in. The reference recommendation results are determined based on the user's historical behavior data.

[0035] Specifically, the recruitment recommendation system can support reporting request parameters. For example, in job recommendations, the request parameters can include user-entered screening criteria for job screening, such as the address, salary requirements, and job functions of the current request. In S101, the reporting request parameter function switch of the recruitment recommendation system can be turned on first to record the online user's request parameters into a file or database. The request parameters can be the request parameters actually entered by the user. Then, the request parameters that meet the conditions are filtered out from the above request parameters as the target request parameters, such as request parameters for a certain period of time, or request parameters that include specified screening criteria. In job recommendations, for example, the specified screening criteria can be one or more of office location, salary requirements, job functions, and industry. In resume recommendations, for example, the specified screening criteria can be one or more of office location, age, gender, major, education, work experience, personality and hobby description, salary requirements, job functions, and industry.

[0036] In an embodiment of the present application, the target request parameter may also include reference recommendation results that the user is interested in or not interested in. For example, in job recommendations, the reference recommendation results may be reference recommended positions that the user is interested in or not interested in, such as software development engineer. For example, in resume recommendations, the reference recommendation results may be reference recommended resumes that the user is interested in or not interested in, such as resumes for the region, age, gender, major, education, work experience, and / or personality and hobbies that the user is interested in, or resumes for the region, age, gender, major, education, work experience, and / or personality and hobbies that the user is not interested in.

[0037] In some embodiments, the reference recommendation results that a user is interested in or not interested in can be the user's own input of the desired or undesired recommendation results, or can be determined based on the user's historical behavior data in the recruitment recommendation system. The user's historical behavior data includes, but is not limited to, positions that the user frequently browses, saves, and / or applies to in the recruitment recommendation system, resumes that the user views, saves, and / or invites for interviews in the recruitment recommendation system, positions or resumes that the user frequently closes in the recruitment recommendation system and / or has been listed as uninterested by the user.

[0038] S102: Based on the target request parameters, the interface of the first version of the recruitment recommendation system is called to obtain multiple first recommendation results, and based on the target request parameters, the interface of the second version of the recruitment recommendation system is called to obtain multiple second recommendation results.

[0039] In S102, the interface of the first version of the recruitment recommendation system can be called based on the target request parameters. The first version of the recruitment recommendation system can be used to query multiple first recommendation results that meet the target request parameter requirements from the recommendation data stored in the target database. The target database is used to store recommendation data, and the recommendation data may include position data or resume data. For example, when recommending positions, the first version of the recruitment recommendation system can be used to query multiple first recommended positions that meet the target request parameter requirements from the numerous position data stored in the target database. For example, when recommending resumes, the first version of the recruitment recommendation system can be used to query multiple first recommended resumes that meet the target request parameter requirements from the numerous resume data stored in the target database.

[0040] Similarly, the interface of the second version of the recruitment recommendation system can be called based on the target request parameters. The second version of the recruitment recommendation system can be used to query the recommendation data stored in the target database for multiple second recommendation results that meet the target request parameter requirements. For example, when recommending positions, the second version of the recruitment recommendation system can be used to query the numerous position data stored in the target database for multiple second recommended positions that meet the target request parameter requirements. For example, when recommending resumes, the second version of the recruitment recommendation system can be used to query the numerous resume data stored in the target database for multiple second recommended resumes that meet the target request parameter requirements.

[0041] In some examples, the first version of the recruitment recommendation system may be an old version of the recruitment recommendation system, and the second version of the recruitment recommendation system may be a new version of the recruitment recommendation system.

[0042] S103: Calculate a first proportion of first recommendation results that are inconsistent with a reference recommendation result among the plurality of first recommendation results, and calculate a second proportion of second recommendation results that are inconsistent with the reference recommendation result among the plurality of second recommendation results.

[0043] For example, taking job recommendations as an example, suppose the user is interested in a software development engineer as the reference recommendation result (i.e., the reference recommended position). The first version of the recruitment recommendation system generates multiple first recommendation results for positions 1, 2, and 3, while the second version of the recruitment recommendation system generates multiple second recommendation results for positions 1, 2, and 4. Positions 1 through 3 are all software development engineers, while position 4 is not. Therefore, the first proportion is 0 / 3, and the second proportion is 1 / 3.

[0044] S104: Compare the multiple first recommendation results with the multiple second recommendation results, and determine whether the multiple second recommendation results are missing data compared with the multiple first recommendation results based on the comparison results, and calculate a third proportion of the second recommendation results that are inconsistent with the first recommendation results in the multiple second recommendation results.

[0045] Using job recommendations as an example, let's assume the first version of the recruitment recommendation system generates multiple first recommendations for positions 1, 2, and 3, and the second version of the recruitment recommendation system generates multiple second recommendations for positions 1, 2, 4, and 5. Compared to the first recommendations, the second recommendations are missing data for position 3, indicating data loss. Since positions 4 and 5 in the second recommendations are inconsistent with positions 1, 2, and 3 in the first recommendations, the third percentage can be 1 / 2.

[0046] S105: If the missing second recommendation result in the multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold, then the size relationship between the first proportion and the second proportion is determined, wherein, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then the second version of the recruitment recommendation system is determined to have passed the test; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then the second version of the recruitment recommendation system is determined to have passed the test.

[0047] In actual applications, some of the first recommendation results obtained by the first version of the recruitment recommendation system may be inaccurate. For example, the reference recommendation result (i.e., the reference recommended position) that the user is interested in is software development engineer, while some of the first recommendation results obtained by the first version of the recruitment recommendation system are not software development engineers. Therefore, after version iteration, it is expected that these inaccurate recommendation results can be deleted from the multiple second recommendation results obtained by the second version of the recruitment recommendation system, and then the second recommendation results missing from the multiple second recommendation results may be the recommendation results that are expected to be missing, or of course, they may not be the recommendation results that are expected to be missing. In some examples, the recommendation results that are expected to be missing may be recommendation results that are inconsistent with the reference recommendation results.

[0048] Theoretically, there may be differences between the multiple second recommendation results obtained by the second version of the recruitment recommendation system and the multiple first recommendation results obtained by the first version of the recruitment recommendation system, but the differences will not be too large.

[0049] Therefore, in S105, considering the data missing of the second recommendation result and the data difference with the first recommendation result, if the missing second recommendation result in the plurality of second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold, it indicates that the missing second recommendation result in the plurality of second recommendation results is the expected missing recommendation result, and the data difference is as expected. Then, the relationship between the first proportion and the second proportion can be further determined. The first preset threshold can be flexibly adjusted according to actual conditions, and this application does not limit it.

[0050] In some embodiments, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, it means that compared with the multiple first recommendation results, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is interested in has become smaller, and the effect of the second version of the recruitment recommendation system has improved. It can be determined that the second version of the recruitment recommendation system has passed the test.

[0051] In other embodiments, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, it means that compared with the multiple first recommendation results, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is not interested in has become larger, that is, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is interested in has become smaller, and the effect of the second version of the recruitment recommendation system has been improved, then it can be determined that the second version of the recruitment recommendation system has passed the test.

[0052] The embodiments of the present application provide a testing method for a recruitment recommendation system. On the one hand, for the second version (i.e., new version) of the recruitment recommendation system, by comparing the multiple second recommendation results obtained by the second version of the recruitment recommendation system with the multiple first recommendation results obtained by the first version of the recruitment recommendation system and the reference recommendation results that the user is interested in or not interested in, the recruitment recommendation system test results are obtained based on the multiple comparison results. This can realize automated testing of the new version of the recruitment recommendation system, significantly improve test efficiency, reduce the influence of human factors, and help improve the accuracy and reliability of test results, and reduce test costs; on the other hand, by comparing the multiple second recommendation results with the multiple first recommendation results, the data missing situation of the second recommendation results and the data difference with the first recommendation results are taken into account. When the data missing situation and the data difference situation are both as expected, the effect of the second version of the recruitment recommendation system is evaluated by comparing the size relationship between the first proportion and the second proportion, so that the final test result is more in line with user expectations, further improving the test accuracy and user experience.

[0053] According to some embodiments of the present application, optionally, the testing method for the recruitment recommendation system provided by the embodiments of the present application may further include the following steps 1 and 2.

[0054] Step 1: If the missing second recommendation results in the multiple second recommendation results are inconsistent with the expected missing recommendation results, and the number of missing second recommendation results is greater than the second preset threshold, it is determined that the second version of the recruitment recommendation system test has failed.

[0055] If the missing second recommendation result in the plurality of second recommendation results is inconsistent with the expected missing recommendation result, it indicates that the missing second recommendation result in the plurality of second recommendation results is not the expected missing recommendation result. In this case, if the number of missing second recommendation results is greater than a second preset threshold, it can be determined that the second version of the recruitment recommendation system has failed the test.

[0056] Step 2: If the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is greater than the first proportion, then the second version of the recruitment recommendation system test is determined to have failed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is less than the first proportion, then the second version of the recruitment recommendation system test is determined to have failed.

[0057] Specifically, if the target request parameter uses the reference recommendation results that the user is interested in, and the second proportion is greater than the first proportion, it means that compared with the multiple first recommendation results, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is interested in has increased, and the effectiveness of the second version of the recruitment recommendation system has decreased, then the second version of the recruitment recommendation system is judged to have failed the test.

[0058] If the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is smaller than the first proportion, it means that compared with the multiple first recommendation results, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is not interested in has become smaller, that is, the difference proportion between the multiple second recommendation results and the reference recommendation results that the user is interested in has become larger, and the effectiveness of the second version of the recruitment recommendation system has declined, then it is determined that the second version of the recruitment recommendation system test has failed.

[0059] In this way, by comparing multiple second recommendation results with multiple first recommendation results, the data missing situation of the second recommendation results and the data difference with the first recommendation results are taken into consideration. When the data missing situation and the data difference situation are in line with expectations, the effectiveness of the second version of the recruitment recommendation system is evaluated by comparing the size relationship between the first proportion and the second proportion, so that the final test results are more in line with user expectations, further improving the test accuracy and user experience.

[0060] According to some embodiments of the present application, the recruitment recommendation system can optionally be used for job recommendation. The reference recommendation result can be a reference recommended job, that is, a reference recommended job that the user is interested in or not interested in. The reference recommended job that the user is interested in or not interested in can be a recommended job that the user inputs as desired or undesirable, or can be determined by the user's historical behavior data of the recruitment recommendation system. Accordingly, the first recommendation result can be the first recommended job, and the second recommendation result can be the second recommended job.

[0061] Taking the reference recommended position that the user is interested in as software development engineer as an example, in S103, the first recommendation result inconsistent with the reference recommendation result refers to the first recommended position that is inconsistent with software development engineer (i.e., not a software development engineer), and the second recommendation result inconsistent with the reference recommendation result refers to the second recommended position that is inconsistent with software development engineer (i.e., not a software development engineer).

[0062] In this way, for the new version of the recruitment recommendation system for job recommendation, this application can realize automated testing of the new version of the recruitment recommendation system, significantly improve test efficiency, reduce the impact of human factors, and help improve the accuracy and reliability of test results, and reduce test costs; on the other hand, by comparing multiple second recommended positions with multiple first recommended positions, the data missing situation of the second recommended positions and the data difference with the first recommended positions are taken into account. When the data missing situation and the data difference situation are both in line with expectations, the effect of the second version of the recruitment recommendation system is evaluated by comparing the size relationship between the first proportion and the second proportion, so that the final test position is more in line with user expectations, further improving the test accuracy and user experience.

[0063] According to other embodiments of the present application, the recruitment recommendation system can optionally be used for job recommendation. The reference recommendation result can be a reference recommended resume, that is, a reference recommended resume that the user is interested in or not interested in, such as a resume of the region, age, gender, major, education, work experience and / or personality hobbies that the user is interested in, or a resume of the region, age, gender, major, education, work experience and / or personality hobbies that the user is not interested in. The reference recommended resume that the user is interested in or not interested in can be a recommended resume that the user expects to obtain or does not expect to obtain input by the user himself, or it can be determined by the user's historical behavior data of the recruitment recommendation system. Accordingly, the first recommendation result can be the first recommended resume, and the second recommendation result can be the second recommended resume.

[0064] In S103, when the reference recommendation result is a reference recommended resume that the user is interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, education level, work experience, and / or personality and hobby description that the user is interested in. That is, when the reference recommendation result is a reference recommended resume that the user is interested in, the first recommended resume that is inconsistent with the reference recommended resume is the first recommended resume that is inconsistent with the region, age, gender, education level, work experience, and / or personality and hobby description that the user is interested in, and the second recommended resume that is inconsistent with the reference recommended resume is the second recommended resume that is inconsistent with the region, age, gender, education level, work experience, and / or personality and hobby description that the user is interested in.

[0065] In the case where the reference recommendation result is a reference recommended resume that the user is not interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, education level, work experience and / or personality and hobby description that the user is not interested in. That is, in the case where the reference recommendation result is a reference recommended resume that the user is not interested in, the first recommended resume that is inconsistent with the reference recommended resume is the first recommended resume that is inconsistent with the region, age, gender, education level, work experience and / or personality and hobby description that the user is not interested in, and the second recommended resume that is inconsistent with the reference recommended resume is the second recommended resume that is inconsistent with the region, age, gender, education level, work experience and / or personality and hobby description that the user is not interested in.

[0066] In this way, for the new version of the recruitment recommendation system for resume recommendation, this application can realize automated testing of the new version of the recruitment recommendation system, significantly improve test efficiency, reduce the impact of human factors, and help improve the accuracy and reliability of test results, and reduce test costs; on the other hand, by comparing multiple second recommended resumes with multiple first recommended resumes, the data missing conditions of the second recommended resumes and the data differences with the first recommended resumes are taken into account. When the data missing conditions and data differences are in line with expectations, the relationship between the first proportion and the second proportion is compared to evaluate whether the effect of the second version of the recruitment recommendation system has been improved, so that the final test resume is more in line with user expectations, further improving the test accuracy and user experience.

[0067] According to some embodiments of the present application, optionally, the testing method for the recruitment recommendation system provided in the embodiments of the present application may further include the following steps:

[0068] For each filtering condition input by the user in the target request parameter, the proportion of the second recommendation results that meet the requirements of each filtering condition in the plurality of second recommendation results can be calculated.

[0069] Taking resume recommendation as an example, the proportion of second recommended resumes whose industries meet the industry screening conditions, the proportion of second recommended resumes whose academic qualifications meet the academic qualification screening conditions, the proportion of second recommended resumes whose ages meet the age screening conditions, etc.

[0070] Accordingly, in S105, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, and the proportion of the second recommendation results that meet the requirements of each screening condition in the multiple second recommendation results is greater than or equal to the preset threshold, then the second version of the recruitment recommendation system is determined to have passed the test. Alternatively, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, and the proportion of the second recommendation results that meet the requirements of each screening condition in the multiple second recommendation results is greater than or equal to the preset threshold, then the second version of the recruitment recommendation system is determined to have passed the test.

[0071] In this way, whether the second recommendation result meets each filtering condition is taken into consideration. By calculating the proportion of second recommendation results that meet each filtering condition in multiple second recommendation results, and adding the judgment of one or more proportions to obtain the test result, the test accuracy and user experience can be further improved.

[0072] According to some embodiments of the present application, optionally, before S102, calling the interface of the first version of the recruitment recommendation system based on the target request parameters to obtain multiple first recommendation results, the testing method for the recruitment recommendation system provided in the embodiments of the present application may further include the following steps:

[0073] Disable the switch for updating recommended data in the target database. Recommended data includes job data or resume data.

[0074] As previously described, the first version of the recruitment recommendation system can be used to query the recommendation data stored in the target database for multiple first recommendation results that meet the target request parameter requirements. The second version of the recruitment recommendation system can be used to query the recommendation data stored in the target database for multiple second recommendation results that meet the target request parameter requirements.

[0075] There may be a time difference between calling the interface of the first version of the recruitment recommendation system and calling the interface of the second version of the recruitment recommendation system. By turning off the switch for updating the recommended data in the target database, it is possible to effectively avoid differences in the recommendation results of the two versions of the recruitment recommendation system that are not caused by data updates in the target database, thereby further improving the test accuracy.

[0076] According to some embodiments of the present application, optionally, before turning off the switch for updating the recommendation data in the target database, the testing method for the recruitment recommendation system provided by the embodiment of the present application may further include the following steps 11 to 15.

[0077] Step 11: Synchronize the recommended data in the source data table from at least one recruitment channel to the target data table in the target database, where each piece of recommended data includes multiple fields arranged in columns.

[0078] In step 11, recommendation data from source data tables of one or more recruitment channels can be synchronized to a target data table in a target database. The recommendation data can be position data or resume data. Recruitment channels include, but are not limited to, recruitment websites, recruitment platforms, internal recommendations, and / or social media apps, and are not limited in this application.

[0079] Each piece of recommended data may include multiple fields arranged in columns. For example, if the recommended data is resume data, each resume data may include multiple fields such as resume ID, name, age, gender, major, education, place of residence, salary requirements, work experience, and / or personality and hobbies description.

[0080] Assuming that the source data table of any recruitment channel is data table A and the target data table is data table B, in step 11, the recommended data in data table A that meets the preset synchronization conditions can be synchronized to data table B. The preset synchronization conditions can be flexibly set according to actual conditions and are not limited in this application.

[0081] Step 12: Set multiple second target data columns in the target data table that need to be compared with the multiple first target data columns in the source data table, and align the multiple first target data columns with the multiple second target data columns.

[0082] Step 13: Compare the plurality of first target data columns with the plurality of second target data columns to obtain difference results, where the difference results include first data unique to the plurality of first target data columns, second data unique to the plurality of second target data columns, and at least one item in the second target data columns that differs from the data in the first target data columns.

[0083] The unique first data in the first target data rows are data present in the first target data rows but missing in the second target data rows. The unique second data in the second target data rows are data present in the second target data rows but missing in the first target data rows.

[0084] Step 14: Based on the difference results, perform a quality score on this data synchronization and analyze any problems that exist in this data synchronization.

[0085] Figure 2 This is a flow chart of step 14 in the test method for the recruitment recommendation system provided in the embodiment of the present application. Figure 2 As shown, in some specific embodiments, optionally, step 14: performing a quality score on the data synchronization and analyzing problems existing in the data synchronization may include the following steps 141 and 142.

[0086] Step 141: Determine whether the first data missing in the target data table is the expected missing data. If not, deduct points according to the preset deduction rules based on the number or proportion of the first data missing in the target data table, and determine that the problem existing in this data synchronization at least includes missing data.

[0087] Step 142: If there is a difference between the data in the second target data column and the corresponding first target data column, points are deducted according to the preset deduction rules based on the number or proportion of different data in the second target data column, and it is determined that the problem with this data synchronization at least includes a data synchronization rule error.

[0088] Among them, the preset deduction rule is that starting from the preset total score, if the number or proportion of the first data missing in the target data table is x times the first preset deduction threshold, then x times the first preset score will be deducted; if the number or proportion of the data with differences in the second target data column is y times the second preset deduction threshold, then y times the second preset score will be deducted; the multiples are rounded up, and x and y are both positive integers.

[0089] It should be noted that the sizes of the preset total score, the first preset deduction threshold, the second preset deduction threshold, the first preset score and the second preset score can be flexibly adjusted according to actual conditions, and this application does not limit this.

[0090] Both x and y can be integers rounded up. For example, when the number or proportion of missing first data in the target data table is 1.3 times the first preset deduction threshold, x = 2. When the number or proportion of different data in the second target data column is 2.5 times the second preset deduction threshold, y = 3.

[0091] In some embodiments, optionally, the first preset score may be greater than the second preset score.

[0092] Severe data loss will result in a higher penalty, while moderate data synchronization rule errors may result in lower penalty points. If the final score after deductions is less than the preset threshold, the data synchronization quality will be considered unqualified.

[0093] Step 15: Output an evaluation report, which displays the discrepant results, the quality score of this data synchronization, and any problems with this data synchronization.

[0094] In this way, when synchronizing recommended data, by finding data missing and differences in the target data table, performing quality scoring and analyzing the problems existing in this data synchronization, automated testing of data synchronization is achieved, and various analyses such as quality analysis and effect analysis are implemented, which helps developers quickly and accurately understand the details of this data synchronization.

[0095] According to some embodiments of the present application, optionally, the testing method for the recruitment recommendation system provided in the embodiments of the present application may further include the following steps:

[0096] After finding the second target data column having data different from the first target data column, a test case corresponding to the second target data column having data different from the first target data column is executed to find the cause of the data difference.

[0097] Specifically, after finding the second target data column that has data different from the first target data column, only the test case corresponding to the second target data column that has data different from the first target data column can be executed, so as to quickly find the cause of the difference in data in the second target data column, reduce the testing workload, shorten the testing time, and improve the testing efficiency.

[0098] Based on the same technical concept as the testing method for the recruitment recommendation system provided in the above embodiment, the present application also provides a testing device for the recruitment recommendation system.

[0099] Figure 3 A structural diagram of a testing device for a recruitment recommendation system provided in an embodiment of the present application. Figure 3 As shown, the testing device 30 for the recruitment recommendation system provided in the embodiment of the present application may include the following modules:

[0100] The acquisition module 301 is used to obtain the target request parameters of the user in the recruitment recommendation system, where the target request parameters include the screening conditions input by the user and the reference recommendation results that the user is interested in or not interested in;

[0101] A calling module 302 is configured to call an interface of the first version of the recruitment recommendation system based on the target request parameters to obtain a plurality of first recommendation results, and to call an interface of the second version of the recruitment recommendation system based on the target request parameters to obtain a plurality of second recommendation results;

[0102] A first calculation module 303 is configured to calculate a first proportion of first recommendation results inconsistent with a reference recommendation result among the plurality of first recommendation results, and calculate a second proportion of second recommendation results inconsistent with the reference recommendation result among the plurality of second recommendation results;

[0103] A second calculation module 304 is configured to compare the plurality of first recommendation results with the plurality of second recommendation results, determine whether data is missing from the plurality of second recommendation results based on the comparison result, and calculate a third percentage of second recommendation results that are inconsistent with the first recommendation results in the plurality of second recommendation results;

[0104] The judgment module 305 is used to judge the size relationship between the first proportion and the second proportion if the missing second recommendation result in multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold value, wherein, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed.

[0105] The embodiment of the present application provides a testing device for a recruitment recommendation system. On the one hand, for the second version (i.e., the new version) of the recruitment recommendation system, by comparing the multiple second recommendation results obtained by the second version of the recruitment recommendation system with the multiple first recommendation results obtained by the first version of the recruitment recommendation system and the reference recommendation results that the user is interested in or not interested in, the recruitment recommendation system test results are obtained according to the multiple comparison results, thereby realizing automated testing of the new version of the recruitment recommendation system, significantly improving the testing efficiency, reducing the influence of human factors, and being conducive to improving the accuracy and reliability of the test results and reducing the testing cost; on the other hand, by comparing the multiple second recommendation results with the multiple first recommendation results, the data missing situation of the second recommendation results and the data difference with the first recommendation results are taken into account. When the data missing situation and the data difference situation are both as expected, the effect of the second version of the recruitment recommendation system is evaluated by comparing the size relationship between the first proportion and the second proportion, so that the final test result is more in line with the user's expectations, further improving the test accuracy and user experience.

[0106] According to some embodiments of the present application, optionally, the judgment module 305 can also be used to determine that the second version of the recruitment recommendation system test has failed if the missing second recommendation result in multiple second recommendation results is inconsistent with the expected missing recommendation result, and the number of missing second recommendation results is greater than a second preset threshold; if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is greater than the first proportion, then the second version of the recruitment recommendation system test has failed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is less than the first proportion, then the second version of the recruitment recommendation system test has failed.

[0107] According to some embodiments of the present application, the reference recommendation result may be a reference recommended position, the first recommendation result may be a first recommended position, and the second recommendation result may be a second recommended position. Alternatively, the reference recommendation result may be a reference recommended resume, the first recommendation result may be a first recommended resume, and the second recommendation result may be a second recommended resume.

[0108] Among them, when the reference recommendation result is a reference recommended resume that the user is interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, education level, work experience and / or personality and hobby description that the user is interested in; when the reference recommendation result is a reference recommended resume that the user is not interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, education level, work experience and / or personality and hobby description that the user is not interested in.

[0109] According to some embodiments of the present application, the test device 30 for a recruitment recommendation system provided in the embodiments of the present application may optionally further include a shutdown module for disabling a switch for updating recommended data in the target database, where the recommended data includes position data or resume data. The first version of the recruitment recommendation system is configured to query the recommended data stored in the target database for multiple first recommendation results that meet the target request parameter requirements, and the second version of the recruitment recommendation system is configured to query the recommended data stored in the target database for multiple second recommendation results that meet the target request parameter requirements.

[0110] According to some embodiments of the present application, optionally, the testing device 30 for a recruitment recommendation system provided in the embodiments of the present application may further include a data synchronization analysis module for synchronizing recommendation data in a source data table from at least one recruitment channel to a target data table in a target database, where each recommendation data includes multiple fields arranged in columns; setting multiple second target data columns in the target data table that need to be compared with multiple first target data columns in the source data table, and aligning the multiple first target data columns with the multiple second target data columns; comparing the multiple first target data columns with the multiple second target data columns to obtain difference results, the difference results including first data unique to the multiple first target data columns, second data unique to the multiple second target data columns, and at least one item in the second target data columns that is different from the data in the first target data columns; performing a quality score on this data synchronization based on the difference results, and analyzing problems existing in this data synchronization; outputting an evaluation report, the evaluation report showing the difference results, the quality score of this data synchronization, and problems existing in this data synchronization.

[0111] According to some embodiments of the present application, optionally, the data synchronization analysis module can be specifically used to determine whether the first data missing in the target data table is the expected missing data. If not, points are deducted according to the preset deduction rules based on the number or proportion of the first data missing in the target data table, and it is determined that the problem with this data synchronization at least includes missing data; if there is a difference between the data in the second target data column and the corresponding first target data column, points are deducted according to the preset deduction rules based on the number or proportion of the data with differences in the second target data column, and it is determined that the problem with this data synchronization at least includes an error in the data synchronization rule.

[0112] Among them, the preset deduction rule is that starting from the preset total score, if the number or proportion of the first data missing in the target data table is x times the first preset deduction threshold, then x times the first preset score will be deducted; if the number or proportion of the data with differences in the second target data column is y times the second preset deduction threshold, then y times the second preset score will be deducted; the multiples are rounded up, and x and y are both positive integers.

[0113] According to some embodiments of the present application, optionally, the testing device 30 for a recruitment recommendation system provided in the embodiments of the present application may further include an execution module for executing a test case corresponding to the second target data column that is different from the data in the first target data column after finding the second target data column that is different from the data in the first target data column, so as to find out the cause of the data difference.

[0114] Figure 3 Each module / unit in the device shown has the function of implementing each step in the testing method for the recruitment recommendation system provided by the above method embodiment and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0115] The electronic device in the embodiment of the present application can be a user terminal device, a server, other computing devices, or a cloud server. Figure 4 This is a hardware structure diagram of an electronic device according to an embodiment of the present application. The electronic device may include a processor 401 and a memory 402 storing computer program instructions. When the processor 401 executes the computer program instructions, the process or function of any of the above-mentioned embodiments is implemented.

[0116] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application. The memory 402 may include a large-capacity memory for data or instructions. For example, the memory 402 may be at least one of the following: a hard disk drive (HDD), a read-only memory (ROM), a random access memory (RAM), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, a universal serial bus (USB) drive, or other physical / tangible memory storage device. For another example, the memory 402 may include a removable or non-removable (or fixed) medium. For another example, the memory 402 may be inside or outside the integrated gateway disaster recovery device. The memory 402 may be a non-volatile solid-state memory. In other words, the memory 402 typically includes a tangible (non-transitory) computer-readable storage medium (such as a memory device) encoded with computer-executable instructions, and when the software is executed (such as by one or more processors), the operations described in the method of the embodiments of the present application may be performed. The processor 401 implements the process or function of any of the methods in the above embodiments by reading and executing computer program instructions stored in the memory 402 .

[0117] In one example, Figure 4 The electronic device shown may also include a communication interface 403 and a bus 410. The processor 401, memory 402, and communication interface 403 are connected via bus 410 and communicate with each other. The communication interface 403 is primarily used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present application. The bus 410 includes hardware, software, or both, and can couple the components of the online data traffic metering device to each other. For example, the bus may include at least one of the following: an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Area Bus (VLB) bus, or other suitable buses. The bus 410 may include one or more buses. Although the embodiments of the present application describe or illustrate a specific bus, the embodiments of the present application may consider any suitable bus or interconnection method.

[0118] In combination with the method in the above embodiments, an embodiment of the present application also provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are executed by a processor, they implement the process or function of any method in the above embodiments.

[0119] In addition, an embodiment of the present application further provides a computer program product, which stores computer program instructions. When the computer program instructions are executed by a processor, the process or function of any one of the methods in the above embodiments is implemented.

[0120] The flowcharts and / or block diagrams of the methods, devices, systems and computer program products of the embodiments of the present application are described above by way of example, and various aspects thereof are described. It should be understood that each box in the flowchart and / or block diagram or a combination thereof may be implemented by computer program instructions, or may be implemented by dedicated hardware that performs a specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions. For example, these computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to form a machine that enables these instructions executed by such a processor to enable the implementation of the functions / actions specified in each box in the flowchart and / or block diagram or a combination thereof. Such a processor may be a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.

[0121] The functional blocks shown in the block diagram of the embodiment of the present application can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc.; when implemented in software, it is a program or code segment used to perform the required task. The program or code segment can be stored in a memory or transmitted on a transmission medium or a communication link via a data signal carried in a carrier. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0122] It should be noted that the present application is not limited to the specific configurations and processes described above or shown in the figures. The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described system, device, module or unit can refer to the corresponding process in the method embodiment without further description. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with the technical field can think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A testing method for a recruitment recommendation system, characterized in that: include: Obtain the user's target request parameters in the recruitment recommendation system. The target request parameters include the screening conditions entered by the user and the reference recommendation results that the user is interested in or not interested in. Calling the interface of the first version of the recruitment recommendation system based on the target request parameters to obtain multiple first recommendation results, and calling the interface of the second version of the recruitment recommendation system based on the target request parameters to obtain multiple second recommendation results; Calculating a first proportion of first recommendation results inconsistent with a reference recommendation result among the plurality of first recommendation results, and calculating a second proportion of second recommendation results inconsistent with the reference recommendation result among the plurality of second recommendation results; Comparing the plurality of first recommendation results with the plurality of second recommendation results, and determining whether data is missing from the plurality of second recommendation results compared to the plurality of first recommendation results based on the comparison result, and calculating a third proportion of the plurality of second recommendation results that are inconsistent with the first recommendation results; If the missing second recommendation result among multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold, then the size relationship between the first proportion and the second proportion is judged, wherein, if the target request parameter uses the reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then the second version of the recruitment recommendation system is determined to have passed the test; or, if the target request parameter uses the reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then the second version of the recruitment recommendation system is determined to have passed the test.

2. The method according to claim 1, characterized in that The method further comprises: If the missing second recommendation results in the plurality of second recommendation results are inconsistent with the expected missing recommendation results, and the number of missing second recommendation results is greater than the second preset threshold, it is determined that the second version of the recruitment recommendation system test has failed; If the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is greater than the first proportion, then the second version of the recruitment recommendation system test is determined to have failed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is less than the first proportion, then the second version of the recruitment recommendation system test is determined to have failed.

3. The method according to claim 1, characterized in that The reference recommendation result is a reference recommended position, the first recommendation result is a first recommended position, and the second recommendation result is a second recommended position; Alternatively, the reference recommendation result is a reference recommended resume, the first recommendation result is a first recommended resume, and the second recommendation result is a second recommended resume; Among them, when the reference recommendation result is a reference recommended resume that the user is interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, major, education level, work experience and / or personality and hobby description that the user is interested in; when the reference recommendation result is a reference recommended resume that the user is not interested in, the recommended resume that is inconsistent with the reference recommended resume is a recommended resume that is inconsistent with the region, age, gender, major, education level, work experience and / or personality and hobby description that the user is not interested in.

4. The method according to claim 1, wherein Before calling the interface of the first version of the recruitment recommendation system based on the target request parameter to obtain a plurality of first recommendation results, the method further includes: Disable the switch for updating recommended data in the target database. Recommended data includes job data or resume data. Among them, the first version of the recruitment recommendation system is used to query multiple first recommendation results that meet the target request parameter requirements from the recommendation data stored in the target database, and the second version of the recruitment recommendation system is used to query multiple second recommendation results that meet the target request parameter requirements from the recommendation data stored in the target database.

5. The method according to claim 4, characterized in that Before turning off the switch for updating the recommended data in the target database, the method further includes: Synchronize recommendation data from a source data table of at least one recruitment channel to a target data table of a target database, where each piece of recommendation data includes multiple fields arranged in columns; Setting a plurality of second target data columns in the target data table that need to be compared with the plurality of first target data columns in the source data table, and aligning the plurality of first target data columns with the plurality of second target data columns; Comparing the plurality of first target data columns with the plurality of second target data columns to obtain difference results, the difference results including at least one of first data unique to the plurality of first target data columns, second data unique to the plurality of second target data columns, and data in the second target data columns that differ from data in the first target data columns; Based on the difference results, the quality of this data synchronization is scored and the problems existing in this data synchronization are analyzed; An evaluation report is output, wherein the evaluation report displays the discrepant results, the quality score of this data synchronization, and the problems existing in this data synchronization.

6. The method according to claim 5, characterized in that Based on the difference results, the quality of this data synchronization is scored and the problems existing in this data synchronization are analyzed, including: Determine whether the first missing data in the target data table is the expected missing data. If not, deduct points based on the number or proportion of the missing first data in the target data table according to the preset deduction rules, and determine that the problem existing in this data synchronization at least includes missing data; If there are differences between the data in the second target data column and the corresponding first target data column, points are deducted according to the preset deduction rules based on the number or proportion of different data in the second target data column, and it is determined that the problem in this data synchronization includes at least a data synchronization rule error; Among them, the preset deduction rule is that starting from the preset total score, if the number or proportion of the first data missing in the target data table is x times the first preset deduction threshold, then x times the first preset score will be deducted; if the number or proportion of the data with differences in the second target data column is y times the second preset deduction threshold, then y times the second preset score will be deducted; the multiples are rounded up, and x and y are both positive integers.

7. The method according to claim 5, characterized in that The method further comprises: After finding the second target data column having data different from the first target data column, a test case corresponding to the second target data column having data different from the first target data column is executed to find the cause of the data difference.

8. A testing device for a recruitment recommendation system, characterized in that: include: The acquisition module is used to obtain the target request parameters of the user in the recruitment recommendation system. The target request parameters include the screening conditions entered by the user and the reference recommendation results that the user is interested in or not interested in; A calling module, configured to call an interface of the first version of the recruitment recommendation system based on the target request parameters to obtain a plurality of first recommendation results, and to call an interface of the second version of the recruitment recommendation system based on the target request parameters to obtain a plurality of second recommendation results; a first calculation module, configured to calculate a first proportion of first recommendation results inconsistent with a reference recommendation result among a plurality of first recommendation results, and calculate a second proportion of second recommendation results inconsistent with the reference recommendation result among a plurality of second recommendation results; a second calculation module, configured to compare the plurality of first recommendation results with the plurality of second recommendation results, determine whether data is missing from the plurality of second recommendation results compared to the plurality of first recommendation results based on the comparison result, and calculate a third proportion of second recommendation results that are inconsistent with the first recommendation results in the plurality of second recommendation results; A judgment module is used to judge the size relationship between the first proportion and the second proportion if the missing second recommendation result in multiple second recommendation results is consistent with the expected missing recommendation result, and the third proportion is less than the first preset threshold value, wherein, if the target request parameter uses a reference recommendation result that the user is interested in, and the second proportion is less than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed; or, if the target request parameter uses a reference recommendation result that the user is not interested in, and the second proportion is greater than or equal to the first proportion, then it is determined that the second version of the recruitment recommendation system test has passed.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, the test method for a recruitment recommendation system according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the testing method for a recruitment recommendation system according to any one of claims 1 to 7.

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