Test method, device and equipment for search engine ranking strategy of recruitment platform

Abstract

The application discloses a test method, device and equipment for a search engine ranking strategy of a recruitment platform, which comprises the following steps: obtaining a first search result and a second search result for a target use case, the target weight to-be-evaluated items of the first search result and the second search result being different, and other weight to-be-evaluated items being the same; running multiple use cases of the search engine to obtain the weights of multiple weight to-be-evaluated items in the first search result and the weights of multiple weight to-be-evaluated items in the second search result; calculating a first ranking total score of the first search result and a second ranking total score of the second search result according to the obtained weights; calculating the difference or quotient of the first ranking total score and the second ranking total score to obtain a test weight of the target weight to-be-evaluated item; comparing the test weight with an expected weight, if the test weight is consistent with the expected weight, it is determined that the target use case passes the test, and if the test weight is inconsistent with the expected weight, it is determined that the target use case fails the test. The application is beneficial to improving the test efficiency and coverage of the ranking strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data service, and particularly relates to a test method and device for a search engine sorting strategy of a recruitment platform, an electronic device, a computer readable storage medium and a computer program product. BACKGROUND

[0002] The search engine of the recruitment platform can be used by users to search for desired positions or resumes of job seekers, so as to help the job seekers find suitable positions and companies, or help the recruiters find suitable talents. In the search process, the multiple search results obtained by searching usually need to be sorted by relying on a sorting strategy. For example, the search results are sorted according to the matching degree of the search keywords and the recruitment information, or according to the publishing time of the recruitment information, or according to the completeness and quality of the recruitment information, and the like.

[0003] When a new sorting strategy or a sorting strategy is updated, the sorting strategy needs to be tested. However, the current sorting strategy test often relies on manual execution, which not only consumes time and effort, but also is inefficient. With the continuous expansion and update of the search engine function, manual testing has been difficult to meet the needs of rapid iteration and continuous optimization. Moreover, manual testing is easily affected by human factors such as fatigue and distraction, resulting in errors in the test results. Manual testing is also difficult to cover all possible test scenarios and boundary conditions, and thus some potential problems may be missed.

[0004] In addition, the search engine sorting strategy of the recruitment platform usually involves multiple factors and complex algorithms, such as keyword matching, user behavior analysis, weight calculation, and the like. The current test method is often difficult to comprehensively cover these factors and algorithms, resulting in insufficient comprehensiveness and accuracy of the test results. SUMMARY

[0005] Therefore, the embodiments of the present application provide a test method and device for a search engine sorting strategy of a recruitment platform, an electronic device, a computer readable storage medium and a computer program product, to solve at least one of the above technical problems.

[0006] The embodiment of the application provides a search engine sorting strategy testing method of a recruitment platform, the search engine is provided with a plurality of use cases, the plurality of use cases are used for scoring a plurality of weight to be scored items in a search result based on a plurality of preset weight score items and a scoring standard of each weight score item for any one search result, obtaining the weight of each weight to be scored item, the plurality of use cases correspond to the plurality of weight score items and the plurality of weight to be scored items one by one; the sorting strategy is that for any one search result, the total score of the search result is calculated according to the weight of the plurality of weight to be scored items in the search result, and the plurality of search results are sorted in descending order of the total score, and the search engine sorting strategy testing method of the recruitment platform comprises:

[0007] A first search result and a second search result used for testing a target use case are obtained, the target weight to be scored item in the first search result is different from the target weight to be scored item in the second search result, other weight to be scored items are the same, the target weight to be scored item is a weight to be scored item corresponding to the target use case; the target weight to be scored item in the first search result meets a preset recruitment or job seeking requirement, the target weight to be scored item in the second search result does not meet the recruitment or job seeking requirement, the scoring standard of the target weight score item is that the target weight to be scored item is given a weight N1 if the target weight to be scored item meets the recruitment or job seeking requirement, and is given a weight N2 if the target weight to be scored item does not meet the recruitment or job seeking requirement, N1>N2;

[0008] The plurality of use cases of the search engine are run, the weight of the plurality of weight to be scored items in the first search result and the weight of the plurality of weight to be scored items in the second search result are obtained; the first total score of the first search result is calculated according to the weight of the plurality of weight to be scored items in the first search result, and the second total score of the second search result is calculated according to the weight of the plurality of weight to be scored items in the second search result; the difference or quotient of the first total score and the second total score is calculated, and the difference or quotient is used as the test weight of the target weight to be scored item; the test weight of the target weight to be scored item is compared with the expected weight of the target weight to be scored item, if they are consistent, it is determined that the target use case test is passed; if they are inconsistent, it is determined that the target use case test is not passed.

[0009] According to some embodiments of the present application, optionally, the plurality of weight score items include first type weight score items and second type weight score items, the first type weight score items being weight score items determined based on historical behaviors of the user, and the second type weight score items being weight score items determined based on basic attributes of the search result; when the search result is a resume of a job seeker, the first type weight score items include a weight score item of a resume not interested by a recruiter, and the second type weight score items include at least one of a search term similarity weight score item, a resume active time decay weight score item, a weight score item of a latest job function in the resume hitting a job function of the recruiter, a weight score item of a latest job industry in the resume hitting a job industry of the recruiter, a salary weight score item, and an education weight score item; when the search result is a job position, the first type weight score items include at least one of a weight score item of a position already applied for by the job seeker, a weight score item of an area not interested by the job seeker, and a weight score item of a commuting distance not interested by the job seeker, and the second type weight score items include at least one of a search term similarity weight score item, a salary weight score item, a function weight score item, and an office location weight score item.

[0010] According to some embodiments of the present application, optionally, the first search result is a first resume of a job seeker, and the second search result is a second resume of a job seeker; the first total score of the first search result is calculated according to the weights of the plurality of weight score items in the first search result, and the second total score of the second search result is calculated according to the weights of the plurality of weight score items in the second search result, including: the weights of the plurality of weight score items in the first resume of the job seeker are calculated according to a first total score formula to obtain the first total score of the first resume of the job seeker; the weights of the plurality of weight score items in the second resume of the job seeker are calculated according to the first total score formula to obtain the second total score of the second resume of the job seeker; wherein the expression of the first total score formula is: F 总分1 = F1*F2*F3*F4*F5*F6*F7, wherein F 总分1 represents the total score of the resume, F1 represents a search term similarity weight, F2 represents a resume active time decay weight, F3 represents a weight of a latest job function in the resume hitting a job function of the recruiter, F4 represents a weight of a latest job industry in the resume hitting a job industry of the recruiter, F5 represents a salary weight, F6 represents an education weight, and F7 represents a weight of a resume not interested by a recruiter.

[0011] According to some embodiments of the present application, optionally, the first search result is a first job position, and the second search result is a second job position; the first total score of the first search result is calculated according to the weights of the plurality of weight-to-score items in the first search result, and the second total score of the second search result is calculated according to the weights of the plurality of weight-to-score items in the second search result, including: calculating the weights of the plurality of weight-to-score items in the first job position according to the second total score formula, to obtain the first total score of the first job position; calculating the weights of the plurality of weight-to-score items in the second job position according to the second total score formula, to obtain the second total score of the second job position; wherein the expression of the second total score formula is: F 总分2 =F1’*F2’*F3’*F4’*F5’*F6’*F7’,wherein F 总分2 represents the total score of the position ranking, F1’ represents the search term similarity weight, F2’ represents the weight of the positions to which the job seeker has delivered, F3’ represents the salary weight, F4’ represents the function weight, F5’ represents the office location weight, F6’ represents the weight of the areas in which the job seeker is not interested, and F7’ represents the weight of the commuting distances in which the job seeker is not interested.

[0012] According to some embodiments of the present application, optionally, the test method of the search engine ranking strategy of the recruitment platform further includes: after all the plurality of use cases are tested, if it is detected that the ranking strategy is updated and the update is to add a new weight-to-score item and a new use case corresponding thereto, the new use case is taken as a target use case, and only the new use case is tested, and the existing use cases are not tested.

[0013] According to some embodiments of the present application, optionally, the weights of the plurality of weight-to-score items in the first search result and the weights of the plurality of weight-to-score items in the second search result are obtained by running the plurality of use cases of the search engine, including: placing the plurality of use cases into a comma-separated values (CSV) file, each use case including at least a use case ID, a use case description, and an expected result; reading the plurality of use cases in the CSV file, and parameterizing the plurality of read use cases; placing the first search result, the second search result, and the historical behavior data of the user as pre-data in the CSV file, the historical behavior data of the user being used to set the first type of weight-to-score item, the first search result and the second search result corresponding to a target ID of the target use case; calling a search engine interface to run the plurality of use cases of the search engine, the plurality of use cases being specifically used to call the first search result and the second search result corresponding to the target ID in the CSV file, and score the plurality of weight-to-score items in the first search result and the second search result, to obtain the weights of the plurality of weight-to-score items in the first search result and the weights of the plurality of weight-to-score items in the second search result.

[0014] The embodiment of the application provides a search engine sorting strategy testing device of a recruitment platform, the search engine is provided with a plurality of use cases, the plurality of use cases are used for scoring a plurality of weight to be scored items in a search result based on a plurality of preset weight score items and a scoring standard of each weight score item, obtaining the weight of each weight to be scored item, the plurality of use cases correspond to the plurality of weight score items and the plurality of weight to be scored items one by one; the sorting strategy is that for any one search result, the weight of the plurality of weight to be scored items in the search result is used to calculate the total score of the search result, and the plurality of search results are sorted in descending order of the total score, and the search engine sorting strategy testing device of the recruitment platform comprises:

[0015] The acquisition module is used for acquiring a first search result and a second search result used for testing a target use case, the target weight to be scored item in the first search result and the second search result is different, other weight to be scored items except the target weight to be scored item are the same, the target weight to be scored item is a weight to be scored item corresponding to the target use case; the target weight to be scored item in the first search result meets a preset recruitment or job seeking requirement, the target weight to be scored item in the second search result does not meet the recruitment or job seeking requirement, the scoring standard of the target weight score item is that the target weight to be scored item is given a weight N1 when meeting the recruitment or job seeking requirement, and is given a weight N2 when not meeting the recruitment or job seeking requirement, N1>N2;

[0016] The running module is used for running the plurality of use cases of the search engine, obtaining the weight of the plurality of weight to be scored items in the first search result and the weight of the plurality of weight to be scored items in the second search result;

[0017] The first calculation module is used for calculating a first total score of the first search result according to the weight of the plurality of weight to be scored items in the first search result, and calculating a second total score of the second search result according to the weight of the plurality of weight to be scored items in the second search result;

[0018] The second calculation module is used for calculating the difference or quotient of the first total score and the second total score, and taking the difference or quotient as the test weight of the target weight to be scored item;

[0019] The judgment module is used for comparing the test weight of the target weight to be scored item with an expected weight of the target weight to be scored item, if the test weight is consistent with the expected weight, it is determined that the target use case passes the test; if the test weight is not consistent with the expected weight, it is determined that the target use case fails the test.

[0020] The embodiment of the application provides an electronic device, the electronic device comprises a processor and a memory storing computer program instructions; the processor implements the steps of the search engine sorting strategy testing method of the recruitment platform when executing the computer program instructions.

[0021] The embodiment of the present application provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions realize the steps of the test method of the search engine sorting strategy of the recruitment platform when executed by a processor.

[0022] The embodiment of the present application provides a computer program product, which includes computer program instructions, and the computer program instructions realize the steps of the test method of the search engine sorting strategy of the recruitment platform when executed by a processor.

[0023] The test method, the device, the electronic equipment, the computer readable storage medium and the computer program product of the search engine sorting strategy of the recruitment platform provided by the embodiment of the present application can be used for the target use case in the multiple use cases of the search engine sorting strategy, the target weight to be scored items in the selected first search result and the second search result are different, and the other weight to be scored items are the same; the target weight to be scored item in the first search result meets the preset recruitment or job seeking requirement, the target weight to be scored item in the second search result does not meet the recruitment or job seeking requirement, the scoring standard of the target weight to be scored item is that the target weight to be scored item meets the recruitment or job seeking requirement to give a weight N1, and does not meet the recruitment or job seeking requirement to give a weight N2, N1>N2; therefore, by running the target use case of the search engine, the weight of the target weight to be scored item in the first search result and the weight of the target weight to be scored item in the second search result should be different in theory, and then the first sorting total score of the first search result is calculated according to the weight of the multiple weight to be scored items in the first search result; the second sorting total score of the second search result is calculated according to the weight of the multiple weight to be scored items in the second search result; the first sorting total score and the second sorting total score should also be different in theory; then, the difference or quotient of the first sorting total score and the second sorting total score is calculated, and the difference or quotient is used as the test weight of the target weight to be scored item; finally, the test weight of the target weight to be scored item is compared with the expected weight of the target weight to be scored item, if consistent, it can be indicated that the target use case test is passed; if inconsistent, it can be indicated that the target use case test is not passed, so that the automation test of the use case in the sorting strategy can be realized, the test efficiency can be significantly improved, the influence of human factors can be reduced, and the accuracy and reliability of the test result can be improved.

[0024] In another aspect, even if the ranking strategy can involve multiple weighted items to be scored and a complex algorithm, by splitting each of the multiple weighted items to be scored and testing the use cases corresponding to each weighted item to be scored in turn, the omission during testing can be reduced and the test coverage can be improved. In yet another aspect, when the ranking strategy is updated, only the updated use cases can be tested and the existing use cases can not be tested, so that the modification of the test script and the test process can be greatly reduced, the test efficiency can be improved, the cost of developing the test script can be reduced, and the method is suitable for the scenario where the search engine ranking strategy of the recruitment platform is continuously updated and optimized. BRIEF DESCRIPTION OF DRAWINGS

[0025] 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.

[0026] Figure 1 A flowchart of a method for testing a search engine ranking strategy of a recruitment platform according to an embodiment of the present application.

[0027] Figure 2 A flowchart of S102 in a method for testing a search engine ranking strategy of a recruitment platform according to an embodiment of the present application.

[0028] Figure 3 A structural diagram of a testing device for a search engine ranking strategy of a recruitment platform according to an embodiment of the present application.

[0029] Figure 4 A schematic diagram of an electronic device for implementing a method for testing a search engine ranking strategy of a recruitment platform according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] The principles and spirits of the present application will be described below with reference to a number of exemplary embodiments. It should be understood that the purpose of providing these embodiments is to make the principles and spirits of the present application clearer and more thorough, so that those skilled in the art can better understand and implement the principles and spirits of the present application. The exemplary embodiments provided herein are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments herein, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0031] It should be noted that, in the present document, the terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Also, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0032] It should be understood that the term "and / or" used in the present document is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present document generally represents that the front and rear associated objects have an "or" relationship.

[0033] Various modifications and changes can be made to the present application without departing from the spirit or scope thereof, which will be apparent to one skilled in the art. Therefore, the present application is intended to cover the modifications and variations of the present application falling within the scope of the corresponding claims (claimed technical solutions) and their equivalents. It should be noted that the embodiments provided by the present application can be combined with each other without contradiction.

[0034] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate the understanding of the embodiments of the present application, the present application first specifically describes the problems existing in the related art:

[0035] The search engine of the recruitment platform can be used by users to search for desired positions or resumes of job seekers, so as to help job seekers find suitable positions and companies, or help recruiters find suitable talents. In the search process, it is usually necessary to rely on sorting strategies to sort a plurality of search results. For example, the search results are sorted according to the matching degree of the search keywords and the recruitment information, or according to the publishing time of the recruitment information, or according to the completeness and quality of the recruitment information, etc.

[0036] When a new ranking strategy or a ranking strategy is updated, the ranking strategy needs to be tested. However, current ranking strategy testing often relies on manual execution, which is not only time-consuming and labor-intensive, but also inefficient. With the continuous expansion and update of search engine functions, manual testing has been difficult to meet the needs of rapid iteration and continuous optimization. Moreover, manual testing is susceptible to human factors such as fatigue, distraction, etc., resulting in errors in test results. Manual testing also has difficulty covering all possible test scenarios and boundary conditions, which may miss some potential problems.

[0037] In addition, the search engine ranking strategy of a recruitment platform usually involves multiple factors and complex algorithms, such as keyword matching, user behavior analysis, weight calculation, etc. Current testing methods often have difficulty in fully covering these factors and algorithms, resulting in insufficient comprehensiveness and accuracy of test results.

[0038] In view of the above research findings of the inventors, the embodiments of the present application provide a method, device, and computer readable storage medium for testing the search engine ranking strategy of a recruitment platform, which can solve the above at least one technical problem existing in the related art.

[0039] First, the method for testing the search engine ranking strategy of a recruitment platform provided by the embodiments of the present application will be introduced.

[0040] A user can input a search term (or search keyword) in the search engine of a recruitment platform, and the search engine can search for multiple search results such as job seeker resumes or job positions based on the search term. The ranking strategy of the search engine can sort the multiple search results. Specifically, the search engine can be provided with multiple use cases, and each use case can be used to score multiple weight-to-be-scored items in a search result based on a plurality of preset weight scoring items and scoring standards for each weight scoring item, to obtain the weight of each weight-to-be-scored item. Each use case corresponds to one of the multiple weight scoring items and the multiple weight-to-be-scored items. For example, use case a corresponds to weight scoring item f1 and weight-to-be-scored item f1', use case b corresponds to weight scoring item f2 and weight-to-be-scored item f2', and so on. Use case a can be used to score weight-to-be-scored item f1' in a search result based on preset weight scoring item f1 and the scoring standard of weight scoring item f1, to obtain the weight of weight-to-be-scored item f1'. Use case b can be used to score weight-to-be-scored item f2' in a search result based on preset weight scoring item f2 and the scoring standard of weight scoring item f2, to obtain the weight of weight-to-be-scored item f2'.

[0041] The sorting strategy can calculate a total score of each search result according to the weights of the plurality of weight-to-be-scored items in the search result, and sort the plurality of search results in descending order of the total scores.

[0042] Figure 1 A flowchart of a method for testing a search engine sorting strategy of a recruitment platform is provided in an embodiment of the present application. As shown in Figure 1 the method for testing a search engine sorting strategy of a recruitment platform provided in an embodiment of the present application can include the following steps S101-S105.

[0043] S101: Obtain a first search result and a second search result for a target use case.

[0044] The target use case can be any one of a plurality of use cases, the weight-scored item corresponding to the target use case is referred to as a target weight-scored item, and the weight-to-be-scored item corresponding to the target use case is referred to as a target weight-to-be-scored item. The target use case is used to score the target weight-to-be-scored item in the search result based on the target weight-scored item and the scoring standard of the target weight-scored item, to obtain the weight of the target weight-to-be-scored item.

[0045] The target weight-to-be-scored items in the first search result and the second search result are different, and the other weight-to-be-scored items except the target weight-to-be-scored item are the same. Taking the first search result as a first job seeker resume and the second search result as a second job seeker resume as an example, assuming that the target weight-scored item is education background, the target weight-to-be-scored item in the first search result is, for example, "education background is undergraduate", and the target weight-to-be-scored item in the second search result is, for example, "education background is high school". The other weight-to-be-scored items except the target weight-to-be-scored item in the first search result and the second search result are the same. For example, still taking the first search result as a first job seeker resume and the second search result as a second job seeker resume as an example, the other weight-to-be-scored items except the target weight-to-be-scored item in the first search result and the second search result are the same, such as the resume active time, the work function, the work industry, and the salary requirement. For example, the resume active time is active within the last 3 days, the work function is R&D, the work industry is Internet, and the salary requirement is more than 10,000 yuan per month.

[0046] The target weight-to-be-scored item in the first search result meets the preset recruitment or job-seeking requirement, and the target weight-to-be-scored item in the second search result does not meet the recruitment or job-seeking requirement. The scoring standard of the target weight-scored item is that the target weight-to-be-scored item meets the recruitment or job-seeking requirement to give a weight N1, and does not meet the recruitment or job-seeking requirement to give a weight N2, N1>N2. The sizes of N1 and N2 can be flexibly adjusted according to actual conditions, which are not limited in the present application. For example, in some examples, N1 can be equal to 10, and N2 can be equal to 1.

[0047] For example, taking the first search result as a first job seeker resume and the second search result as a second job seeker resume, assume that the target weight score item is education, and the scoring standard of the target weight score item is that the education meets the recruitment requirement to give a weight N1, and the education does not meet the recruitment requirement to give a weight N2. The recruitment requirement can be flexibly adjusted according to actual conditions, and the present application does not limit this. For example, in some examples, for the education, the recruitment requirement can be that the education is a bachelor degree or above.

[0048] Assume that the target weight score item in the first job seeker resume is “the education is a bachelor degree”, and the target weight score item in the second job seeker resume is “the education is a high school degree”, it can be seen that the target weight score item in the first job seeker resume meets the recruitment requirement, so theoretically, the weight of the target weight score item in the first job seeker resume should be N1 in the case that the target use case is normal. Since the target weight score item in the second job seeker resume does not meet the recruitment requirement, theoretically, the weight of the target weight score item in the second job seeker resume should be N2 in the case that the target use case is normal.

[0049] In other embodiments, optionally, the first search result can also be a first job position, and the second search result can also be a second job position. Taking the first search result as a first job position and the second search result as a second job position, assume that the target weight score item is salary, and the scoring standard of the target weight score item is that the salary meets the job requirement to give a weight N1, and the salary does not meet the job requirement to give a weight N2. The job requirement can be flexibly adjusted according to actual conditions, and the present application does not limit this. For example, in some examples, for the salary, the job requirement can be that the salary is more than 10,000 yuan per month.

[0050] Assume that the target weight score item in the first job position is “more than 10,000 yuan per month”, and the target weight score item in the second job position is “8,000 yuan per month”, it can be seen that the target weight score item in the first job position meets the job requirement, so theoretically, the weight of the target weight score item in the first job position should be N1 in the case that the target use case is normal. Since the target weight score item in the second job position does not meet the recruitment requirement, theoretically, the weight of the target weight score item in the second job position should be N2 in the case that the target use case is normal.

[0051] S102: Run multiple use cases of the search engine to obtain the weights of multiple weight score items in the first search result and the weights of multiple weight score items in the second search result.

[0052] In S102, a plurality of use cases of the search engine are run. The plurality of use cases can be used to score the plurality of weight-to-be-scored items in the first search result based on the plurality of preset weight-scored items and the scoring criteria of each weight-scored item, to obtain the weight of each weight-to-be-scored item in the first search result. The plurality of use cases can also be used to score the plurality of weight-to-be-scored items in the second search result based on the plurality of preset weight-scored items and the scoring criteria of each weight-scored item, to obtain the weight of each weight-to-be-scored item in the second search result.

[0053] The plurality of use cases correspond to the plurality of weight-scored items and the plurality of weight-to-be-scored items one by one. For example, use case a corresponds to weight-scored item f1 and weight-to-be-scored item f1', and so on. Use case a can be used to score weight-to-be-scored item f1' in the first search result based on preset weight-scored item f1 and the scoring criteria of weight-scored item f1, to obtain the weight of weight-to-be-scored item f1' in the first search result. Use case a can also be used to score weight-to-be-scored item f1' in the second search result based on preset weight-scored item f1 and the scoring criteria of weight-scored item f1, to obtain the weight of weight-to-be-scored item f1' in the second search result.

[0054] For the target use case, the target use case can be used to score the target weight-to-be-scored item in the first search result based on the target weight-scored item and the scoring criteria of the target weight-scored item, to obtain the weight of the target weight-to-be-scored item in the first search result, and to score the target weight-to-be-scored item in the second search result, to obtain the weight of the target weight-to-be-scored item in the second search result.

[0055] Since the target weight-to-be-scored items in the first search result and the second search result are different, theoretically, the weight of the target weight-to-be-scored item in the first search result and the weight of the target weight-to-be-scored item in the second search result should be different.

[0056] Since the first search result and the second search result are the same in the weight-to-be-scored items other than the target weight-to-be-scored item, the weight of the other weight-to-be-scored items in the first search result obtained by the other use cases and the weight of the other weight-to-be-scored items in the second search result should be the same.

[0057] S103: According to the weights of the plurality of weight-to-be-scored items in the first search result, a first total score of the first search result is calculated, and according to the weights of the plurality of weight-to-be-scored items in the second search result, a second total score of the second search result is calculated.

[0058] In S103, a first sorting total score of the first search result can be calculated according to the weights of the multiple weight-to-be-scored items in the first search result and a preset algorithm, and a second sorting total score of the second search result can be calculated according to the weights of the multiple weight-to-be-scored items in the second search result and the preset algorithm.

[0059] The preset algorithm used in the first search result and the second search result is the same. The preset algorithm can be flexibly adjusted according to actual conditions, which is not limited in the present application. For example, the preset algorithm can be addition or multiplication.

[0060] For example, in some examples, the weights of the multiple weight-to-be-scored items in the first search result can be added to obtain the first sorting total score of the first search result, and the weights of the multiple weight-to-be-scored items in the second search result can be added to obtain the second sorting total score of the second search result.

[0061] For example, in some other examples, the weights of the multiple weight-to-be-scored items in the first search result can be multiplied to obtain the first sorting total score of the first search result, and the weights of the multiple weight-to-be-scored items in the second search result can be multiplied to obtain the second sorting total score of the second search result.

[0062] In S104, a difference or quotient of the first sorting total score and the second sorting total score is calculated, and the difference or quotient is taken as a test weight of the target weight-to-be-scored item.

[0063] In the case where the preset algorithm is addition, after the first sorting total score and the second sorting total score are obtained, a difference of the first sorting total score and the second sorting total score can be calculated, and the difference is taken as the test weight of the target weight-to-be-scored item. Alternatively, in the case where the preset algorithm is multiplication, after the first sorting total score and the second sorting total score are obtained, a quotient of the first sorting total score and the second sorting total score can be calculated, and the quotient is taken as the test weight of the target weight-to-be-scored item.

[0064] In S105, the test weight of the target weight-to-be-scored item is compared with an expected weight of the target weight-to-be-scored item. If the test weight is consistent with the expected weight, it is determined that the target use case test is passed. If the test weight is not consistent with the expected weight, it is determined that the target use case test is not passed.

[0065] Taking the test weight of the target weight-to-be-scored item as the quotient of the first sorting total score and the second sorting total score as an example, the expected weight of the target weight-to-be-scored item can be N1 / N2, N1 and N2 are known, so the expected weight N1 / N2 of the target weight-to-be-scored item is known. As described above, in the case where the target use case is normal, the weight of the target weight-to-be-scored item in the first search result should be N1, and the weight of the target weight-to-be-scored item in the second search result should be N2. Therefore, in the case where the target use case is normal, the test weight of the target weight-to-be-scored item should also be N1 / N2.

[0066] Taking the difference between the first ranking total score and the second ranking total score as an example of the test weight of the target-weight to-be-scored item, the expected weight of the target-weight to-be-scored item can be N1-N2, N1 and N2 are known, so the expected weight N1-N2 of the target-weight to-be-scored item is known. As described above, in the case where the target use case is normal, the weight of the target-weight to-be-scored item in the first search result should be N1, and the weight of the target-weight to-be-scored item in the second search result should be N2. Therefore, in the case where the target use case is normal, the test weight of the target-weight to-be-scored item should also be N1-N2.

[0067] If the test weight of the target-weight to-be-scored item is consistent with the expected weight of the target-weight to-be-scored item, it can be indicated that the target use case is normal, and it is determined that the target use case test is passed. If the test weight of the target-weight to-be-scored item is inconsistent with the expected weight of the target-weight to-be-scored item, it can be indicated that the target use case is abnormal, and it is determined that the target use case test is failed.

[0068] For multiple use cases of the search engine ranking strategy, after the current target use case test is completed, an untested use case is taken as a new target use case, step S101 is returned, and steps S101 to S105 are repeated until the multiple use cases are tested.

[0069] It should be noted that different use cases correspond to different weight scoring items and different weight to-be-scored items. Accordingly, when updating the target use case, the first search result and the second search result corresponding to the current target use case need to be used.

[0070] The search engine ranking strategy testing method provided by the embodiments of the present application can be used to test the search engine ranking strategy of the recruitment platform. In one aspect, for a target use case in multiple use cases of the search engine ranking strategy, the target weight to-be-scored item in the first search result and the second search result is different, and the other weight to-be-scored items are the same. The target weight to-be-scored item in the first search result meets the preset recruitment or job-seeking requirement, and the target weight to-be-scored item in the second search result does not meet the recruitment or job-seeking requirement. The scoring standard of the target weight to-be-scored item is that the target weight to-be-scored item is given a weight N1 if it meets the recruitment or job-seeking requirement, and is given a weight N2 if it does not meet the recruitment or job-seeking requirement, and N1>N2. Therefore, by running the target use case of the search engine, the weight of the target weight to-be-scored item in the first search result and the weight of the target weight to-be-scored item in the second search result should theoretically be different. Then, the first ranking total score of the first search result is calculated according to the weights of the multiple weight to-be-scored items in the first search result, the second ranking total score of the second search result is calculated according to the weights of the multiple weight to-be-scored items in the second search result, and the first ranking total score and the second ranking total score should theoretically be different. Then, the difference or quotient of the first ranking total score and the second ranking total score is calculated, and the difference or quotient is taken as the test weight of the target weight to-be-scored item. Finally, the test weight of the target weight to-be-scored item is compared with the expected weight of the target weight to-be-scored item. If they are consistent, it means that the target use case passes the test. If they are inconsistent, it means that the target use case fails the test. Thus, the automatic testing of the use cases in the ranking strategy can be realized, the test efficiency can be significantly improved, the influence of human factors can be reduced, and the accuracy and reliability of the test results can be improved.

[0071] On the other hand, even if the ranking strategy may involve multiple weight to-be-scored items and complex algorithms, by splitting each of the multiple weight to-be-scored items and testing the use cases corresponding to each weight to-be-scored item in turn, the omission during testing can be reduced, and the test coverage can be improved. In yet another aspect, when the ranking strategy is updated, only the updated use cases can be tested, and the existing use cases are not tested. Thus, the changes to the test script and test process can be greatly reduced, the test efficiency can be improved, the cost of developing the test script can be reduced, and the method is suitable for the scenario where the search engine ranking strategy of the recruitment platform is continuously updated and optimized.

[0072] According to some embodiments of the present application, optionally, the plurality of weight score items can include a first type of weight score item and a second type of weight score item. The first type of weight score item can be a weight score item determined based on historical behavior of the user, and the second type of weight score item can be a weight score item determined based on basic attributes of the search result. For example, the historical behavior of the user can include determining that the user is not interested in a resume, the user is not interested in a region, the user is not interested in a function, the user is not interested in an industry, the user is not interested in a salary, and / or the user is not interested in a commuting distance, etc., according to historical operations of the user on the recruitment platform. The user is not interested in a resume can be that a recruiter is not interested in a job seeker resume for one or more majors, one or more educational backgrounds, one or more salaries, one or more addresses, one or more work experiences, one or more job functions, one or more industries, and / or one or more personality preferences, etc.

[0073] The basic attributes of the search result can be used to help the user quickly understand the content of the search result.

[0074] In some embodiments, when the search result is a job seeker resume, the first type of weight score item can include a recruiter is not interested in a resume weight score item, and the second type of weight score item can include at least one of a search term similarity weight score item, a resume active time decay weight score item, a recent job function in a resume hits a recruitment function weight score item, a recent job industry in a resume hits a recruitment industry weight score item, a salary weight score item, and an educational background weight score item.

[0075] Specifically, when a recruiter searches for a job seeker resume through a search engine, the first type of weight score item can include a recruiter is not interested in a resume weight score item, and the scoring standard of the recruiter is not interested in a resume weight score item can be that if the job seeker resume is a recruiter is not interested in a resume, i.e., does not meet the recruitment requirements, a lower weight is given; if the job seeker resume is not a recruiter is not interested in a resume, a higher weight is given. The weight size can be flexibly adjusted according to actual conditions, which is not limited in the present application.

[0076] When the search result is a job seeker resume, the second type of weight score item can include at least one of a search term similarity weight score item, a resume active time decay weight score item, a recent job function in a resume hits a recruitment function weight score item, a recent job industry in a resume hits a recruitment industry weight score item, a salary weight score item, and an educational background weight score item.

[0077] The search term similarity weight score item is a weight score item of the similarity between the content in the job seeker resume and the search term. The scoring standard of the search term similarity weight score item can be that the higher the similarity, the higher the weight given; otherwise, the lower the similarity, the lower the weight given.

[0078] Resume active time decay weight score item refers to a weight score item of a decay degree of a resume active time (such as an update time, a view time, or a delivery time). The scoring standard of the resume active time decay weight score item can be that the closer the resume active time is to the current time, the higher the weight is given; the farther the resume active time is from the current time, the lower the weight is given.

[0079] Recent job function in resume hits recruitment function weight score item refers to a weight score item of whether a recent job function in a resume hits a current recruitment function. For example, the recruitment function is R&D, and the recent job function in the resume is also R&D, it is determined that the recent job function in the resume hits the recruitment function, and a higher weight is given; otherwise, a lower weight is given.

[0080] Recent job industry in resume hits recruitment industry weight score item refers to a weight score item of whether a recent job industry in a resume hits a current recruitment industry. For example, the recruitment industry is the Internet, and the recent job industry in the resume is also the Internet, it is determined that the recent job industry in the resume hits the recruitment industry, and a higher weight is given; otherwise, a lower weight is given.

[0081] Salary weight score item refers to a weight score item of whether the expected salary in the resume of the job seeker is within the salary range required by the recruitment. The scoring standard of the salary weight score item can be that if the expected salary in the resume of the job seeker is within the salary range required by the recruitment, that is, the expected salary meets the recruitment requirements, a higher weight is given; otherwise, a lower weight is given.

[0082] Education weight score item refers to a weight score item of whether the education in the resume of the job seeker meets the required education of the recruitment. The scoring standard of the education weight score item can be that if the education in the resume of the job seeker meets the required education of the recruitment, a higher weight is given; otherwise, a lower weight is given.

[0083] Correspondingly, the plurality of weight to-be-scored items of the resume of the job seeker can include the recruiter-uninterested-resume weight to-be-scored item, and at least one of the search term similarity weight to-be-scored item, the resume active time decay weight to-be-scored item, the recent job function in resume hits recruitment function weight to-be-scored item, the recent job industry in resume hits recruitment industry weight to-be-scored item, the salary weight to-be-scored item, and the education weight to-be-scored item.

[0084] In this way, the weight score items are determined based on the historical behavior of the user and the basic attributes of the search result, which is conducive to making the final ranking result meet the habits and personalized needs of the user and improving the user experience.

[0085] When the search result is a resume of a job seeker, the first search result can be a first resume of a job seeker, and the second search result can be a second resume of a job seeker. The first resume of the job seeker is different from the second resume of the job seeker in a target weight to-be-scored item, and is the same as the second resume of the job seeker in other weight to-be-scored items except the target weight to-be-scored item.

[0086] Correspondingly, S103: calculating a first ranking total score of the first search result according to the weights of the multiple weight to-be-scored items in the first search result, and calculating a second ranking total score of the second search result according to the weights of the multiple weight to-be-scored items in the second search result, can include the following step one and step two.

[0087] Step one: calculating the weights of the multiple weight to-be-scored items in the first resume of the job seeker according to the first total score formula to obtain the first ranking total score of the first resume of the job seeker.

[0088] Step two: calculating the weights of the multiple weight to-be-scored items in the second resume of the job seeker according to the first total score formula to obtain the second ranking total score of the second resume of the job seeker.

[0089] For example, the expression of the first total score formula can be:

[0090] F 总分1 =F1*F2* F3* F4* F5* F6* F7 (1)

[0091] Wherein, F 总分1 represents a resume ranking total score, F1 represents a search term similarity weight, i.e., the weight of the search term similarity weight to-be-scored item, F2 represents a resume active time decay weight, i.e., the weight of the resume active time decay weight to-be-scored item, F3 represents a resume recent job function hits recruitment function weight, i.e., the weight of the resume recent job function hits recruitment function weight to-be-scored item, F4 represents a resume recent job industry hits recruitment industry weight, i.e., the weight of the resume recent job industry hits recruitment industry weight to-be-scored item, F5 represents a salary weight, i.e., the weight of the salary weight to-be-scored item, F6 represents an education weight, i.e., the weight of the education weight to-be-scored item, and F7 represents a recruiter uninterested resume weight, i.e., the weight of the recruiter uninterested resume weight to-be-scored item.

[0092] Based on the above first total score formula, the resume ranking total score of the first resume of the job seeker, i.e., the first ranking total score, can be obtained. At the same time, based on the above first total score formula, the resume ranking total score of the second resume of the job seeker, i.e., the second ranking total score, can be obtained.

[0093] In some embodiments, when the search result is a job opening, the first type of weight score item can include at least one of a job opening the candidate has applied for weight score item, a region the candidate is not interested in weight score item, and a commuting distance the candidate is not interested in weight score item. The second type of weight score item can include at least one of a search term similarity weight score item, a salary weight score item, a function weight score item, and an office location weight score item.

[0094] In particular, when the candidate searches for a job opening through the search engine, the first type of weight score item can include at least one of a job opening the candidate has applied for weight score item, a region the candidate is not interested in weight score item, and a commuting distance the candidate is not interested in weight score item. The job opening the candidate has applied for, the region the candidate is not interested in, and the commuting distance the candidate is not interested in can be determined according to the historical data of the candidate on the recruitment platform. The second type of weight score item can include at least one of a search term similarity weight score item, a salary weight score item, a function weight score item, and an office location weight score item.

[0095] The search term similarity weight score item is a weight score item of the similarity between the content in the job opening and the search term. The scoring standard of the search term similarity weight score item can be that the higher the similarity between the two, the higher the weight given; otherwise, the lower the similarity between the two, the lower the weight given.

[0096] The salary weight score item is a weight score item of whether the salary given by the job opening is within the expected salary range of the candidate. The scoring standard of the salary weight score item can be that if the salary given by the job opening is within the expected salary range of the candidate, i.e., the salary meets the job requirements, a higher weight is given; otherwise, a lower weight is given.

[0097] The function weight score item is a weight score item of whether the function of the job opening is the expected function of the candidate. The scoring standard of the function weight score item can be that if the function of the job opening is the expected function of the candidate, i.e., the function meets the job requirements, a higher weight is given; otherwise, a lower weight is given.

[0098] The office location weight score item is a weight score item of whether the office location of the job opening is the expected office location of the candidate. The scoring standard of the office location weight score item can be that if the office location of the job opening is the expected office location of the candidate, i.e., the office location meets the job requirements, a higher weight is given; otherwise, a lower weight is given.

[0099] Correspondingly, the multiple weight-to-be-scored items of the job position to be applied for can include at least one of the job position weight-to-be-scored item that the job seeker has delivered, the region weight-to-be-scored item that the job seeker is not interested in, and the commuting distance weight-to-be-scored item that the job seeker is not interested in, and at least one of the search term similarity weight-to-be-scored item, the salary weight-to-be-scored item, the function weight-to-be-scored item, and the office location weight-to-be-scored item.

[0100] When the search result is a job position, the first search result can be a first job position, and the second search result can be a second job position. The target weight-to-be-scored item in the first job position is different from that in the second job position, and other weight-to-be-scored items are the same.

[0101] Correspondingly, S103: calculating a first sorting total score of the first search result according to the weight of the multiple weight-to-be-scored items in the first search result, and calculating a second sorting total score of the second search result according to the weight of the multiple weight-to-be-scored items in the second search result, can include the following steps three and four.

[0102] Step three: calculating the weight of the multiple weight-to-be-scored items in the first job position according to the second total score formula to obtain the first sorting total score of the first job position.

[0103] Step four: calculating the weight of the multiple weight-to-be-scored items in the second job position according to the second total score formula to obtain the second sorting total score of the second job position.

[0104] Wherein, the expression of the second total score formula can be:

[0105] F 总分2 =F1’*F2’* F3’* F4’* F5’* F6’* F7’ (2)

[0106] Wherein, F 总分2 represents the position sorting total score, F1’ represents the search term similarity weight, i.e., the weight of the search term similarity weight-to-be-scored item, F2’ represents the job position weight that the job seeker has delivered, F3’ represents the salary weight, F4’ represents the function weight, F5’ represents the office location weight, F6’ represents the region weight that the job seeker is not interested in, and F7’ represents the commuting distance weight that the job seeker is not interested in.

[0107] Based on the above-mentioned second total score formula, the position sorting total score of the first job position, i.e., the first sorting total score, can be obtained. At the same time, based on the above-mentioned second total score formula, the position sorting total score of the second job position, i.e., the second sorting total score, can be obtained.

[0108] According to some embodiments of the present application, optionally, the test method of the search engine sorting strategy of the recruitment platform can further include the following steps:

[0109] After all the use cases are tested, if it is detected that the ranking strategy is updated and the new weight score item and its corresponding new use case are added, the new use case is taken as the target use case to test only the new use case pair and not to test the existing use case.

[0110] Specifically, as search engine technology continues to develop, the ranking strategy is also constantly updated and optimized. However, the current testing method is often difficult to adapt to these changes, and the testing script and testing process need to be frequently adjusted, increasing the complexity and difficulty of the testing work.

[0111] In the embodiments of the present application, when it is detected that the ranking strategy is updated, for the added new weight score item and its corresponding new use case, the new use case is taken as the target use case, and steps S101 to S105 are executed to test only the new use case pair and not to test the existing use case.

[0112] In this way, when the ranking strategy is updated, by testing the updated use case and not testing the existing use case, the changes to the testing script and testing process can be greatly reduced, the testing efficiency is improved, the cost of developing the testing script is reduced, and the scenario of constantly updating and optimizing the search engine ranking strategy of the recruitment platform is applicable.

[0113] Figure 2 A flowchart of S102 in the method for testing the search engine ranking strategy of the recruitment platform provided in the embodiments of the present application is shown. Figure 2 As shown, according to some embodiments of the present application, S102: running a plurality of use cases of a search engine to obtain the weights of a plurality of weight to be scored items in a first search result and the weights of a plurality of weight to be scored items in a second search result, can include steps S201 to S204.

[0114] S201: placing a plurality of use cases in a comma-separated value (CSV) file, each use case including at least a use case ID, a use case description, and an expected result.

[0115] Specifically, taking the search result as a job seeker's resume as an example, the interface for calling the search engine can be first encapsulated, and the interface return result actual_result is uniformly formatted, for example, {“resumelist”: [], “scorelist”: []}.

[0116] Wherein, resumelist is the resume id in the interface return result, which is placed in the resume list in order; scorelist is the total score of the resume id in the interface return result, which is placed in the resume score list in order.

[0117] Then, an automated testing architecture can be built according to a Python testing framework such as pytest. Specifically, a plurality of use cases can be placed in a Comma-Separated Values (CSV) file in the following structure: use case ID, use case description, interface parameter, and expected result.

[0118] pytest is a Python testing framework for writing and running unit tests, integration tests, and functional tests. It provides a rich set of features, including automatic test case discovery, parameterized tests, assertion support, parallel testing, and a rich plugin system.

[0119] S202: Read a plurality of use cases in the CSV file and parameterize the plurality of use cases read.

[0120] Specifically, a method get_cases() for reading the CSV file can be created to read a plurality of use cases in the CSV file. Then, the plurality of use cases read can be parameterized according to a pytest fixture method such as parametrize, and the code of the parameterized use cases is as follows:

[0121] caseid,caseinfo,request,expect_result

[0122] wherein the expect_result format is:

[0123] {“resumelist”:[],“weight”:}。

[0124] S203: Place the first search result, the second search result, and the user's historical behavior data as pre-data in the CSV file, and use the user's historical behavior data to set the first type of weight score item, and the first search result and the second search result correspond to the target ID of the target use case.

[0125] Specifically, the first search result, the second search result, and the user's historical behavior data can be placed as pre-data in the CSV file in the following structure: use case ID, data description, data type, and specific pre-data content.

[0126] It should be noted that it is necessary to ensure that the pre-data placed corresponds to the use case ID, and that only one unique variable is different between a plurality of data, and the weight of the unique variable in the sorting strategy is consistent with the use case expected output weight rank_weight.

[0127] When inserting data, a corresponding data insertion method can be called according to the data type and the corresponding use case ID to complete the processing of the pre-data. The data types include, but are not limited to, ES, opensearch, redis, lindorm-hbase, and tikv.

[0128] S204: The search engine interface is called to run multiple use cases of the search engine, and the multiple use cases are specifically used to call the first search result and the second search result corresponding to the target ID in the CSV file, and score multiple weight to-be-scored items in the first search result and the second search result to obtain the weights of the multiple weight to-be-scored items in the first search result and the weights of the multiple weight to-be-scored items in the second search result.

[0129] Specifically, the search engine interface can be called according to the request request parameterized by the use case, so as to run multiple use cases of the search engine. The multiple use cases can be used to call the first search result and the second search result corresponding to the target ID in the CSV file, and score multiple weight to-be-scored items in the first search result and the second search result to obtain the weights of the multiple weight to-be-scored items in the first search result and the weights of the multiple weight to-be-scored items in the second search result.

[0130] Then, S103 to S105 are executed, the first sorting total score of the first search result is calculated according to the weights of the multiple weight to-be-scored items in the first search result, and the second sorting total score of the second search result is calculated according to the weights of the multiple weight to-be-scored items in the second search result. The difference or quotient of the first sorting total score and the second sorting total score is calculated, and the difference or quotient is taken as the test weight of the target weight to-be-scored item. The test weight of the target weight to-be-scored item is compared with the expected weight of the target weight to-be-scored item, if they are consistent, it is determined that the target use case test is passed; if they are inconsistent, it is determined that the target use case test is not passed.

[0131] After the target use case test is completed, the pre-data corresponding to the target use case can be inserted and deleted to avoid affecting the next test.

[0132] According to some embodiments of the present application, optionally, the test method of the search engine sorting strategy of the recruitment platform can further include the following steps:

[0133] After the multiple use cases are all tested, a test report is output.

[0134] The test report can show each use case ID, the use case description of each use case, and the test result of each use case, so as to facilitate the developer to view the test result of each use case.

[0135] Based on the same technical concept as the test method of the search engine ranking strategy of the recruitment platform provided in the above embodiment, accordingly, the application also provides a test device of a search engine ranking strategy of a recruitment platform. The search engine is provided with a plurality of use cases, and the plurality of use cases are used to score a plurality of weight to be scored items in the search results based on a plurality of preset weight score items and a scoring standard of each weight score item, so as to obtain the weight of each weight to be scored item. The plurality of use cases correspond to the plurality of weight score items and the plurality of weight to be scored items one by one. The ranking strategy is to calculate the total score of the search results according to the weight of the plurality of weight to be scored items in the search results for any one search result, and to sort the plurality of search results in descending order of the total score. Please refer to the following embodiments.

[0136] Figure 3 A structural schematic diagram of the test device of the search engine ranking strategy of the recruitment platform provided in the embodiment of the application. As shown in Figure 3 The test device 30 of the search engine ranking strategy of the recruitment platform provided in the embodiment of the application can include the following modules:

[0137] The acquisition module 301 is used to acquire a first search result and a second search result for a target use case. The target weight to be scored item in the first search result is different from that in the second search result, and the weight to be scored items other than the target weight to be scored item are the same. The target weight to be scored item is the weight to be scored item corresponding to the target use case. The target weight to be scored item in the first search result meets the preset recruitment or job-seeking requirement, and the target weight to be scored item in the second search result does not meet the recruitment or job-seeking requirement. The scoring standard of the target weight to be scored item is that the target weight to be scored item meets the recruitment or job-seeking requirement to give a weight N1, and does not meet the recruitment or job-seeking requirement to give a weight N2, N1>N2;

[0138] The running module 302 is used to run a plurality of use cases of the search engine, so as to obtain the weight of the plurality of weight to be scored items in the first search result and the weight of the plurality of weight to be scored items in the second search result;

[0139] The first calculation module 303 is used to calculate the first total score of the first search result according to the weight of the plurality of weight to be scored items in the first search result, and to calculate the second total score of the second search result according to the weight of the plurality of weight to be scored items in the second search result;

[0140] The second calculation module 304 is used to calculate the difference or quotient of the first total score and the second total score, and to take the difference or quotient as the test weight of the target weight to be scored item;

[0141] The judgment module 305 is configured to compare the test weight of the target weight to-be-scored item with the expected weight of the target weight to-be-scored item, and if the two are consistent, it is determined that the target case test is passed; if the two are inconsistent, it is determined that the target case test is failed.

[0142] The search engine ranking strategy testing device provided by the embodiment of the application is used for testing the target case in the plurality of cases of the search engine ranking strategy. The target weight to-be-scored item in the first search result is different from the target weight to-be-scored item in the second search result, and the other weight to-be-scored items are the same. The target weight to-be-scored item in the first search result meets the preset recruitment or job-seeking requirement, the target weight to-be-scored item in the second search result does not meet the recruitment or job-seeking requirement, the scoring standard of the target weight to-be-scored item is that the target weight to-be-scored item is given a weight N1 if it meets the recruitment or job-seeking requirement, and is given a weight N2 if it does not meet the recruitment or job-seeking requirement, and N1>N2. Therefore, the weight of the target weight to-be-scored item in the first search result obtained by running the target case of the search engine is theoretically different from the weight of the target weight to-be-scored item in the second search result. Then, the first ranking total score of the first search result is calculated according to the weights of the plurality of weight to-be-scored items in the first search result, the second ranking total score of the second search result is calculated according to the weights of the plurality of weight to-be-scored items in the second search result, and the first ranking total score and the second ranking total score are theoretically different. Then, the difference or quotient of the first ranking total score and the second ranking total score is calculated, and the difference or quotient is taken as the test weight of the target weight to-be-scored item. Finally, the test weight of the target weight to-be-scored item is compared with the expected weight of the target weight to-be-scored item. If the two are consistent, it is indicated that the target case test is passed. If the two are inconsistent, it is indicated that the target case test is failed. Therefore, the automatic test of the case in the ranking strategy can be realized, the test efficiency can be significantly improved, the influence of human factors can be reduced, and the accuracy and reliability of the test result can be improved.

[0143] On the other hand, even if the ranking strategy may involve a plurality of weight to-be-scored items and a complex algorithm, by splitting each of the plurality of weight to-be-scored items and testing the case corresponding to each weight to-be-scored item in turn, the omission during the test can be reduced, and the test coverage rate can be improved. On the other hand, when the ranking strategy is updated, only the updated case can be tested, and the existing case is not tested, so that the change of the test script and the test process can be greatly reduced, the test efficiency can be improved, the cost of developing the test script can be reduced, and the scenario that the search engine ranking strategy of the recruitment platform is continuously updated and optimized is applicable.

[0144] Figure 3Each module / unit in the device shown has the function of implementing each step in the testing method of the search engine ranking strategy of the recruitment platform provided by the above method embodiment, and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0145] According to some embodiments of the present application, optionally, the multiple weight scoring items include a first type of weight scoring items and a second type of weight scoring items, the first type of weight scoring items are weight scoring items determined based on the user's historical behavior, and the second type of weight scoring items are weight scoring items determined based on the basic attributes of the search results.

[0146] When the search result is a job seeker's resume, the first category of weight scoring items includes a weight scoring item for resumes that the recruiter is not interested in, and the second category of weight scoring items includes at least one of a search term similarity weight scoring item, a resume active time decay weight scoring item, a weight scoring item for the most recent job function in the resume hitting the recruitment function, a weight scoring item for the most recent job industry in the resume hitting the recruitment industry, a salary weight scoring item, and an educational background weight scoring item.

[0147] When the search result is a job position, the first type of weighted scoring items includes at least one of the weighted scoring items of the job applicant's applied positions, the weighted scoring items of the areas the job applicant is not interested in, and the weighted scoring items of the commuting distances the job applicant is not interested in. The second type of weighted scoring items includes at least one of the weighted scoring items of the search term similarity, the salary weighted scoring items, the function weighted scoring items, and the office location weighted scoring items.

[0148] According to some embodiments of the present application, optionally, the first search result is a resume of a first job applicant, and the second search result is a resume of a second job applicant. The first calculation module 303 can be configured to: calculate the weights of multiple weighted items to be scored in the resume of the first job applicant according to the first total score formula to obtain a first ranking total score for the resume of the first job applicant; and calculate the weights of multiple weighted items to be scored in the resume of the second job applicant according to the first total score formula to obtain a second ranking total score for the resume of the second job applicant.

[0149] Among them, the expression of the first total score formula is:

[0150] F 总分1 =F1*F2*F3*F4*F5*F6*F7

[0151] Among them, F 总分1 represents the total score of resume ranking, F1 represents the weight of search term similarity, F2 represents the weight of job seeker active time decay, F3 represents the weight of the most recent job function in the resume hitting the recruitment function, F4 represents the weight of the most recent job industry in the resume hitting the recruitment industry, F5 represents the weight of salary, F6 represents the weight of education background, and F7 represents the weight of resumes that the recruiter is not interested in.

[0152] According to some embodiments of the present application, optionally, the first search result is a first job position, and the second search result is a second job position. The first calculation module 303 can be configured to: calculate the weights of the plurality of weighted to-be-scored items in the first job position according to the second total score formula to obtain a first ranking total score of the first job position; and calculate the weights of the plurality of weighted to-be-scored items in the second job position according to the second total score formula to obtain a second ranking total score of the second job position.

[0153] The expression of the second total score formula is:

[0154] F 总分2 =F1’*F2’*F3’*F4’*F5’*F6’*F7’

[0155] F 总分2 represents a position ranking total score, F1’ represents a search term similarity weight, F2’ represents a job seeker has delivered a position weight, F3’ represents a salary weight, F4’ represents a function weight, F5’ represents an office location weight, F6’ represents a job seeker is not interested in the area weight, and F7’ represents a job seeker is not interested in the commuting distance weight.

[0156] According to some embodiments of the present application, optionally, the test device 30 of the search engine ranking strategy of the recruitment platform can further include an update module. After the plurality of use cases are all tested, if it is detected that the ranking strategy is updated, and the update is to add a new weighted scoring item and a corresponding new use case, the new use case is taken as a target use case, and only the new use case is tested, and the existing use cases are not tested.

[0157] According to some embodiments of the present application, optionally, the running module 302 can be configured to: place the plurality of use cases in a comma-separated value (CSV) file, each use case including at least a use case ID, a use case description, and an expected result; read the plurality of use cases in the CSV file, and parameterize the plurality of read use cases; place the first search result, the second search result, and the historical behavior data of the user as pre-data in the CSV file, the historical behavior data of the user being used to set the first type of weighted scoring item, the first search result and the second search result corresponding to a target ID of the target use case; and call a search engine interface to run the plurality of use cases of the search engine, the plurality of use cases being specifically configured to call the first search result and the second search result corresponding to the target ID in the CSV file, and score the plurality of weighted to-be-scored items in the first search result and the second search result to obtain the weights of the plurality of weighted to-be-scored items in the first search result and the weights of the plurality of weighted to-be-scored items in the second search result.

[0158] The electronic device in the embodiments of the present application can be a user terminal device, can be a server, can be other computing devices, and can also be a cloud server.Figure 4 A hardware structural diagram of an electronic device of an embodiment of the present application is shown, which can include a processor 401 and a memory 402 storing computer program instructions, and the processor 401 implements the flow or function of the method of any of the above embodiments when executing the computer program instructions.

[0159] Specifically, the processor 401 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits of the embodiments of the present application. The memory 402 can include a mass storage for data or instructions. For example, the memory 402 can be at least one of 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 magnetic tape, a universal serial bus (USB) drive, or other physical / tangible memory storage device. Also, the memory 402 can include removable or non-removable (or fixed) media. Further, the memory 402 can be internal or external to the integrated gateway disaster recovery device. The memory 402 can be a non-volatile solid-state memory. In other words, generally the memory 402 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 (e.g., by one or more processors), the operations described in the method of the embodiments of the present application can be performed. The processor 401 implements the flow or function of the method of any of the above embodiments by reading and executing the computer program instructions stored in the memory 402.

[0160] In one example, Figure 4The electronic device shown can also include a communication interface 403 and a bus 410. Among them, the processor 401, the memory 402, the communication interface 403 are connected through the bus 410 and complete the communication between each other. The communication interface 403 is mainly used to realize the communication between various modules, devices, units and / or equipment in the embodiments of the application. The bus 410 includes hardware, software or both, which can couple the components of the online data traffic billing device to each other. For example, the bus can include at least one of the following: an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front side bus (FSB), a hyper transport (HT) interconnect, an industry 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 (VLB) bus or other suitable bus. The bus 410 can include one or more buses. Although the embodiments of the application describe or show a specific bus, any suitable bus or interconnection method can be considered by the embodiments of the application.

[0161] In combination with the method in the above embodiments, the embodiments of the application further provide a computer readable storage medium, which has stored thereon computer program instructions, and the computer program instructions are executed by a processor to implement the flow or function of any of the methods in the above embodiments.

[0162] In addition, the embodiments of the application also provide a computer program product, which has stored thereon computer program instructions, and the computer program instructions are executed by a processor to implement the flow or function of any of the methods in the above embodiments.

[0163] The flowcharts and / or block diagrams of the methods, devices, systems and computer program products of the embodiments of the application are described above as examples, and the related aspects are described. It should be understood that each block in the flowchart and / or block diagram can be implemented by computer program instructions, or by special hardware that performs specified functions or actions, or by a combination of special hardware and computer instructions. For example, these computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, to form a machine, so that the instructions executed by the processor enable the implementation of the functions / actions specified in each block or combination of blocks in the flowchart and / or block diagram. Such a processor can be a general purpose processor, a special purpose processor, a special application processor, or a field programmable logic circuit.

[0164] The functional blocks shown in the structural block diagram of the embodiments 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 tasks. The program or code segment can be stored in a memory or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0165] It should be noted that the present application is not limited to the specific configurations and processes described above or shown in the drawings. The above description is merely a specific implementation of the present application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the described systems, devices, modules or units can refer to the corresponding processes in the method embodiments, and do not need to be described again.

Claims

1. A method for testing a search engine ranking strategy of a recruitment platform, characterized in that, The search engine is provided with multiple use cases, and the multiple use cases are used to score multiple weight-to-be-scored items in a search result based on preset multiple weight-score items and scoring standards of the respective weight-score items, to obtain weights of the respective weight-to-be-scored items, and the multiple use cases correspond to the multiple weight-score items and the multiple weight-to-be-scored items one by one; the sorting strategy is to calculate a sorting total score of a search result according to the weights of the multiple weight-to-be-scored items in the search result, and sort multiple search results in descending order of the sorting total score, and the test method comprises: Obtaining a first search result and a second search result for testing a target use case, the target weight-to-be-scored item in the first search result and the second search result is different, and other weight-to-be-scored items except the target weight-to-be-scored item are the same, the target weight-to-be-scored item is a weight-to-be-scored item corresponding to the target use case; the target weight-to-be-scored item in the first search result meets a preset recruitment or job-seeking requirement, the target weight-to-be-scored item in the second search result does not meet the recruitment or job-seeking requirement, the scoring standard of the target weight-score item is that the target weight-to-be-scored item meets the recruitment or job-seeking requirement to give a weight N1, and does not meet the recruitment or job-seeking requirement to give a weight N2, N1>N2; Running multiple use cases of the search engine to obtain weights of the multiple weight-to-be-scored items in the first search result and the multiple weight-to-be-scored items in the second search result; calculating a first sorting total score of the first search result according to the weights of the multiple weight-to-be-scored items in the first search result, and calculating a second sorting total score of the second search result according to the weights of the multiple weight-to-be-scored items in the second search result; calculating the difference or quotient of the first sorting total score and the second sorting total score, and taking the difference or quotient as the test weight of the target weight-to-be-scored item; comparing the test weight of the target weight-to-be-scored item with the expected weight of the target weight-to-be-scored item, if they are consistent, it is determined that the target use case test is passed; if they are not consistent, it is determined that the target use case test is not passed.

2. The test method of claim 1, wherein, The multiple weight-score items include first-type weight-score items and second-type weight-score items, the first-type weight-score items are weight-score items determined based on historical behaviors of users, and the second-type weight-score items are weight-score items determined based on basic attributes of search results; When the search result is a resume of a job seeker, the first-type weight-score items include a recruiter-uninterested-resume weight-score item, and the second-type weight-score items include at least one of a search-term-similarity weight-score item, a resume-active-time-decay weight-score item, a latest-job-function-in-resume-hits-recruitment-function weight-score item, a latest-job-industry-in-resume-hits-recruitment-industry weight-score item, a salary weight-score item, and an education weight-score item; When the search result is a job position, the first type of weight score item includes at least one of a job seeker having delivered a position weight score item, a job seeker not interested in an area weight score item, and a job seeker not interested in a commuting distance weight score item, and the second type of weight score item includes at least one of a search word similarity weight score item, a salary weight score item, a function weight score item, and an office location weight score item.

3. The test method of claim 1, wherein, The first search result is a first job seeker resume, and the second search result is a second job seeker resume; According to the weights of the multiple weight to be scored items in the first search result, a first total score of the first search result is calculated, and according to the weights of the multiple weight to be scored items in the second search result, a second total score of the second search result is calculated, including: According to the first total score formula, the weights of the multiple weight to be scored items in the first job seeker resume are calculated to obtain the first total score of the first job seeker resume; According to the first total score formula, the weights of the multiple weight to be scored items in the second job seeker resume are calculated to obtain the second total score of the second job seeker resume; The expression of the first total score formula is: F 总分1 = F1*F2*F3*F4*F5*F6*F7 wherein F 总分1 F1 represents a search term similarity weight, F2 represents a resume active time decay weight, F3 represents a weight for a recent job function in the resume hitting a recruitment function, F4 represents a weight for a recent job industry in the resume hitting a recruitment industry, F5 represents a salary weight, F6 represents an educational background weight, and F7 represents a recruiter uninterested resume weight.

4. The test method of claim 1, wherein, The first search result is a first job position, and the second search result is a second job position; According to the weights of the multiple weight to be scored items in the first search result, a first total score of the first search result is calculated, and according to the weights of the multiple weight to be scored items in the second search result, a second total score of the second search result is calculated, including: According to the second total score formula, the weights of the multiple weight to be scored items in the first job position are calculated to obtain the first total score of the first job position; According to the second total score formula, the weights of the multiple weight to be scored items in the second job position are calculated to obtain the second total score of the second job position; The expression of the second total score formula is: F 总分2 = F1' * F2' * F3' * F4' * F5' * F6' * F7' wherein F 总分2 represents the position ranking total score, F1' represents the search term similarity weight, F2' represents the job seeker has delivered position weight, F3' represents the salary weight, F4' represents the function weight, F5' represents the office location weight, F6' represents the job seeker not interested area weight, and F7' represents the job seeker not interested commuting distance weight.

5. The test method of claim 1, wherein, The test method further includes: After the multiple use cases are all tested, if it is detected that the ranking strategy is updated, and the update is to add a new weight score item and a new use case corresponding to the new weight score item, the new use case is taken as a target use case, and only the new use case is tested, and the existing use cases are not tested.

6. The test method of claim 1, wherein, The multiple use cases of the search engine are run to obtain the weights of the multiple weight to be scored items in the first search result and the weights of the multiple weight to be scored items in the second search result, including: The multiple use cases are placed in a comma-separated value (CSV) file, and each use case at least includes a use case ID, a use case description, and an expected result; The multiple use cases in the CSV file are read, and the read multiple use cases are parameterized; The first search result, the second search result, and historical behavior data of a user are taken as pre-data and placed in the CSV file, the historical behavior data of the user is used to set the first type of weight score item, and the first search result, the second search result, and a target ID of the target use case correspond to each other. The search engine interface is called to run multiple use cases of the search engine, and the multiple use cases are specifically used to call first search results and second search results corresponding to a target ID in a CSV file and score multiple weight-to-be-scored items in the first search results and the second search results to obtain weights of the multiple weight-to-be-scored items in the first search results and weights of the multiple weight-to-be-scored items in the second search results.

7. A device for testing a search engine ranking strategy of a recruitment platform, characterized in that, The search engine is provided with multiple use cases, and the multiple use cases are used to score multiple weight-to-be-scored items in any search result based on preset multiple weight scoring items and scoring standards of each weight scoring item to obtain weights of each weight-to-be-scored item. The multiple use cases correspond to the multiple weight scoring items and the multiple weight-to-be-scored items one by one. The sorting strategy is that, for any search result, the sorting total score of the search result is calculated according to the weights of the multiple weight-to-be-scored items in the search result, and the multiple search results are sorted in descending order of the sorting total score. The test device comprises: The acquisition module is configured to acquire first search results and second search results for testing a target use case. The target weight-to-be-scored item in the first search results is different from the target weight-to-be-scored item in the second search results, and other weight-to-be-scored items except the target weight-to-be-scored item are the same. The target weight-to-be-scored item is a weight-to-be-scored item corresponding to the target use case. The target weight-to-be-scored item in the first search results meets preset recruitment or job-seeking requirements, the target weight-to-be-scored item in the second search results does not meet the recruitment or job-seeking requirements, the scoring standard of the target weight-to-be-scored item is that the target weight-to-be-scored item is given a weight N1 if it meets the recruitment or job-seeking requirements, and is given a weight N2 if it does not meet the recruitment or job-seeking requirements, and N1>N2. The running module is configured to run multiple use cases of the search engine to obtain weights of the multiple weight-to-be-scored items in the first search results and weights of the multiple weight-to-be-scored items in the second search results. The first calculation module is configured to calculate a first sorting total score of the first search results according to the weights of the multiple weight-to-be-scored items in the first search results, and calculate a second sorting total score of the second search results according to the weights of the multiple weight-to-be-scored items in the second search results. The second calculation module is configured to calculate a difference or quotient of the first sorting total score and the second sorting total score, and take the difference or quotient as a test weight of the target weight-to-be-scored item. The judgment module is configured to compare the test weight of the target weight-to-be-scored item with an expected weight of the target weight-to-be-scored item. If the test weight is consistent with the expected weight, it is determined that the target use case passes the test. If the test weight is not consistent with the expected weight, it is determined that the target use case fails the test.

8. An electronic device, comprising: The electronic device comprises a processor and a memory storing computer program instructions; and the electronic device implements the test method of the search engine sorting strategy of the recruitment platform according to any one of claims 1-6 when executing the computer program instructions.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the test method of the search engine sorting strategy of the recruitment platform according to any one of claims 1-6.

10. A computer program product, characterised in that, It comprises computer program instructions which, when executed by a processor, implement a test method of a search engine ranking strategy of a recruitment platform as claimed in any one of claims 1-6.

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