CN112950352A - User screening strategy generation method and device and electronic equipment - Google Patents
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Abstract
The disclosure relates to a user screening strategy generation method and device, electronic equipment and a computer readable medium. The method comprises the following steps: determining a user screening target, wherein the user screening target comprises a project category and screening parameters; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation to generate a plurality of user screening strategies; and extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard. According to the user screening strategy generation method, the user screening strategy generation device, the electronic equipment and the computer readable medium, the user screening strategy can be quickly and accurately generated according to the target set by the administrator, the labor cost is reduced, the strategy screening accuracy is improved, the strategy online time is shortened, the risk users can be timely identified, and the resource safety is ensured.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a user screening policy generation method and apparatus, an electronic device, and a computer readable medium.
Background
With the development of economy, in order to meet the development requirement of the financial service institution, a personal user or an enterprise user often performs borrowing activities by the financial service institution, and the borrowing activities of the user are likely to bring risks to the financial service institution. Before the repayment deadline expires, the financial business condition of the borrower (credit user) is greatly changed adversely, which may affect the performance capability of the borrower, so that the risk of being open to debt, bad account and the like occurs.
In the user policy making, the prior art is to make a user policy by analyzing through expert experience knowledge and combining with personal behavior data based on historical user basic information. If the guidance suggestions of the user strategy are updated, for example, the screening proportion of the user is increased or the rejection rate of the user is increased, after the guidance suggestions are changed, the strategy making personnel still need to analyze again in the analysis mode, and much time is wasted. Moreover, the user strategy with manual participation inevitably introduces errors in the process of making the user strategy, and brings safety risk to the practical application of the user strategy.
Therefore, a new user filtering policy generation method, device, electronic device and computer readable medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present disclosure provides a user screening policy generation method, device, electronic device, and computer readable medium, which can quickly and accurately generate a user screening policy according to a target set by an administrator, reduce labor cost, improve policy screening accuracy, accelerate policy online time, and timely identify a risk user, thereby ensuring resource security.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a method for generating a user screening policy is provided, where the method includes: determining a user screening target, wherein the user screening target comprises a project category and screening parameters; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation; generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy; screening paths from a plurality of root nodes to leaf nodes by using the screening parameters to generate a plurality of user screening strategies; and extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard.
Optionally, the method further comprises: receiving a resource limit application from a user; judging the resource limit application based on the target user screening strategy; and generating a return notice for the resource limit application based on the judgment result, wherein the return notice comprises an approval application or a rejection application.
Optionally, the determining the resource limit application based on the target user screening policy includes: acquiring user information and application items from the credit granting application; and inputting the user information and the application items into a target user screening strategy for judgment.
Optionally, extracting a plurality of target users from the historical users based on the item category to generate a user sample set, including: determining a judgment criterion of a sample label based on the item category; extracting a plurality of target users from historical users based on the judgment criterion; determining a sample label for the plurality of target users generates the user sample set.
Optionally, the inputting the user sample set and the screening parameters into a policy model for calculation to generate a plurality of user screening policies, including: inputting the user sample set into a random forest model for calculation; screening the random forest model calculation results by using the screening parameters; and generating a plurality of user screening strategies according to the screening results.
Optionally, inputting the user sample set into a random forest model for calculation, including: inputting the user sample set into a random forest model; calculating the random forest model based on a sample label; and generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy.
Optionally, the screening of the random forest model calculation results by using the screening parameters includes: and screening the paths from the root nodes to the leaf nodes by using the screening parameters.
Optionally, generating a plurality of user screening policies according to the screening result includes: and generating the plurality of user screening strategies through the paths meeting the screening parameters.
Optionally, extracting a target user screening policy from the plurality of user screening policies based on a preset criterion includes: calculating the number of the same rules in the plurality of user screening strategies; and sequencing the plurality of user screening strategies according to the number of the same rules to extract the target user screening strategy.
Optionally, extracting a target user screening policy from the plurality of user screening policies based on a preset criterion includes: calculating the number of rules contained in each user screening strategy in the plurality of user screening strategies; determining the complexity of the user screening strategy according to the quantity; sorting the plurality of user screening policies from small to large based on the complexity to extract the target user screening policy.
According to an aspect of the present disclosure, a user screening policy generation apparatus is provided, the apparatus including: the target module is used for determining a user screening target, and the user screening target comprises a project category and screening parameters; a sample module for extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; the calculation module is used for inputting the user sample set and the screening parameters into a policy model for calculation and generating a user screening policy based on a path from a root node to a leaf node in the random forest model, wherein each section of path from the root node to the leaf node is a rule in the user screening policy; screening paths from a plurality of root nodes to leaf nodes by using the screening parameters to generate a plurality of user screening strategies; and the strategy module is used for extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard.
Optionally, the method further comprises: the application module is used for receiving a resource limit application from a user; the judging module is used for judging the resource limit application based on the target user screening strategy; and the notification module is used for generating a return notification for the resource limit application based on the judgment result, wherein the return notification comprises an approval application or a rejection application.
Optionally, the determining module is further configured to obtain user information and an application item from the credit granting application; and inputting the user information and the application items into a target user screening strategy for judgment.
Optionally, the sample module comprises: a criterion unit, configured to determine a judgment criterion of a sample tag based on the item category; the user unit is used for extracting a plurality of target users from historical users based on the judgment criterion; a set unit, configured to determine sample labels for the multiple target users and generate the user sample set.
Optionally, the calculation module includes: the calculating unit is used for inputting the user sample set into a random forest model for calculation; the screening unit is used for screening the calculation result of the random forest model by using the screening parameters; and the result unit is used for generating a plurality of user screening strategies according to the screening results.
Optionally, the computing unit is further configured to input the user sample set into a random forest model; calculating the random forest model based on a sample label; and generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy.
Optionally, the screening unit is further configured to screen paths from the plurality of root nodes to the leaf nodes by using the screening parameter.
Optionally, the result unit is further configured to generate the plurality of user screening policies through a path that satisfies the screening parameter.
Optionally, the policy module includes: the rule quantity unit is used for calculating the quantity of the same rules in the plurality of user screening strategies; and sequencing the plurality of user screening strategies according to the number of the same rules to extract the target user screening strategy.
Optionally, the policy module includes: a complexity unit, configured to calculate the number of rules included in each of the plurality of user screening policies; determining the complexity of the user screening strategy according to the quantity; sorting the plurality of user screening policies from small to large based on the complexity to extract the target user screening policy.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the user screening strategy generation method, the user screening strategy generation device, the electronic equipment and the computer readable medium, a user screening target is determined, wherein the user screening target comprises an item category and screening parameters; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation to generate a plurality of user screening strategies; the mode of extracting the target user screening strategy from the user screening strategies based on the preset standard can quickly and accurately generate the user screening strategy according to the target set by the administrator, so that the labor cost is reduced, the strategy screening accuracy is improved, the strategy online time is shortened, the risk users can be timely identified, and the resource safety is ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a user filtering policy generation method and apparatus according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating a user filtering policy generation method according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment.
Fig. 4 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment.
Fig. 5 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment.
Fig. 6 is a block diagram illustrating a user filtering policy generation apparatus according to an exemplary embodiment.
Fig. 7 is a block diagram illustrating a user filtering policy generating apparatus according to another exemplary embodiment.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 9 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
In the present invention, resources refer to any available substances, information, time, information resources including computing resources and various types of data resources. The data resources include various private data in various domains. The innovation of the invention is how to use the information interaction technology between the server and the client to make the resource allocation process more automatic, efficient and reduce the labor cost. Thus, the present invention can be applied to the distribution of various resources including physical goods, water, electricity, and meaningful data, essentially. However, for convenience, the resource allocation is described as being implemented by taking financial data resources as an example, but those skilled in the art will understand that the present invention can also be applied to allocation of other resources.
Fig. 1 is a system block diagram illustrating a user filtering policy generation method and apparatus according to an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports financial services websites browsed by the user using the terminal apparatuses 101, 102, and 103. The background management server may analyze the received resource quota application, and feed back the processing result (approval application or rejection application) to the administrator of the financial service website and/or the terminal device 101, 102, 103.
The server 105 may, for example, receive a resource quota application from a user; the server 105 may determine the resource quota application based on the target user screening policy, for example; the server 105 may generate a return notification for the resource quota application, for example, based on the determination result, where the return notification includes an approval application or a rejection application.
The server 105 may be a single entity server, or may be composed of a plurality of servers, for example, it should be noted that the user screening policy generating method provided by the embodiment of the present disclosure may be executed by the server 105, and accordingly, the user screening policy generating device may be disposed in the server 105. And the web page end provided for the user to browse the financial service platform is generally positioned in the terminal equipment 101, 102 and 103.
Fig. 2 is a flowchart illustrating a user filtering policy generation method according to an exemplary embodiment. The user filtering policy generating method 20 includes at least steps S202 to S208.
As shown in fig. 2, in S202, user filtering targets are determined, which include item categories and filtering parameters.
In an application scenario, when a user applies for a resource quota, before risk analysis of a machine learning model is not performed on the user, the user with a high default risk is screened out through a user policy, and then applications of the user are rejected.
In one particular embodiment, the default risk rate may select users greater than 3 times the average risk, and the sample coverage may be 3%. More specifically, assuming that the average risk rate of all users is 2%, users with a user risk greater than 6% need to be screened as target users, and the number of screened users is about 3% of the total number of users.
Further, the parameters may further include: coverage rate threshold, risk rate threshold, number of variable combination results, depth of tree model, rule complexity of generating rule, and the like.
In S204, a plurality of target users are extracted from the historical users based on the item categories to generate a user sample set. The method comprises the following steps: determining a judgment criterion of a sample label based on the item category; extracting a plurality of target users from historical users based on the judgment criterion; determining a sample label for the plurality of target users generates the user sample set.
When the screening target is the default risk, the target user can be a historical user with default records, and furthermore, overdue time can be used as a sample label. When the target is the risk of arrears, the target user can be the user who has the record of arrears, further, the sample label can be a positive label and a negative label, the positive label represents that the user arrears and then the user is returned in time within a short time (within 7 days), and the negative label represents that the user is the returned resource within a longer time.
In S206, the user sample set and the screening parameters are input into a policy model for calculation, and a plurality of user screening policies are generated. The method comprises the following steps: inputting the user sample set into a random forest model for calculation; screening the random forest model calculation results by using the screening parameters; and generating a plurality of user screening strategies according to the screening result.
In S208, a target user filtering policy is extracted from the plurality of user filtering policies based on a preset criterion. The screened user strategy can be evaluated from different angles, and the target user screening strategy is preferably selected for online use.
The filtering may be performed, for example, according to the difficulty level of the user filtering policy, and may also be performed, for example, according to the coverage accuracy of the user filtering policy, and so on.
According to the user screening strategy generation method disclosed by the invention, a user screening target is determined, wherein the user screening target comprises a project category and screening parameters; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation to generate a plurality of user screening strategies; the mode of extracting the target user screening strategy from the user screening strategies based on the preset standard can quickly and accurately generate the user screening strategy according to the target set by the administrator, so that the labor cost is reduced, the strategy screening accuracy is improved, the strategy online time is shortened, the risk users can be timely identified, and the resource safety is ensured.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment. The process 30 shown in fig. 3 is a supplementary description of the process shown in fig. 2.
As shown in FIG. 3, in S302, a resource quota application from a user is received. After the user registers and trusts, the user needs to apply for the resource limit before obtaining the resource limit.
In S304, the resource quota application is determined based on the target user screening policy. The method comprises the following steps: acquiring user information and application items from the credit granting application; and inputting the user information and the application items into a target user screening strategy for judgment.
In S306, a return notification is generated for the resource quota application based on the determination result, where the return notification includes an approval application or a rejection application. And rejecting or approving the application of the user according to the judgment result of the user screening strategy. Furthermore, after the application of the user is approved, a machine learning model of the user risk class can be called to further analyze the risk of the user so as to determine the resource limit of the user.
The user screening strategy and the user risk analysis model are combined, part of user resource limit application which does not meet the requirements can be rapidly eliminated, the calculation pressure of a machine learning model is reduced, and the user application speed is increased.
Fig. 4 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment. The flow 40 shown in fig. 4 is a detailed description of S206 "inputting the user sample set and the filtering parameters into a policy model for calculation to generate a plurality of user filtering policies" in the flow shown in fig. 2.
As shown in fig. 4, in S402, the user sample set is input into a random forest model for calculation. The method comprises the following steps: inputting the user sample set into a random forest model; calculating the random forest model based on a sample label; and generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy. A rule is formed by the path from the root node to the leaf node, and a rule set (which may be a single variable, 2 variable combinations, or a rule set with more than 3 variable combinations, for example) is generated.
In S404, the random forest model calculation results are screened by using the screening parameters. The method comprises the following steps: and screening the paths from the root nodes to the leaf nodes by using the screening parameters. The filtering parameter may be a rule complexity, and for example, a rule with a higher complexity may be deleted.
In S406, a plurality of user screening policies are generated according to the screening result. The method comprises the following steps: and generating the plurality of user screening strategies through the paths meeting the screening parameters.
In the prior art, only a few parameters of a user strategy can be combined in a manual mode to generate a user screening strategy, and the user screening strategy generated through a random forest decision tree can introduce more variables, so that more comprehensive analysis is performed.
Fig. 5 is a flowchart illustrating a user filtering policy generation method according to another exemplary embodiment. The flow 50 shown in fig. 5 is a detailed description of "extracting a target user filtering policy from the plurality of user filtering policies based on a preset criterion" in S208 in the flow shown in fig. 2.
As shown in fig. 5, in S502, the number of the same rules in the plurality of user filtering policies is calculated.
In S504, the plurality of user screening policies are ranked according to the number of the same rules. It is believed that if a rule is used the most, then the rule can use as many users as possible, enabling better screening of users. Each user policy contains a plurality of rules, and the number of the same rules is extracted. The user filtering policies may be ordered by the number of identical rules they contain.
In S506, the number of rules included in each of the plurality of user filtering policies is calculated.
In S508, the complexity of the user screening policy is determined according to the number. It is believed that the user filtering policy including the most rules has many corresponding operations and complex logic, which may cause other system problems. It can be considered that the simpler the user screening policy is, the more satisfactory it is.
In S510, the plurality of user screening policies are ranked based on the complexity from small to large.
In S512, the target user filtering policy is extracted according to the ranking.
In one embodiment, the number of repetitions per rule is n, the coverage and risk level is a multiple of the input criteria, p, the complexity of the rule, c,
p = (coverage threshold/(coverage threshold + risk threshold)/risk threshold/2;
r = n × p/c may represent an evaluation value of the user filtering policy, and the higher the value is, the more applicable the user filtering policy is to more users.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 6 is a block diagram illustrating a user filtering policy generation apparatus according to an exemplary embodiment. As shown in fig. 6, the user filtering policy generating device 60 includes: a goal module 602, a sample module 604, a calculation module 606, and a policy module 608.
The goal module 602 is configured to determine a user filtering goal, where the user filtering goal includes an item category and a filtering parameter;
a sample module 604 for extracting a plurality of target users from the historical users based on the item categories to generate a set of user samples; the sample module 604 includes: a criterion unit, configured to determine a judgment criterion of a sample tag based on the item category; the user unit is used for extracting a plurality of target users from historical users based on the judgment criterion; a set unit, configured to determine sample labels for the multiple target users and generate the user sample set.
The calculation module 606 is configured to input the user sample set and the screening parameters into a policy model for calculation, so as to generate a plurality of user screening policies. The calculation module 606 includes: the calculating unit is used for inputting the user sample set into a random forest model for calculation; the computing unit is further used for inputting the user sample set into a random forest model; calculating the random forest model based on a sample label; generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy; the screening unit is used for screening the calculation result of the random forest model by using the screening parameters; the screening unit is further configured to screen paths from the plurality of root nodes to the leaf nodes by using the screening parameters; and the result unit is used for generating a plurality of user screening strategies according to the screening results. The result unit is further configured to generate the plurality of user screening policies through paths that satisfy the screening parameters.
The policy module 608 is configured to extract a target user filtering policy from the plurality of user filtering policies based on a preset criterion. The policy module 608 includes: the rule quantity unit is used for calculating the quantity of the same rules in the plurality of user screening strategies; and sequencing the plurality of user screening strategies according to the number of the same rules to extract the target user screening strategy. A complexity unit, configured to calculate the number of rules included in each of the plurality of user screening policies; determining the complexity of the user screening strategy according to the quantity; sorting the plurality of user screening policies from small to large based on the complexity to extract the target user screening policy.
Fig. 7 is a block diagram illustrating a user filtering policy generating apparatus according to another exemplary embodiment. As shown in fig. 7, the user filtering policy generating device 70 includes: an application module 702, a determination module 704, and a notification module 706.
The application module 702 is used for receiving a resource quota application from a user;
the judging module 704 is used for judging the resource limit application based on the target user screening strategy; the judging module 704 is further configured to obtain user information and an application item from the credit granting application; and inputting the user information and the application items into a target user screening strategy for judgment.
The notification module 706 is configured to generate a return notification for the resource quota application based on the determination result, where the return notification includes an approval application or a rejection application.
According to the user screening strategy generation device disclosed by the invention, a user screening target is determined, wherein the user screening target comprises a project category and a screening parameter; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation to generate a plurality of user screening strategies; the mode of extracting the target user screening strategy from the user screening strategies based on the preset standard can quickly and accurately generate the user screening strategy according to the target set by the administrator, so that the labor cost is reduced, the strategy screening accuracy is improved, the strategy online time is shortened, the risk users can be timely identified, and the resource safety is ensured.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 800 according to this embodiment of the disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components (including the memory unit 820 and the processing unit 810), a display unit 840, and the like.
Wherein the storage unit stores program code that can be executed by the processing unit 810, such that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure in this specification. For example, the processing unit 810 may perform the steps as shown in fig. 2, 3, 4, 5.
The memory unit 820 may include readable media in the form of volatile memory units such as a random access memory unit (RAM) 8201 and/or a cache memory unit 8202, and may further include a read only memory unit (ROM) 8203.
The memory unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 800 may also communicate with one or more external devices 800' (e.g., keyboard, pointing device, bluetooth device, etc.) such that a user can communicate with devices with which the electronic device 800 interacts, and/or any devices (e.g., router, modem, etc.) with which the electronic device 800 can communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 860. The network adapter 860 may communicate with other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 9, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: determining a user screening target, wherein the user screening target comprises a project category and screening parameters; extracting a plurality of target users from the historical users based on the item categories to generate a user sample set; inputting the user sample set and the screening parameters into a strategy model for calculation to generate a plurality of user screening strategies; and extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A user screening strategy generation method is characterized by comprising the following steps:
determining a user screening target, wherein the user screening target comprises a project category and screening parameters;
extracting a plurality of target users from the historical users based on the item categories to generate a user sample set;
inputting the user sample set and the screening parameters into a strategy model for calculation;
generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy;
screening paths from a plurality of root nodes to leaf nodes by using the screening parameters to generate a plurality of user screening strategies;
and extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard.
2. The method of claim 1, further comprising:
receiving a resource limit application from a user;
judging the resource limit application based on the target user screening strategy;
and generating a return notice for the resource limit application based on the judgment result, wherein the return notice comprises an approval application or a rejection application.
3. The method of claim 2, wherein determining the resource quota application based on the target user screening policy comprises:
acquiring user information and application items from the credit granting application;
and inputting the user information and the application items into a target user screening strategy for judgment.
4. The method of claim 1, wherein extracting a plurality of target users among historical users based on the item categories to generate a user sample set comprises:
determining a judgment criterion of a sample label based on the item category;
extracting a plurality of target users from historical users based on the judgment criterion;
determining a sample label for the plurality of target users generates the user sample set.
5. The method of claim 1, wherein entering the set of user samples and the screening parameters into a policy model for computation to generate a plurality of user screening policies comprises:
inputting the user sample set into a random forest model for calculation;
screening the random forest model calculation results by using the screening parameters;
and generating a plurality of user screening strategies according to the screening results.
6. A method as claimed in claim 5, wherein entering the set of user samples into a random forest model for computation comprises:
inputting the user sample set into a random forest model;
calculating the random forest model based on a sample label;
and generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy.
7. A method as claimed in claim 6, wherein the screening of random forest model calculations using the screening parameters comprises:
and screening the paths from the root nodes to the leaf nodes by using the screening parameters.
8. A user filtering policy generation apparatus, comprising:
the target module is used for determining a user screening target, and the user screening target comprises a project category and screening parameters;
a sample module for extracting a plurality of target users from the historical users based on the item categories to generate a user sample set;
the calculation module is used for inputting the user sample set and the screening parameters into a strategy model for calculation; generating a user screening strategy based on a path from a root node to a leaf node in the random forest model, wherein each section of the path from the root node to the leaf node is a rule in the user screening strategy; screening paths from a plurality of root nodes to leaf nodes by using the screening parameters to generate a plurality of user screening strategies;
and the strategy module is used for extracting a target user screening strategy from the plurality of user screening strategies based on a preset standard.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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