Optimization method and device of resume screening rule, equipment and storage medium

Abstract

The invention discloses a resume screening rule optimization method and device, equipment and a storage medium. The method comprises the following steps: combining resume screening rules in a to-be-optimized screening rule set to obtain at least one candidate resume screening rule combination; determining at least one candidate applicant matching degree according to the historical applicant features and the at least one candidate resume screening rule combination; according to the at least one candidate applicant matching degree, selecting a to-be-evaluated resume screening rule combination from the candidate resume screening rule combinations, and determining a to-be-evaluated applicant matching degree of the to-be-evaluated resume screening rule combination; determining a rule combination score of the to-be-evaluated resume screening rule combination according to the historical applicant characteristics, the to-be-evaluated resume screening rule combination and the to-be-evaluated applicant matching degree; and if the rule combination score meets the rule generation condition, determining the to-be-evaluated resume screening rule combination as a target resume screening rule combination. According to the technical scheme, the reliability of the resume screening rule can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and particularly to the field of information processing technology. Specifically, the present application relates to a method, device, equipment and storage medium for optimizing resume screening rules. Background Art

[0002] Processing resumes is a key task of the human resources department and an important module in the human resources informatization system. When an enterprise conducts recruitment, it often involves screening the resumes of candidates. Traditional methods are to browse the resume information of candidates one by one by recruiters and make a manual judgment on whether the candidates meet the enterprise's recruitment requirements based on the information in the candidates' resumes to screen out candidates who meet the enterprise's recruitment requirements.

[0003] In the face of a large number of candidate resumes, to improve the efficiency of resume screening, a resume rule engine is usually set up to process complex resume data. However, most of the rules of the rule engine are formulated manually. When dealing with complex data relationships, the traditional rule engine mainly relies on simple rule matching and cannot avoid problems such as strong subjectivity and being easily affected by biases. Summary of the Invention

[0004] The present application provides a method, device, equipment and storage medium for optimizing resume screening rules to improve the reliability of resume screening rules.

[0005] According to one aspect of the present application, a method for optimizing resume screening rules is provided. The method includes:

[0006] Combining the resume screening rules in the set of screening rules to be optimized for the target position to obtain at least one candidate resume screening rule combination for the target position;

[0007] Determining at least one candidate applicant matching degree of the historical applicant according to the historical applicant characteristics of the historical applicant and at least one candidate resume screening rule combination; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the candidate resume screening rule combination;

[0008] Selecting a resume screening rule combination to be evaluated from the at least one candidate resume screening rule combination according to the at least one candidate applicant matching degree, and determining the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated;

[0009] Determining the rule combination score of the resume screening rule combination to be evaluated according to the historical applicant characteristics, the resume screening rule combination to be evaluated and the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated;

[0010] If the combined rule score meets the rule generation condition, then determine the to-be-evaluated resume screening rule combination as the target resume screening rule combination.

[0011] According to another aspect of the present application, there is provided an optimization device for resume screening rules, the device includes:

[0012] A rule combination module, configured to combine the resume screening rules in the to-be-optimized screening rule set for a target position to obtain at least one candidate resume screening rule combination for the target position;

[0013] A first matching degree determination module, configured to determine at least one candidate applicant matching degree of the historical applicant according to the historical applicant characteristics of the historical applicant and at least one candidate resume screening rule combination; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the candidate resume screening rule combination;

[0014] A second matching degree determination module, configured to select a to-be-evaluated resume screening rule combination from the at least one candidate resume screening rule combination according to the at least one candidate applicant matching degree, and determine the to-be-evaluated applicant matching degree corresponding to the to-be-evaluated resume screening rule combination;

[0015] A score determination module, configured to determine the combined rule score of the to-be-evaluated resume screening rule combination according to the historical applicant characteristics, the to-be-evaluated resume screening rule combination, and the to-be-evaluated applicant matching degree;

[0016] A rule combination determination module, configured to determine the to-be-evaluated resume screening rule combination as the target resume screening rule combination if the combined rule score meets the rule generation condition.

[0017] According to another aspect of the present application, there is provided an electronic device, the electronic device includes:

[0018] One or more processors;

[0019] A memory, configured to store one or more programs;

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the optimization methods for resume screening rules provided by the embodiments of the present application.

[0021] According to another aspect of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements any one of the optimization methods for resume screening rules provided by the embodiments of the present application.

[0022] According to another aspect of the present application, there is provided a computer program product, including a computer program which, when executed by a processor, implements any one of the optimization methods for resume screening rules provided by the embodiments of the present application.

[0023] In the present application, by combining the resume screening rules in the screening rule set to be optimized for the target position, at least one candidate resume screening rule combination for the target position is obtained; according to the historical applicant characteristics of historical applicants and at least one candidate resume screening rule combination, at least one candidate applicant matching degree of the historical applicant is determined; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the candidate resume screening rule combination; according to at least one candidate applicant matching degree, a resume screening rule combination to be evaluated is selected from at least one candidate resume screening rule combination, and the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated is determined; according to the historical applicant characteristics, the resume screening rule combination to be evaluated, and the candidate applicant matching degree, the rule combination score of the resume screening rule combination to be evaluated is determined; if the rule combination score meets the rule generation condition, the resume screening rule combination to be evaluated is determined as the target resume screening rule combination. The above technical solution, in the rule generation stage, through the rule recombination and optimization of the resume screening rule set for the target position, helps to solve problems such as strong subjectivity and susceptibility to bias, and improves the reliability of the resume screening rules. Brief Description of the Drawings

[0024] Figure 1 is a flowchart of an optimization method for resume screening rules provided by Embodiment 1 of the present application;

[0025] Figure 2 is a flowchart of an optimization method for resume screening rules provided by Embodiment 2 of the present application;

[0026] Figure 3 is a schematic structural diagram of an optimization device for resume screening rules provided by Embodiment 3 of the present application;

[0027] Figure 4 is a schematic structural diagram of an electronic device implementing the optimization method for resume screening rules of Embodiment 4 of the present application. Detailed Embodiments

[0028] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present application.

[0029] It should be noted that in the description of the present application, the claims and the above-mentioned drawings, terms such as "first", "second", etc. are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0030] In addition, it should be noted that in the technical solution of the present application, the collection, storage, use, processing, transmission, provision, disclosure and other processing of relevant data such as the to-be-optimized screening rule set and resume screening rules involved comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0031] Embodiment 1

[0032] Figure 1 is a flowchart of an optimization method for resume screening rules provided according to Embodiment 1 of the present application. This embodiment is applicable to the situation of reorganizing and optimizing preset resume screening rules during the resume screening rule generation stage, and can be executed by an optimization device for resume screening rules. The optimization device for resume screening rules can be implemented in the form of hardware and / or software, and the optimization device for resume screening rules can be configured in a computer device, such as a server. As Figure 1 shown, the method includes:

[0033] S110. Combine the resume screening rules in the to-be-optimized screening rule set for the target position to obtain at least one candidate resume screening rule combination for the target position.

[0034] In this embodiment, the target position refers to the position for which resume screening rules need to be generated currently. The screening rule set to be optimized refers to the set composed of resume screening rules that are pre-established and to be put into use. The resume screening rule refers to the conditions or criteria set based on various characteristics of candidates, used to evaluate the matching degree between candidates and the recruitment position; the resume screening rule may include basic condition rules, ability and skill rules, work experience and performance rules, other rules, etc.; among them, the basic condition rules may include at least one of educational requirements, work experience, professional background, etc.; the ability and skill rules may include at least one of language ability, technical skills, communication ability, etc.; the work experience and performance rules may include at least one of industry experience, project experience, performance performance, etc.; other rules may include at least one of geographical requirements, certificates and honors, etc. The candidate resume screening rule combination refers to the rule combination obtained by freely combining the resume screening rules in the screening rule set to be optimized; exemplarily, this rule combination may be a rule combination composed of educational requirements and work experience in the basic condition rules and geographical requirements in other rules, or a set composed of basic condition rules, ability and skill rules, and work experience and performance rules.

[0035] Exemplarily, the Monte Carlo Tree Search algorithm (MCTS) can be used to combine the resume screening rules in the screening rule set to be optimized for the target position to obtain at least one candidate resume screening rule combination for the target position.

[0036] S120. Determine at least one candidate candidate matching degree of the historical candidate according to the historical candidate characteristics of the historical candidate and at least one candidate resume screening rule combination.

[0037] In this embodiment, the historical candidate is a candidate who has applied before. The historical candidate characteristics refer to various attributes of the candidate who has applied before, such as educational background, work experience, skills, previous interview scores, etc. The candidate candidate matching degree is used to characterize the matching degree between the historical candidate and the candidate resume screening rule combination; by calculating the fit degree between the characteristics of this candidate and the screening rule, it is evaluated whether this candidate meets the requirements of the target position under this screening rule.

[0038] Optionally, for each candidate resume screening rule combination, based on the corresponding relationship between the feature fields of the historical candidate characteristics of the historical candidate and the candidate feature scores, using the feature fields in this candidate resume screening rule combination as indexes, determine at least one candidate feature score to be evaluated; perform weighted summation on at least one candidate feature score to be evaluated to obtain the candidate candidate matching degree of the historical candidate under this candidate resume screening rule combination.

[0039] In this embodiment, the feature field refers to the field in the resume used to describe the attributes of the applicant; they represent the specific information of the applicant, such as education background, years of work experience, skill certifications, language proficiency, etc. The candidate feature score refers to scoring each feature field of the historical applicant's features to evaluate the applicant's performance in a specific feature; for example, for work experience, it can be scored by the number of years or the job level. Each feature will have a specific score value, indicating the applicant's advantages or disadvantages in this area. The feature score to be evaluated refers to the candidate feature scores corresponding to the feature fields in the candidate resume screening rule combination.

[0040] Exemplarily, the corresponding relationship between the feature fields of the historical applicant's features and the candidate feature scores can be represented in the form of a matrix. Assuming there are three historical applicants and four screening rules, the matrix can be represented as:

[0041]

[0042] Where W is the matrix representing the corresponding relationship between the feature fields of the historical applicant's features and the candidate feature scores. w ij refers to the candidate feature score of the applicant's features of the i-th historical applicant under the j-th screening rule.

[0043] Furthermore, the candidate applicant matching degree can be determined by the following formula:

[0044]

[0045] Where R refers to the candidate applicant matching degree. a j refers to the weight of the j-th screening rule. w ij refers to the candidate feature score of the i-th historical applicant under the j-th screening rule; it should be noted that since the feature fields in the candidate resume screening rule combination are part or all of the feature fields of the historical applicant's features, j at this time is not necessarily consecutive. For example, it can be j = 1, 3, 4, or it can be j = 1, 2, 4.

[0046] S130. Select the resume screening rule combination to be evaluated from at least one candidate resume screening rule combination according to at least one candidate applicant matching degree, and determine the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated.

[0047] In this embodiment, the resume screening rule combination to be evaluated refers to a set of rules selected by the system from among many candidate rule combinations for further evaluating the effectiveness of this rule combination. The candidate applicant matching degree to be evaluated refers to the matching degree between the historical applicant and this combination under the resume screening rule combination to be evaluated.

[0048] In an alternative embodiment, the matching degrees of at least one candidate applicant can be sorted, and the candidate resume screening rule combination corresponding to the candidate applicant matching degree with a higher ranking is determined as the resume screening rule combination to be evaluated.

[0049] Exemplarily, the candidate resume screening rule combination corresponding to the candidate applicant matching degree ranked first can be determined as the resume screening rule combination to be evaluated.

[0050] Furthermore, if there are multiple resume screening rule combinations to be evaluated, MCTS can be used to repeatedly combine the resume screening rules in the set of screening rules to be optimized for the target position, obtaining at least one candidate resume screening rule combination for the target position until there is only one resume screening rule combination to be evaluated.

[0051] S140. Determine the rule combination score of the resume screening rule combination to be evaluated according to the historical applicant characteristics, the resume screening rule combination to be evaluated, and the matching degree of the applicant to be evaluated.

[0052] In this embodiment, the rule combination score refers to the evaluation of the resume screening rule combination to be evaluated, which is used to evaluate the advantages and disadvantages of this combination.

[0053] In an alternative embodiment, the matching degree of the applicant to be evaluated can be directly used as the rule combination score of the resume screening rule combination to be evaluated.

[0054] S150. If the rule combination score meets the rule generation condition, the resume screening rule combination to be evaluated is determined as the target resume screening rule combination.

[0055] In this embodiment, the rule generation condition is preset through a large number of experiments and according to actual situations or empirical values. Exemplarily, the rule generation condition can be that the rule combination score is greater than or equal to the combination score threshold. The target resume screening rule combination refers to the finally selected screening rule combination that meets the rule generation condition, which is usually used in subsequent recruitment to screen the most suitable candidates.

[0056] In an alternative embodiment, if the rule combination score does not meet the rule generation condition, MCTS is used to re - execute the combination of the resume screening rules in the set of screening rules to be optimized for the target position, obtaining at least one candidate resume screening rule combination for the target position until the rule combination score meets the rule generation condition and / or the number of iterations of MCTS reaches the iteration number threshold.

[0057] In another alternative embodiment, instead of determining whether the rule combination score meets the rule generation condition, MCTS can be directly used to re - execute the combination of resume screening rules in the set of screening rules to be optimized for the target position until the change rate of the rule combination score meets the rule generation condition and / or the number of iterations of MCTS reaches the iteration number threshold.

[0058] In this embodiment, the change rate of the rule combination score refers to the difference between the rule combination score obtained in the current iteration process and the rule combination score obtained in the previous iteration process; the rule generation condition at this time can be that the change rate of the rule combination score is less than or equal to the change rate threshold.

[0059] In the embodiment of the present application, by combining the resume screening rules in the set of screening rules to be optimized for the target position, at least one candidate resume screening rule combination for the target position is obtained; according to the historical candidate characteristics of historical candidates and at least one candidate resume screening rule combination, at least one candidate candidate matching degree of the historical candidates is determined; according to at least one candidate candidate matching degree, a resume screening rule combination to be evaluated is selected from at least one candidate resume screening rule combination, and the candidate matching degree to be evaluated corresponding to the resume screening rule combination to be evaluated is determined; according to the historical candidate characteristics, the resume screening rule combination to be evaluated, and the candidate matching degree to be evaluated, the rule combination score of the resume screening rule combination to be evaluated is determined; if the rule combination score meets the rule generation condition, the resume screening rule combination to be evaluated is determined as the target resume screening rule combination. The above - mentioned technical solution, in the rule generation stage, through the rule recombination and optimization of the resume screening rule set for the target position, helps to solve problems such as strong subjectivity and being easily affected by biases, and improves the reliability of the resume screening rules.

[0060] Embodiment Two

[0061] Figure 2 is a flowchart of an optimization method for a resume screening rule provided according to Embodiment Two of the present application. On the basis of the technical solutions of the above - mentioned embodiments, "determining the rule combination score of the resume screening rule combination to be evaluated according to the historical candidate characteristics, the resume screening rule combination to be evaluated, and the candidate matching degree to be evaluated" is refined into "determining the bias score of the resume screening rule combination to be evaluated according to the historical candidate characteristics and the resume screening rule combination to be evaluated; determining the rule combination score of the resume screening rule combination to be evaluated according to the bias score and the candidate matching degree to be evaluated". It should be noted that for the parts not detailed in the embodiments of the present application, reference can be made to the relevant descriptions of other embodiments. As Figure 2 shown, the method includes:

[0062] S210. Combine the resume screening rules in the target position's screening rules set to be optimized to obtain at least one candidate resume screening rule combination for the target position.

[0063] S220. Determine at least one candidate applicant matching degree of the historical applicant according to the historical applicant characteristics of the historical applicant and at least one candidate resume screening rule combination.

[0064] S230. Select a resume screening rule combination to be evaluated from at least one candidate resume screening rule combination according to at least one candidate applicant matching degree, and determine the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated.

[0065] S240. Determine the bias score of the resume screening rule combination to be evaluated according to the historical applicant characteristics and the resume screening rule combination to be evaluated.

[0066] In this embodiment, the bias score is an indicator of whether there are bias or unfair factors in the resume screening process of the resume screening rule combination to be evaluated.

[0067] Optionally, based on the correspondence between the feature fields of the historical applicant characteristics and the candidate bias feature values, use the feature fields in the resume screening rule combination to be evaluated as an index to determine at least one bias feature value to be evaluated; perform a weighted sum on at least one bias feature value to be evaluated to obtain the bias score of the resume screening rule combination to be evaluated.

[0068] In this embodiment, the candidate bias feature value refers to the bias score corresponding to each applicant feature in the historical applicant characteristics; this score is preset in advance through a large number of experiments and according to the actual situation or empirical values. The bias feature value to be evaluated refers to the bias score of each feature of the historical applicant under the resume screening rule combination to be evaluated.

[0069] Exemplarily, the bias score can be determined by the following formula:

[0070]

[0071] Among them, B refers to the bias score of the resume screening rule combination to be evaluated. βk refers to the weight of the kth bias dimension of the historical applicant under the resume screening rule combination to be evaluated; this bias dimension corresponds to the applicant characteristics. b k refers to the bias feature value to be evaluated of the kth bias dimension of the historical applicant under the resume screening rule combination to be evaluated.

[0072] S250. Determine the rule combination score of the resume screening rule combination to be evaluated according to the bias score and the candidate applicant matching degree to be evaluated.

[0073] Optionally, a pre-configured bias weight is used to weight the bias score to obtain a bias index; the difference between the candidate match degree to be evaluated and the bias index is calculated to obtain the rule combination score of the resume screening rule combination to be evaluated.

[0074] In this embodiment, the bias weight is a hyperparameter used to control the influence of bias. By adjusting the value of the bias weight, the best balance can be found between the score and fairness.

[0075] Exemplarily, the rule combination score can be determined by the following formula:

[0076] C = R - λB;

[0077] Where C refers to the rule combination score. R refers to the candidate match degree. λ refers to the bias weight. B refers to the bias score.

[0078] S260. If the rule combination score meets the rule generation condition, the resume screening rule combination to be evaluated is determined as the target resume screening rule combination.

[0079] In an alternative implementation, if the rule combination score does not meet the rule generation condition, the feature weights in the resume screening rule combination to be evaluated are adjusted, and the rule combination score of the adjusted resume screening rule combination to be evaluated is re-determined until the adjusted rule combination score or the iteration times of the rule combination adjustment meet the combination adjustment end condition.

[0080] In this embodiment, the combination adjustment end condition is set artificially in advance through a large number of experiments and according to the actual situation or empirical values; exemplarily, the combination adjustment condition can be that the iteration times of the rule combination adjustment are equal to the iteration times threshold and / or the rule combination score is greater than or equal to the combination score threshold.

[0081] Specifically, if the rule combination score does not meet the rule generation condition, with the goal of maximizing the candidate match degree and minimizing the bias score, the feature weights in the resume screening rule combination to be evaluated are adjusted, and the rule combination score of the adjusted resume screening rule combination to be evaluated is re-determined until the adjusted rule combination score or the iteration times of the rule combination adjustment meet the combination adjustment end condition.

[0082] In another alternative implementation, if the rule combination score does not meet the rule generation condition, MCTS is used to re-perform the combination of the resume screening rules in the screening rule set to be optimized for the target position with the goal of maximizing the candidate match degree and minimizing the bias score, to obtain at least one candidate resume screening rule combination for the target position until the rule combination score meets the rule generation condition and / or the iteration times of MCTS reach the iteration times threshold.

[0083] In another alternative embodiment, MCTS can be used as a generator to generate candidate resume screening rule combinations to be evaluated and determine the matching degrees of candidates to be evaluated for the candidate resume screening rule combinations to be evaluated; the calculation of bias scores is used as a discriminator to measure the bias scores of the candidate resume screening rule combinations to be evaluated, and the bias scores are fed back to the generator so that the generator can re - execute the combination of resume screening rules in the screening rules set to be optimized for the target position with the goal of maximizing the matching degrees of candidate candidates and minimizing the bias scores, obtaining at least one candidate resume screening rule combination for the target position until the rule combination score / rule combination score change rate meets the rule generation condition and / or the number of iterations reaches the iteration number threshold.

[0084] In the embodiment of the present application, by combining the resume screening rules in the screening rules set to be optimized for the target position, at least one candidate resume screening rule combination for the target position is obtained; according to the historical candidate characteristics of historical candidates and at least one candidate resume screening rule combination, at least one candidate matching degree of historical candidates is determined; according to at least one candidate matching degree, a candidate resume screening rule combination to be evaluated is selected from at least one candidate resume screening rule combination, and the matching degree of the candidate to be evaluated corresponding to the candidate resume screening rule combination to be evaluated is determined; according to the historical candidate characteristics and the candidate resume screening rule combination to be evaluated, the bias score of the candidate resume screening rule combination to be evaluated is determined; according to the bias score and the matching degree of the candidate to be evaluated, the rule combination score of the candidate resume screening rule combination to be evaluated is determined; if the rule combination score meets the rule generation condition, the candidate resume screening rule combination to be evaluated is determined as the target resume screening rule combination. In the above - mentioned technical solution, in the rule generation stage, by reorganizing and optimizing the resume screening rule set of the target position, it helps to solve problems such as strong subjectivity and susceptibility to bias, and improves the reliability of the resume screening rules.

[0085] Embodiment III

[0086] Figure 3 FIG. 10 is a structural schematic diagram of an optimization device for resume screening rules provided according to Embodiment III of the present application, which is applicable to the situation of reorganizing and optimizing preset resume screening rules in the resume screening rule generation stage. The optimization device for resume screening rules can be implemented in the form of hardware and / or software, and the optimization device for resume screening rules can be configured in a computer device, such as a server. As Figure 3 shown, the device includes:

[0087] A rule combination module 310, configured to combine the resume screening rules in the screening rules set to be optimized for the target position to obtain at least one candidate resume screening rule combination for the target position;

[0088] The first matching degree determination module 320 is configured to determine at least one candidate applicant matching degree of a historical applicant according to the historical applicant characteristics of the historical applicant and at least one combination of candidate resume screening rules; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the combination of candidate resume screening rules.

[0089] The second matching degree determination module 330 is configured to select a resume screening rule combination to be evaluated from at least one combination of candidate resume screening rules according to at least one candidate applicant matching degree, and determine the matching degree of the applicant to be evaluated corresponding to the resume screening rule combination to be evaluated.

[0090] The scoring determination module 340 is configured to determine the rule combination score of the resume screening rule combination to be evaluated according to the historical applicant characteristics, the resume screening rule combination to be evaluated, and the matching degree of the applicant to be evaluated.

[0091] The rule combination determination module 350 is configured to determine the resume screening rule combination to be evaluated as the target resume screening rule combination if the rule combination score meets the rule generation condition.

[0092] In the embodiment of the present application, by combining the resume screening rules in the screening rule set to be optimized for the target position, at least one combination of candidate resume screening rules for the target position is obtained; according to the historical applicant characteristics of the historical applicant and at least one combination of candidate resume screening rules, at least one candidate applicant matching degree of the historical applicant is determined; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the combination of candidate resume screening rules; according to at least one candidate applicant matching degree, a resume screening rule combination to be evaluated is selected from at least one combination of candidate resume screening rules, and the matching degree of the applicant to be evaluated corresponding to the resume screening rule combination to be evaluated is determined; according to the historical applicant characteristics, the resume screening rule combination to be evaluated, and the matching degree of the applicant to be evaluated, the rule combination score of the resume screening rule combination to be evaluated is determined; if the rule combination score meets the rule generation condition, the resume screening rule combination to be evaluated is determined as the target resume screening rule combination. The above technical solution, in the rule generation stage, by reorganizing and optimizing the resume screening rule set of the target position, helps to solve problems such as strong subjectivity and being easily affected by biases, and improves the reliability of the resume screening rules.

[0093] Optionally, the scoring determination module 340 includes:

[0094] The first scoring determination unit is configured to determine the bias score of the resume screening rule combination to be evaluated according to the historical applicant characteristics and the resume screening rule combination to be evaluated.

[0095] The second scoring determination unit is configured to determine the rule combination score of the resume screening rule combination to be evaluated according to the bias score and the matching degree of the applicant to be evaluated.

[0096] Optionally, the first scoring determination unit is specifically configured to:

[0097] Based on the correspondence between the feature fields of historical candidate characteristics and candidate bias eigenvalues, using the feature fields in the resume screening rule combination to be evaluated as an index, determine at least one bias eigenvalue to be evaluated;

[0098] Perform a weighted sum of at least one bias eigenvalue to be evaluated to obtain the bias score of the resume screening rule combination to be evaluated.

[0099] Optionally, the second scoring determination unit is specifically configured to:

[0100] Use a pre-configured bias weight to weight the bias score to obtain a bias index;

[0101] Calculate the difference between the candidate matching degree to be evaluated and the bias index to obtain the rule combination score of the resume screening rule combination to be evaluated.

[0102] Optionally, the first matching degree determination module 320 is specifically configured to:

[0103] For each candidate resume screening rule combination, based on the correspondence between the feature fields of the historical candidate characteristics of historical candidates and candidate feature scores, using the feature fields in the candidate resume screening rule combination as an index, determine at least one feature score to be evaluated;

[0104] Perform a weighted sum of at least one feature score to be evaluated to obtain the candidate matching degree of the historical candidate under the candidate resume screening rule combination.

[0105] Optionally, the rule combination determination module 350 is further configured to, if the rule combination score does not meet the rule generation condition, adjust the feature weights in the resume screening rule combination to be evaluated, and re-determine the rule combination score of the adjusted resume screening rule combination to be evaluated until the adjusted rule combination score or the iteration times of the rule combination adjustment meet the combination adjustment end condition.

[0106] The resume screening rule optimization device provided by the embodiments of the present application can execute the resume screening rule optimization method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects for executing each resume screening rule optimization method.

[0107] According to the embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0108] Embodiment 4

[0109] Figure 4It is a schematic structural diagram of an electronic device 410 for implementing an optimization method of a resume screening rule according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0110] As Figure 4 shown, the electronic device 410 includes at least one processor 411, and a memory communicatively connected to the at least one processor 411, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 411 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 412 or the computer program loaded from the storage unit 418 into the random access memory (RAM) 413. In the RAM 413, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 411, the ROM 412, and the RAM 413 are connected to each other through a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0111] Multiple components in the electronic device 410 are connected to the I / O interface 415, including: an input unit 416, such as a keyboard, a mouse, etc.; an output unit 417, such as various types of displays, speakers, etc.; a storage unit 418, such as a magnetic disk, an optical disc, etc.; and a communication unit 419, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The processor 411 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 411 executes the various methods and processes described above, such as the optimization method of the resume screening rule.

[0113] In some embodiments, the method for optimizing resume screening rules may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 410 via the ROM 412 and / or the communication unit 419. When the computer program is loaded into the RAM 413 and executed by the processor 411, one or more steps of the method for optimizing resume screening rules described above may be executed. Alternatively, in other embodiments, the processor 411 may be configured for the method for optimizing resume screening rules by any other suitable means (e.g., by means of firmware).

[0114] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The computer programs for implementing the methods of this application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable device for optimizing resume screening rules, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0118] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0120] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps recited in this application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved, and no limitation is imposed herein.

[0121] The above specific embodiments do not constitute a limitation on the protection scope of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims

1. An optimization method for resume screening rules, characterized in that, Including: Combining the resume screening rules in the screening rules to be optimized for the target position set to obtain at least one candidate resume screening rule combination for the target position; Determining at least one candidate applicant matching degree of the historical applicant according to the historical applicant characteristics of the historical applicant and at least one candidate resume screening rule combination; wherein, the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the candidate resume screening rule combination; Selecting a resume screening rule combination to be evaluated from the at least one candidate resume screening rule combination according to the at least one candidate applicant matching degree, and determining the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated; Determining the rule combination score of the resume screening rule combination to be evaluated according to the historical applicant characteristics, the resume screening rule combination to be evaluated and the candidate applicant matching degree to be evaluated; If the rule combination score meets the rule generation condition, determining the resume screening rule combination to be evaluated as the target resume screening rule combination.

2. The method according to claim 1, wherein The determining the rule combination score of the resume screening rule combination to be evaluated according to the historical applicant characteristics, the resume screening rule combination to be evaluated and the candidate applicant matching degree to be evaluated includes: Determining the bias score of the resume screening rule combination to be evaluated according to the historical applicant characteristics and the resume screening rule combination to be evaluated; Determining the rule combination score of the resume screening rule combination to be evaluated according to the bias score and the candidate applicant matching degree to be evaluated.

3. The method according to claim 2, wherein The determining the bias score of the resume screening rule combination to be evaluated according to the historical applicant characteristics and the resume screening rule combination to be evaluated includes: Based on the correspondence between the feature fields of the historical applicant characteristics and the candidate bias feature values, using the feature fields in the resume screening rule combination to be evaluated as indexes, determining at least one bias feature value to be evaluated; Performing a weighted sum on the at least one bias feature value to be evaluated to obtain the bias score of the resume screening rule combination to be evaluated.

4. The method according to claim 2, wherein The determining the rule combination score of the resume screening rule combination to be evaluated according to the bias score and the candidate applicant matching degree to be evaluated includes: Using a pre-configured bias weight to weight the bias score to obtain a bias index; Calculating the difference between the candidate applicant matching degree to be evaluated and the bias index to obtain the rule combination score of the resume screening rule combination to be evaluated.

5. The method according to claim 1, wherein The determining at least one candidate applicant matching degree of the historical applicant according to the historical applicant characteristics of the historical applicant and at least one candidate resume screening rule combination includes: For each candidate resume screening rule combination, based on the correspondence between the feature fields of the historical applicant characteristics of the historical applicant and the candidate feature scores, using the feature fields in the candidate resume screening rule combination as indexes, determining at least one feature score to be evaluated; Performing a weighted sum on the at least one feature score to be evaluated to obtain the candidate applicant matching degree of the historical applicant under the candidate resume screening rule combination.

6. The method according to claim 1, wherein The method further includes: If the combined rule score does not meet the rule generation condition, adjust the feature weights in the resume screening rule combination to be evaluated, and re-determine the combined rule score of the adjusted resume screening rule combination to be evaluated until the adjusted combined rule score or the iteration count of the combined rule adjustment meets the combined adjustment end condition.

7. An optimization device for resume screening rules, characterized in that, Comprising: A rule combination module, configured to combine the resume screening rules in the to-be-optimized screening rule set for a target position to obtain at least one candidate resume screening rule combination for the target position; A first matching degree determination module, configured to determine at least one candidate applicant matching degree of the historical applicant according to the historical applicant features of the historical applicant and at least one candidate resume screening rule combination; wherein the candidate applicant matching degree is used to characterize the matching degree between the historical applicant and the candidate resume screening rule combination; A second matching degree determination module, configured to select a resume screening rule combination to be evaluated from the at least one candidate resume screening rule combination according to the at least one candidate applicant matching degree, and determine the candidate applicant matching degree corresponding to the resume screening rule combination to be evaluated; A score determination module, configured to determine the combined rule score of the resume screening rule combination to be evaluated according to the historical applicant features, the resume screening rule combination to be evaluated, and the candidate applicant matching degree; A rule combination determination module, configured to, if the combined rule score meets the rule generation condition, determine the resume screening rule combination to be evaluated as the target resume screening rule combination.

8. An electronic device, characterized in that, Comprising: One or more processors; A memory, configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the optimization method for resume screening rules according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the optimization method for resume screening rules according to any one of claims 1-6.

10. A computer program product, comprising a computer program, where the computer program implements the optimization method for resume screening rules according to any one of claims 1-6 when executed by a processor.

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