CN113610536A - User strategy distribution method and device for transaction rejection user and electronic equipment - Google Patents
User strategy distribution method and device for transaction rejection user and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to a user policy allocation method, a device, an electronic device and a computer readable medium for a transaction rejection user. The method comprises the following steps: acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; and distributing a user strategy for the transaction rejection user through the promotion score and the user information. According to the method and the system, on the premise of ensuring resource safety, resource service can be provided for the user as much as possible, the user satisfaction degree is improved, the resource utilization efficiency is improved, the labor cost is reduced, and the utilization rate of the server is improved.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a user policy allocation method and apparatus for a transaction rejection user, an electronic device, and a computer-readable medium.
Background
In internet-based application technology, it is often necessary to exchange resources before different parties. In each process of allocating resources, the credit worthiness of the user is often required to be evaluated. In the field of financial resources, for example, a financial market subject applies a certain method to prevent risks from occurring or avoiding risks in a compliance manner on the basis of relevant analysis. The credit worthiness assessment of the user is carried out substantially according to indexes which can affect the resource return performance of the user, a strategy and a rule for assessing the user are set, whether the user meets preset indexes or not is assessed to estimate the resource return performance of the user, and then how to grant credit worthiness to the user is selected, if the user passes the assessment of the strategy, the user is granted with corresponding credit, but if the user does not pass the assessment, the user is marked as a user refusing credit granting, the user is refused to grant the credit, so that the capital operation risk is reduced, the benefit is improved, and therefore when the user applies for resources again, the user can be directly refused, and the flow is shortened.
However, since the current financial prevention policies are all advance prevention policies, some users who can return resources on time still exist among users who are rejected under the policies, namely transaction rejection users. There are data showing that generally, the transaction throughput rate is only 75%, and about 25% of the trusted users are rejected at the transaction side, which is a loss for both the customer and the service company. Based on the actual rate of default, 95% of the 25% rejected customers still have "good" customers rejected. Therefore, it is necessary to salvage back the transaction-declined user.
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 policy allocation method, device, electronic device and computer readable medium for transaction refusing users, which can provide resource services to users as much as possible on the premise of ensuring resource security, improve user satisfaction, improve resource utilization efficiency, reduce labor cost and improve server utilization rate.
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 user policy allocation method for transaction rejection users is provided, the method including: acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; and distributing a user strategy for the transaction rejection user through the promotion score and the user information.
Optionally, before inputting the user information into the salvage model, the method includes: and inputting the user information into a fishing model when the remote information and the rejection reason of the transaction rejection user meet a preset strategy.
Optionally, inputting the user information into a salvage model, and generating a salvage score, including: generating a first user characteristic through the user information; inputting the first user characteristic into the salvage model; the salvage model calculates the first user characteristics to generate a plurality of leaf node values; determining the salvage score based on the plurality of leaf node values.
Optionally, inputting the user information into a lifting model, and generating a lifting score includes: generating a second user characteristic through the user information; inputting the second user characteristic into the lifting model; the lifting model calculates the first user characteristics to generate a differential response value; determining the boost score based on the differential response value.
Optionally, assigning a user policy to the transaction rejection user through the promotion score and the user information includes: determining a user characteristic parameter based on the user information; and comparing the promotion score, the user characteristic parameters and a preset criterion to determine a user strategy.
Optionally, comparing the promotion score, the user characteristic parameter and a preset criterion to determine a user policy, includes: when the promotion score is smaller than a threshold value and the behavior information does not contain preset time, allocating resource limit for the user; and when the promotion score is greater than or equal to a threshold value, the behavior information does not contain preset time, and the basic information does not contain preset staging, allocating resource quota for the user.
Optionally, after allocating a user policy to the transaction rejection user through the promotion score and the user information, the method further includes: monitoring behavior information of a plurality of transaction refusing users in real time; calculating the promotion rate of the transaction refusing users according to the behavior information of the transaction refusing users; updating the preset criterion based on the lifting rate.
Optionally, the method further comprises: extracting one historical user from a plurality of historical users based on an undersampling mode; training a classification model based on the historical user to generate an initial model, the initial model comprising a plurality of weak classification submodels and their corresponding weights; generating sample data based on the error of the initial model and the plurality of historical users; and training the classification model again based on the sample data until the classification model meets preset conditions to generate the salvage model.
Optionally, the method further comprises: dividing the plurality of historical users into an experimental group and a control group; allocating special shared resource information to the experiment group in the observation time period to generate experiment group behavior information; not distributing special shared resource information to the comparison group in the observation time period to generate comparison group behavior information; training a classification model based on the plurality of historical users, the experimental group behavior information, and the control group behavior information to generate the lifting model.
According to an aspect of the present disclosure, there is provided a user policy assigning apparatus for a transaction-rejecting user, the apparatus including: the information module is used for acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information; the salvage module is used for inputting the user information into a salvage model to generate a salvage score, and the salvage model is generated based on a plurality of undersampled historical user information and a classification model; the lifting module is used for inputting the user information into a lifting model to generate a lifting score when the salvage score is larger than a threshold value, and the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; and the strategy module is used for distributing a user strategy for the transaction refusing user through the promotion score and the user information.
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 strategy distribution method, device, electronic equipment and computer readable medium of the transaction refusing user, user information of the transaction refusing user is obtained, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; by the mode of improving the scores and distributing the user strategies for the transaction refusing users by the user information, resource services can be provided for the users as much as possible on the premise of ensuring resource safety, user satisfaction is improved, resource utilization efficiency is improved, labor cost is reduced, and server utilization rate is improved.
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.
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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 policy assignment method and apparatus for a transaction declined user according to an example embodiment.
FIG. 2 is a flow diagram illustrating a user policy assignment method for transacting a declined user according to an example embodiment.
Fig. 3 is a flow diagram illustrating a user policy assignment method for transacting a declined user according to another exemplary embodiment.
Fig. 4 is a flowchart illustrating a user policy assignment method of transacting a declined user according to another exemplary embodiment.
Fig. 5 is a block diagram illustrating a user policy assignment device transacting a declined user according to an example embodiment.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 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 this disclosure, resources refer to any substance, information, time that may be utilized, 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 present disclosure is how to use information interaction technology between the server and the client to make the process of allocation of user policies more automated, efficient and reduce human costs. Thus, in essence, the present disclosure can be applied to the allocation of various user policies, which can be used to allocate resources, resource occupation time, and specific resource information, and more specifically, the resources can include physical goods, water, electricity, and meaningful materials. However, for convenience, the implementation of user policy allocation is described in the present disclosure by taking financial data resources as an example, but those skilled in the art will appreciate that the present disclosure may also be applied to user policy allocation related to other resources.
The user policy allocation of the transaction refusing user (for convenience of description, the method provided by the embodiment of the present application may be abbreviated) provided by the embodiment of the present disclosure may be applied to any application field of a plurality of application fields such as investment, bank, insurance, securities, and e-commerce. In various application domains, the application scenarios involved may include, but are not limited to, login, registration, pre-loan, mid-loan, post-loan, holiday activities, or promotional activities, among others. The method provided by the embodiment of the application can be applied to the generation of the risk decision rule of any business type of any application scenario.
Here, the user policy is a basis for specifically selecting which risk control policy in the risk decision process. Different user policies can be constructed for different application scenarios, user data associated with different services, and the like. Here, the business may be various businesses provided to the user in a plurality of application fields such as investment, bank, insurance, securities, and e-commerce, for example, insurance, loan, and the like. Correspondingly, taking application as an example, the application scenario corresponding to the service may include, but is not limited to, account registration, account login, application for application, approval for application, generation and maintenance of policy, and the like. The application scenarios are only examples, but not exhaustive, and may be determined according to actual application scenarios, which are not limited herein. The user data includes, but is not limited to, service account information of the user, page operation data of the user, service access duration of the user, service access frequency of the user, terminal device identification information of the user, and region information where the user is located, and may be specifically determined according to an actual application scenario, and is not limited herein.
Fig. 1 is a system block diagram illustrating a user policy assignment method and apparatus for a transaction declined user according to an example 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 backend management server may analyze the received user data, and feed back the processing result (e.g., the user policy) to the administrator of the financial services website and/or the terminal device 101, 102, 103.
The terminal device 101, 102, 103 or the server 105 may, for example, obtain user information of the transaction rejection user, the user information including basic information and behavior information, and remote information; the terminal device 101, 102, 103 or the server 105 may, for example, input the user information into a fish-back model that is generated based on the undersampled plurality of historical user information and the classification model training, generating a fish-back score; the terminal device 101, 102, 103 or the server 105 may, for example, input the user information into a promotion model when the salvage score is greater than a threshold value, and generate a promotion score, where the promotion model is generated by training a classification model with historical user information and its corresponding specific resource information; the terminal device 101, 102, 103 or the server 105 may assign a user policy to the transaction declined user, for example, by the promotion score, the user information.
The server 105 may be a server of one entity, and may also be composed of a plurality of servers, for example, it should be noted that the user policy allocation method for the transaction declined user provided by the embodiment of the present disclosure may be executed by the server 105 or the terminal devices 101, 102, 103, and accordingly, the user policy allocation apparatus for the transaction declined user may be disposed in the server 105 or the terminal devices 101, 102, 103. 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 flow diagram illustrating a user policy assignment method for transacting a declined user according to an example embodiment. The user policy assignment method 20 of the transaction declined user includes at least steps S202 to S208.
As shown in fig. 2, in S202, user information of the transaction rejection user is obtained, where the user information includes basic information, behavior information, and remote information. In the embodiment of the present disclosure, the user may be an individual user or an enterprise user, and the allocation of the resource amount may be adjustment of a financial resource amount, or allocation of an electric power resource and a hydraulic resource. The user information may include basic information, such as service account information, terminal device identification information of the user, region information where the user is located, and the like; the user information may also include behavior information, which may be, for example, page operation data of the user, service access duration of the user, service access frequency of the user, and the like, and specific content of the user information may be determined according to an actual application scenario, which is not limited herein. More specifically, the user information of the current user can be obtained in a webpage point burying mode based on user authorization. The remote information can be user data of the user on other transaction platforms or other business departments.
More specifically, behavior information of a user on a website can be acquired through a Fiddler tool, the Fiddler tool works in a web proxy server mode, a client side firstly sends out request data, the Fiddler proxy server intercepts a data packet, and the proxy server impersonates the client side to send data to a server; similarly, the server returns the response data, and the proxy server intercepts the data and returns the intercepted data to the client. And the Fiddler can acquire the related browsing data of residence time, residence page, click operation and the like of the user network browsing.
In S204, the user information is input into a salvage model, which is generated based on a plurality of undersampled historical user information and a classification model training.
In one embodiment, the user information may be entered into a salvage model, for example, when the remote information and the rejection reason for the transaction rejecting user satisfy a preset policy. For example, a user may attempt to salvage back when his/her behavioral information is normal in other business departments or other platforms and the reason for the rejection of the transaction is a multi-platform lending user.
In one embodiment, a first user characteristic may be generated, for example, from the user information; inputting the first user characteristic into the salvage model; the salvage model calculates the first user characteristics to generate a plurality of leaf node values; determining the salvage score based on the plurality of leaf node values.
The user information can be subjected to data cleaning and data fusion so as to be converted into first user characteristics, and more specifically, variable loss rate analysis and processing and abnormal value processing can be performed on the user information; and the user information discretized by continuous variables can be subjected to WOE conversion, discrete variable WOE conversion, text variable processing, text variable word2vec processing and the like.
Among them, WOE is "Weight of Evidence", i.e., Evidence Weight. WOE is a form of encoding of the original features. To WOE encode a feature, this variable needs to be first grouped. Word2vec, a group of correlation models used to generate Word vectors. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic word text. The word2vec model may be used to map each word to a vector, which may be used to represent word-to-word relationships.
In S206, when the salvage score is greater than the threshold, the user information is input into a promotion model to generate a promotion score, and the promotion model is generated by training a classification model through historical user information and its corresponding special resource information. The boost ratio represents a boost ratio of the resource used by the target user after allocating the dedicated resource information to the target user and before allocating the dedicated resource information.
In one embodiment, a second user characteristic may be generated, for example, from the user information; inputting the second user characteristic into the lifting model; the lifting model calculates the first user characteristics to generate a differential response value; determining the boost score based on the differential response value. Data cleansing and data fusion may be performed on the user information to translate the user information into a second user characteristic.
In S208, a user policy is assigned to the transaction declined user according to the promotion score and the user information. User characteristic parameters may be determined, for example, based on the user information; and comparing the promotion score, the user characteristic parameters and a preset criterion to determine a user strategy.
Based on the observation of the historical users, the risk that the credited client moves on the current day of credit for more than 2 times of the current day, and the risk that the client selects the default repayment period number on the current day of credit for 1.5 times of the risk that the client selects the non-default payment period number, under the condition that the refusing users also have the same data expression, the client with higher quality in the transaction refusing users can try to be released
In one embodiment, for example, when the promotion score is smaller than a threshold and the behavior information does not include a preset time, allocating a resource amount to the user; for example, when the user promotion score is smaller than the threshold value and the current day of the credit is not moved, it is determined that the user can be fished back, and a user policy is allocated to the user, wherein the user policy may further include the special shared resource limit.
In one embodiment, for example, when the promotion score is greater than or equal to a threshold, the behavior information does not include a preset time, and the basic information does not include a preset staging, a resource amount is allocated to the user. For example, when the user promotion score is greater than or equal to the threshold, and the credit is not moved on the day, and the default repayment period number is not selected, it is determined that the user can be fished back, and a user policy is allocated to the user, wherein the user policy may further include an specially-shared resource amount.
In one embodiment, after assigning a user policy to the transaction declined user through the promotion score and the user information, the method may further include: monitoring behavior information of a plurality of transaction refusing users in real time; calculating the promotion rate of the transaction refusing users according to the behavior information of the transaction refusing users; updating the preset criterion based on the lifting rate. And updating the dynamic payment rate under the real transaction scene in real time based on the real-time performance of the salvage users. And calculating the dynamic support rate when the ordinary user is not allocated with the specially-shared resource limit, generating the promotion score of the salvaged user, and further updating the user strategy and the preset criterion in real time.
According to the user strategy distribution method of the transaction refusing user, user information of the transaction refusing user is obtained, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; by the mode of improving the scores and distributing the user strategies for the transaction refusing users by the user information, resource services can be provided for the users as much as possible on the premise of ensuring resource safety, user satisfaction is improved, resource utilization efficiency is improved, labor cost is reduced, and server utilization rate is improved.
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 flow diagram illustrating a user policy assignment method for transacting a declined user according to another exemplary embodiment. The flow 30 shown in fig. 3 is a detailed description of "generating a bailing model".
Fewer default samples are available in resource borrowing transactions, and a serious category imbalance problem exists. In this case, the selection of the positive sample is easier and the negative sample is harder to obtain. At the time of the financial risk analysis of the user, users who are not overdue can be used as positive samples, and users who have violated the machine learning model can be used as negative samples, but the number of the negative samples is very small for the positive samples, and the samples generated in this case are unbalanced samples. The risk model obtained based on the undersampling mode can solve the overfitting problem caused by simple oversampling or undersampling aiming at unbalanced samples when a machine model is trained, and an accurate calculation model is obtained, so that the efficiency of user resource limit distribution is improved, and the calculation pressure of a server is reduced.
As shown in fig. 3, in S302, one historical user is extracted from a plurality of historical users based on an undersampling manner. A number of majority samples are extracted and a minority class is combined to form a training data set S. Obtaining the weight of the sample in S, and normalizing the weight; using the training data set S, a historical user is first extracted from the training data set S.
In S304, a classification model is trained based on the historical users to generate an initial model, which includes a plurality of weak classification submodels and their corresponding weights. The historical user trains the weak classifier h1(t) (initial model) according to the weight SD, the output of h1(t) is the probability of judging into two classes, and when the final judgment is carried out, the class is judged to be the class with high probability.
In S306, sample data is generated based on the error of the initial model and the plurality of historical users. Inputting all samples into h1(t), wherein the h1(t) judges the ith sample to be the actual class of the sample with the probability p1(i) and judges the ith sample to be the opposite class of the actual class of the sample with the probability p2 (i); calculating an error e; the weight of the sample S is updated according to the error and normalized.
In S308, the classification model is trained again based on the sample data until a preset condition is met to generate the salvage model. Training again according to the updated sample to generate h2(t) (intermediate model); and updating the sample according to h2(t) again, training until the error of the model is smaller than the threshold value, and outputting the user quota allocation model.
Fig. 4 is a flowchart illustrating a user policy assignment method of transacting a declined user according to another exemplary embodiment. The flow 40 shown in FIG. 4 is a detailed description of "generating a lifting model".
Different preferential information has different motivation effects on different users, the same preferential information is very useful preferential information for the user A, but is not effective for the user B, the preferential information is obtained for the user A, then the resource borrowing behavior can be actively carried out, and the resource borrowing behavior cannot be generated for the user B. How to issue the preferential information for the user improves the possibility that the model wants to calculate that the user performs action and expenditure after issuing the preferential information.
As shown in fig. 4, in S402, the plurality of historical users are divided into an experimental group and a control group. Acquiring a plurality of stock users; and screening out the plurality of historical users from the plurality of inventory users based on a preset condition. The preset condition may be that there is already a resource borrowing record and the resource record is good, the preset condition may also be that users in a certain age group, and so on. Through the preset conditions, the long-term stable client can be extracted for follow-up tracking of the behavior information of the user.
In S404, allocating shared resource information to the experimental group in the observation period, and generating experimental group behavior information. And observing whether the users have dynamic behavior or not, and recording.
In S406, no specific resource information is allocated to the comparison group in the observation period, and comparison group behavior information is generated. And observing whether the users have dynamic behavior or not, and recording.
In S408, a classification model is trained based on the plurality of historical users, the experimental group behavior information, and the control group behavior information to generate the lifting model.
In the observation time, if the historical user in the set has resource borrowing behavior, a label is set for the user, the label is associated with the specially-shared resource information, and the label used by the user can be 'specially-shared resource information is effective' during subsequent machine learning. In observation time, if the historical user in the set has no resource borrowing behavior all the time, setting a label for the user, wherein the label is associated with the specially-shared resource information, and when the user learns in the subsequent machine, the label used by the user can be 'specially-shared resource information is invalid'
Training a classification model according to the labeled historical users to generate the lifting model.
Specifically, in the model training as shown in fig. 3 and 4, an intermediate model may be constructed for each sample set, and a historical user in the sample data may be input into the intermediate model, to obtain a predicted tag, comparing the predicted tag with a corresponding real tag, judging whether the predicted tag is consistent with the real tag, counting the number of the predicted tags consistent with the real tag, and calculating the ratio of the number of the predicted labels consistent with the real labels to the number of all the predicted labels, if the ratio is larger than or equal to a preset ratio, the intermediate model converges to obtain a trained user quota allocation model, if the percentage is less than the preset percentage, and adjusting parameters in the adjusting model, and predicting the prediction label of each object again through the adjusted intermediate model until the ratio is greater than or equal to a preset ratio. The method for adjusting the parameters in the intermediate model may be performed by using a random gradient descent algorithm, a gradient descent algorithm, or a normal equation. If the times of adjusting the parameters of the intermediate model exceed the preset times, the machine learning model used for constructing the intermediate model can be replaced, so that the model training efficiency is improved.
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. 5 is a block diagram illustrating a user policy assignment device transacting a declined user according to an example embodiment. As shown in fig. 5, the user policy assigning apparatus 50 for a transaction rejecting user includes: an information module 502, a salvage module 504, a promotion module 506, and a policy module 508.
The information module 502 is used for acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information;
the salvage module 504 is configured to input the user information into a salvage model, and generate a salvage score, where the salvage model is generated based on a plurality of undersampled historical user information and a classification model training;
the hoisting module 506 is configured to input the user information into a hoisting model to generate a hoisting score when the salvage score is greater than a threshold value, where the hoisting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information;
the policy module 508 is configured to assign a user policy to the transaction declined user according to the promotion score and the user information.
According to the user strategy distribution device of the transaction refusing user, user information of the transaction refusing user is obtained, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; by the mode of improving the scores and distributing the user strategies for the transaction refusing users by the user information, resource services can be provided for the users as much as possible on the premise of ensuring resource safety, user satisfaction is improved, resource utilization efficiency is improved, labor cost is reduced, and server utilization rate is improved.
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 600 according to this embodiment of the disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs steps in accordance with various exemplary embodiments of the present disclosure in the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 2, 3, 4.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 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 600 may also communicate with one or more external devices 600' (e.g., keyboard, pointing device, bluetooth device, etc.), such that a user can communicate with devices with which the electronic device 600 interacts, and/or any device (e.g., router, modem, etc.) with which the electronic device 600 can communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 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 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, 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. 7, 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: acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information; inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on a plurality of undersampled historical user information and a classification model; when the salvage score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information; and distributing a user strategy for the transaction rejection user through the promotion score and the user information.
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 (12)
1. A user policy assignment method for a transaction-rejecting user, comprising:
acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information;
inputting the user information into a fishing-back model to generate a fishing-back score, wherein the fishing-back model is generated based on the user information of a plurality of undersampled historical users and the training of a classification model;
when the salvaging score is larger than a threshold value, inputting the user information into a lifting model to generate a lifting score, wherein the lifting model is generated by training a classification model through the user information and the specially-shared resource information of the historical user;
and distributing a user strategy for the transaction rejection user through the promotion score and the user information.
2. The method of claim 1, wherein prior to entering the user information into a salvage model, comprising:
and inputting the user information into a fishing model when the remote information and the rejection reason of the transaction rejection user meet a preset strategy.
3. The method of claim 1, wherein entering the user information into a salvage model, generating a salvage score, comprises:
generating a first user characteristic through the user information;
inputting the first user characteristic into the salvage model;
the salvage model calculates the first user characteristics to generate a plurality of leaf node values;
determining the salvage score based on the plurality of leaf node values.
4. The method of claim 1, wherein inputting the user information into a promotion model to generate a promotion score comprises:
generating a second user characteristic through the user information;
inputting the second user characteristic into the lifting model;
the lifting model calculates the second user characteristic to generate a differential response value;
determining the boost score based on the differential response value.
5. The method of claim 1, wherein assigning a user policy to the transaction declining user via the promotion score and the user information comprises:
determining a user characteristic parameter based on the user information;
and comparing the promotion score, the user characteristic parameters and a preset criterion to determine a user strategy.
6. The method of claim 5, wherein comparing the boost score, the user characteristic parameter, and a preset criterion to determine a user policy comprises:
when the promotion score is smaller than a threshold value and the behavior information does not contain preset time, allocating resource limit for the user;
and when the promotion score is greater than or equal to a threshold value, the behavior information does not contain preset time, and the basic information does not contain preset staging, allocating resource quota for the user.
7. The method of claim 5, wherein after assigning a user policy to the transaction declining user via the promotion score and the user information, further comprising:
monitoring behavior information of a plurality of transaction refusing users in real time;
calculating the promotion rate of the transaction refusing users according to the behavior information of the transaction refusing users;
updating the preset criterion based on the lifting rate.
8. The method of claim 1, further comprising:
extracting one historical user from a plurality of historical users based on an undersampling mode;
training a classification model based on the historical user to generate an initial model, the initial model comprising a plurality of weak classification submodels and their corresponding weights;
generating sample data based on the error of the initial model and the plurality of historical users;
and training the classification model again based on the sample data until the classification model meets preset conditions to generate the salvage model.
9. The method of claim 1, further comprising:
dividing the plurality of historical users into an experimental group and a control group;
allocating special shared resource information to the experiment group in an observation time period to generate experiment group behavior information;
not allocating special shared resource information to the comparison group in the observation time period to obtain behavior information of the comparison group;
training a classification model based on the plurality of historical users, the experimental group behavior information, and the control group behavior information to generate the lifting model.
10. A user policy assignment device for a transaction-rejecting user, comprising:
the information module is used for acquiring user information of a transaction refusing user, wherein the user information comprises basic information, behavior information and remote information;
the salvage module is used for inputting the user information into a salvage model to generate a salvage score, and the salvage model is generated based on a plurality of undersampled historical user information and a classification model;
the lifting module is used for inputting the user information into a lifting model to generate a lifting score when the salvage score is larger than a threshold value, and the lifting model is generated by training a classification model through historical user information and special shared resource information corresponding to the historical user information;
and the strategy module is used for distributing a user strategy for the transaction refusing user through the promotion score and the user information.
11. 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 1-9.
12. 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 1-9.
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