CN114663127A - User screening method, service processing server and storage medium - Google Patents

User screening method, service processing server and storage medium Download PDF

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Publication number
CN114663127A
CN114663127A CN202210128798.2A CN202210128798A CN114663127A CN 114663127 A CN114663127 A CN 114663127A CN 202210128798 A CN202210128798 A CN 202210128798A CN 114663127 A CN114663127 A CN 114663127A
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user
service
target
probability value
screened
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CN202210128798.2A
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孙月月
陈辉亮
黄明星
沈鹏
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Beijing Absolute Health Ltd
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Beijing Absolute Health Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Abstract

The embodiment of the application provides a user screening method, a service processing server and a storage medium, wherein in the user screening method, a target probability value is calculated by predicting a first probability value of a user to be screened for executing service content display operation aiming at a target service and a second probability value of executing service trigger operation aiming at the target service and then through a weighted average algorithm of the first probability value and the second probability value, and when the target probability value is within a threshold range, the user to be screened is determined to be the target user.

Description

User screening method, service processing server and storage medium
[ technical field ] A method for producing a semiconductor device
The embodiment of the application relates to the technical field of intelligent terminals, in particular to a user screening method, a service processing server and a storage medium.
[ background of the invention ]
With the development of internet insurance, the user size is rapidly increased, and the cost of marketing short messages is increased because a high-quality user with high insurance willingness cannot be determined among all users. In the prior art, in order to reduce marketing cost, operation needs to screen out high-quality users from a large number of users in advance according to manual experience, and write the high-quality users into a message system to achieve the touch of marketing short messages. However, because of limited manual computing capability, only a few features can be considered when users are screened, and optimal users cannot be screened from the whole situation, so that the screened users cannot reach an ideal high-quality level, and the operation cost cannot be reduced.
[ summary of the invention ]
The embodiment of the application provides a user screening method, a business processing server and a storage medium, so that when a high-quality user for marketing needs to be screened out, the specific conditions of the user in business triggering operation and business processing operation can be fully considered, a target user is screened out, the business prediction precision can be improved, and the manual analysis cost is reduced.
In a first aspect, an embodiment of the present application provides a user screening method, where the method includes: predicting a first probability value of a user to be screened for executing service content display operation aiming at a target service according to user characteristics of the user to be screened; predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service according to the user characteristics of the user to be screened; executing a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value; and when the target probability value is within a threshold value range, determining the user to be screened as a target user.
In the user screening method, the target probability value is calculated by predicting the first probability value of the user to be screened for executing the service content display operation aiming at the target service and the second probability value of the service triggering operation aiming at the target service, and then the weighting algorithm of the first probability value and the second probability value is used for determining the user data to be screened as the target user data when the target probability value is in the threshold range.
In one embodiment, the predicting the first probability value of the user to be screened performing the service content display operation for the target service includes: predicting a first probability value of the user to be screened for executing service content display operation aiming at the target service through a first model based on the user characteristics of the user to be screened; the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service; the predicting, according to the user characteristics of the user to be screened, a second probability value of the user to be screened for executing the service triggering operation for the target service includes: predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened; the second model is obtained by training according to a second training sample, and the second training sample includes the user characteristics of the sample user and the result of whether the sample user executes the service triggering operation for the target service.
In one embodiment, the method further comprises: sending a reminding message which can execute the service content display operation and/or the service trigger operation aiming at the target service to the target user, wherein the reminding message comprises a request instruction for requesting to execute the service content display operation and/or the service trigger operation; and determining whether the target user executes the result of the service content display operation and/or the service triggering operation aiming at the target service according to the instruction signal fed back aiming at the request instruction.
In one embodiment, the method further comprises: training the first model by taking the user characteristics of the target user and the result of whether the target user executes the business content display operation aiming at the target business as a new first training sample; and/or training the second model by taking the user characteristics of the target user and the result of whether the target user executes the service triggering operation aiming at the target service as a new second training sample.
In one embodiment, the user characteristics include at least one or a combination of the following: user attributes, user behavior characteristics, and user marketing result characteristics.
In a second aspect, an embodiment of the present application provides a service processing server, where the server includes: the first probability value prediction module is used for predicting a first probability value of the user to be screened for executing the service content display operation aiming at the target service according to the user characteristics of the user to be screened; the second probability value prediction module is used for predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service according to the user characteristics of the user to be screened; the target probability value obtaining module is used for executing a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value; and the target user determination module is used for determining the user to be screened as the target user when the target probability value is within a threshold range.
In one embodiment, the first probability value prediction module includes: the first model execution submodule is used for predicting a first probability value of the user to be screened for executing business content display operation aiming at the target business through a first model based on the user characteristics of the user to be screened; the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service; the second probability value prediction module comprises: the second model execution submodule is used for predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened; the second model is obtained by training according to a second training sample, and the second training sample comprises user characteristics of the sample user and a result of whether the sample user executes the service triggering operation for the target service.
In one embodiment, the server further includes: a reminding information sending module, configured to send reminding information that is capable of performing the service content display operation and/or the service trigger operation for the target service to the target user, where the reminding information includes a request instruction for requesting execution of the service content display operation and/or the service trigger operation; and the execution determining module is used for determining whether the target user executes the result of the service content display operation and/or the service triggering operation aiming at the target service according to the instruction signal fed back aiming at the request instruction.
In one embodiment, the server further includes: a first model repeated training module, configured to train the first model by using, as a new first training sample, the user characteristics of the target user and a result of whether the target user performs the service content display operation for the target service; and/or a second model repeated training module, configured to train the second model with the user characteristics of the target user and a result of whether the target user performs the service triggering operation for the target service as a new second training sample.
In a third aspect, an embodiment of the present application provides a service processing server, including: at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the user screening method.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer instructions are stored, and the instructions, when executed by a processor, implement the steps of the user screening method described above.
It should be understood that the second to fourth aspects of the embodiment of the present application are consistent with the technical solution of the first aspect of the embodiment of the present application, and beneficial effects obtained by the aspects and the corresponding possible implementation are similar, and are not described again.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present specification, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a user screening method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a user screening method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of a user screening method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a user screening method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a service processing server according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a service processing server according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a service processing server according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a service processing server according to an embodiment of the present application.
[ detailed description ] embodiments
For better understanding of the technical solutions in the present specification, the following detailed description of the embodiments of the present application is provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only a few embodiments of the present specification, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present specification.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the specification. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The user screening method provided by the embodiment of the application can be executed by a service processing server, and the service processing server can be one server or a plurality of servers, or a virtualization platform or a cloud computing service center. The service processing server is used for providing background service for the specific processing of the service information. Optionally, the service processing server undertakes primary computation work, and the electronic device terminal establishing communication connection with the service processing server undertakes secondary computation work; or, the business processing server undertakes the secondary calculation work, and the electronic equipment terminal which establishes communication connection with the business processing server undertakes the primary calculation work; or, the electronic device terminal and the service processing server adopt a distributed computing architecture for performing collaborative computing.
The electronic device terminal establishing communication connection with the service processing server may be a mobile terminal such as a mobile phone, a game console, a tablet Computer, an electronic book reader, smart glasses, an MP4(Moving Picture Experts Group Audio Layer IV, fourth generation digital Audio coding and lossy compression technology) player, a smart home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, or the terminal may be a Personal Computer (Personal Computer, PC), such as a laptop Computer and a desktop Computer.
The service information to be screened by the user is the electronic policy service information, which illustrates the technical problem to be actually solved by the technical scheme provided by the embodiment of the present application:
in the prior art, in order to reduce the insurance marketing cost, operation needs to screen out high-quality users from a large number of users according to manual experience in advance, and write the high-quality users into a message system to realize the reaching of marketing short messages. However, because of limited manual computing capability, only a few features can be considered when users are screened, and optimal users cannot be screened from the whole situation, so that the screened users cannot reach an ideal high-quality level, and the operation cost cannot be reduced.
Fig. 1 is a schematic flowchart of a user screening method provided in an embodiment of the present application, and is applied to the service processing server, as shown in the figure, the user screening method may include the following steps:
step S101, according to the user characteristics of the user to be screened, predicting a first probability value of the user to be screened for executing the service content display operation aiming at the target service.
Optionally, the service content display operation may be a feedback operation in which the service processing server sends the target service content to be displayed to the electronic device terminal after receiving the service content request signal, for example, after receiving the service content request signal sent by the electronic device terminal, the service processing server returns the target service content to be displayed, which is matched with the service content request signal, to the electronic device terminal, and at this time, the return operation may be defined as a service content display operation; the creating operation of the display window or the page for the target service content may also be performed by the electronic device terminal, for example, after receiving the target service content to be displayed, the electronic device terminal may display the target service content to be displayed in the display window or the page, and at this time, the creating operation of the display window or the page may be defined as a service content display operation; for example, the electronic device terminal displays the target service content to be displayed in the created display window or page, and within a period of time, the user does not perform a closing operation on the display window, and at this time, the display operation of the display window maintained for a certain period of time may be defined as the service content display operation. The specific setting of which operation is the service content display operation needs to be determined according to the actual operation conditions of those skilled in the art.
It should be noted that the user characteristics at least include one or a combination of the following: the method comprises the following steps of (1) user attributes, user behavior characteristics and user marketing result characteristics, wherein the user attributes can be age, gender, location and the like; the user behavior characteristics can be service content display operation, service trigger operation and the like; the user marketing result characteristics can be processing states of the user for the target service, such as purchase and unsubscribe.
Alternatively, the calculation of the first probability value may be performed by a machine learning algorithm, i.e. the above step S101 may include:
predicting a first probability value of the user to be screened for executing service content display operation aiming at a target service through a first model based on the user characteristics of the user to be screened; the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service.
Illustratively, the training process of the first model may include:
in step S1011, a first training sample is obtained. The first training sample comprises user characteristics of a sample user and a result of whether the sample user performs service content display operation aiming at the target service, and the user characteristics of the sample user and the result of whether the sample user performs the service content display operation aiming at the target service are respectively endowed with the same weight.
In step S1012, a weak classifier is trained. In a specific training process, if a certain sample point has been accurately classified, its weight is reduced in constructing the next training set; conversely, if a sample point is not classified accurately, its weight is increased. The sample set with updated weights is then used to train the next classifier, and the entire training process proceeds iteratively.
Step S1013, the weak classifiers obtained by training are combined into a strong classifier, and the strong classifier is the first model. After the training process of each weak classifier is finished, the weight of the weak classifier with small classification error rate is increased to play a larger decision role in the final classification function, and the weight of the weak classifier with large classification error rate is reduced to play a smaller decision role in the final classification function. That is, weak classifiers with low error rates take up more weight in the final classifier, and are otherwise smaller.
Step S102, predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service according to the user characteristics of the user to be screened.
Optionally, the service triggering operation may be a corresponding service change operation executed by the service processing server after receiving a service state change instruction triggered by the user, where the service state change instruction is, for example, an insurance service purchase instruction, an insurance service renewal instruction, an insurance service unsubscribe instruction, and the like.
It should be noted that the user characteristics at least include one or a combination of the following: the method comprises the following steps of (1) user attributes, user behavior characteristics and user marketing result characteristics, wherein the user attributes can be age, gender, location and the like; the user behavior characteristics can be service content display operation, service trigger operation and the like; the user marketing result feature may be a processing status of the user for the target business, such as purchase, unsubscribe, and the like.
Alternatively, the calculation of the second probability value may be performed by a machine learning algorithm, that is, the step S102 may include:
predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened; the second model is obtained by training according to a second training sample, and the second training sample includes the user characteristics of the sample user and the result of whether the sample user executes the service triggering operation for the target service.
It should be noted that the training process of the second model may be the same as the training process of the first model, and is not described herein again.
And S103, executing a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value.
The first probability value and the second probability value are respectively obtained, so that a means for flexibly adjusting the weighted values of the service content display operation and the service trigger operation by an operator can be provided, the accuracy of screening high-quality users is improved, and the screened users are closer to the judgment standard of the operator on the high-quality users.
Optionally, the weighted values of the first probability value and the second probability value are set according to the emphasis of the operator on the service content display operation and the service triggering operation, for example, for an emerging service, the weighted value for the service triggering operation may be relatively reduced; for the core service, the weight value for the service triggering operation may be relatively increased.
And step S104, when the target probability value is within a threshold value range, determining the user to be screened as a target user.
In the user screening method, the target probability value is calculated by predicting the first probability value of the user to be screened for executing the service content display operation aiming at the target service and the second probability value of the user to be screened for executing the service triggering operation aiming at the target service and then by a weighting algorithm of the first probability value and the second probability value, and when the target probability value is in a threshold range, the user data to be screened is determined to be the target user data.
Fig. 2 is a schematic flow chart of a user screening method provided in an embodiment of the present application, and as shown in the figure, on the basis of the user screening method shown in fig. 1, the user screening method may further include the following steps:
step S105, sending a reminding message capable of performing the service content display operation and/or the service trigger operation for the target service to the target user, where the reminding message includes a request instruction for requesting to perform the service content display operation and/or the service trigger operation.
Optionally, the request instruction for requesting execution of the service content display operation includes an option indicating whether to execute the service content display operation for the target service, such as permission to execute the service content display operation and non-permission to execute the service content display operation; the request instruction for requesting execution of the service triggering operation includes an option indicating whether to execute the service triggering operation for the target service, such as agreeing to execute the service triggering operation and disagreeing to execute the service triggering operation.
Step S106, according to the instruction signal fed back aiming at the request instruction, determining whether the target user executes the result of the service content display operation and/or the service trigger operation aiming at the target service.
Optionally, after the target user triggers the option of the request instruction, an instruction signal indicating whether to execute the service content display operation and/or the service trigger operation is returned to the service processing server, and the service processing server determines whether to execute the service content display operation and/or the service trigger operation according to the instruction signal.
In the user screening method, according to the result of the screened target user, the reminding information capable of executing the business content display operation and/or the business trigger operation is sent to the electronic equipment terminal at the target user, so that the target user can select whether to execute the business content display operation and/or the business trigger operation, and when the target user selects to execute the business content display operation and/or the business trigger operation, the business processing server can respond to the instruction signal and directly execute the business content display operation and/or the business trigger operation, thereby reducing the user operation process, simplifying the operation steps and improving the execution efficiency.
Fig. 3 is a schematic flowchart of a user screening method provided in an embodiment of the present application, and as shown in the figure, on the basis of the user screening method shown in fig. 2, the user screening method may further include the following steps:
step S107, using the user characteristics of the target user and the result of whether the target user executes the service content display operation aiming at the target service as a new first training sample to train the first model.
Optionally, after the service processing server determines to execute the service content display operation according to the instruction signal fed back by the target user, the result of the service content display operation is used as new sample data to be supplemented into the first training sample, and the first model is repeatedly trained.
Step S108, using the user characteristics of the target user and the result of whether the target user executes the service triggering operation aiming at the target service as a new second training sample to train the second model.
It should be noted that step S107 and step S108 may be executed simultaneously, or alternatively executed according to the determination of the operator for the target service. The execution order of step S107 and step S108 when executed simultaneously may be set according to the actual requirements of the operator.
In the user screening method, after the service processing server executes the service content display operation and/or the service triggering operation according to the instruction signal, the execution result is used as a new training sample and is supplemented into the first training sample and/or the second training sample, so that a data closed loop is formed, the first model and/or the second model can be updated through the latest training sample, the model can be updated by itself, and the prediction accuracy is improved.
Fig. 4 is a schematic flowchart of a user screening method provided in an embodiment of the present application, and as shown in the figure, the method includes the following steps:
step S201, after reading account data registered by a user, the electronic device terminal sends attribute information of the user, such as gender, age, location, and the like, in the account data to a service processing server, and when the electronic device terminal reads that a service content display operation, a service trigger operation, or a user marketing result is generated under the account, the electronic device terminal sends the attribute information to the service processing server, and the service processing server stores the service content display operation, the service trigger operation, and the user marketing result in association with the account respectively.
Step S202, the service processing server establishes a first model according to the attribute information of the user to be screened, the service content display operation, the service trigger operation or the user marketing result so as to predict a first probability value of the user to be screened for executing the service content display operation aiming at the target service.
Step S203, the service processing server establishes a second model according to the attribute information of the user to be screened, the service content display operation, the service trigger operation, or the user marketing result, so as to predict a second probability value of the user to be screened for executing the service trigger operation with respect to the target service.
And step S204, the service processing server executes a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value.
Step S205, when the target probability value is in the threshold value range, the service processing server determines that the user to be screened is the target user.
Step S206, sending a reminding message capable of performing the service content display operation and the service trigger operation for the target service to the target user, where the reminding message includes a request instruction for requesting to perform the service content display operation and the service trigger operation.
Step S207, determining whether the target user executes the result of the service content display operation and the service trigger operation for the target service according to the instruction signal fed back for the request instruction.
Step S208, taking the attribute information of the target user, the business content display operation, the business trigger operation or the user marketing result and the result of whether the target user executes the business content display operation aiming at the target business as a new first training sample to repeatedly train the first model.
Step S209, repeatedly training the second model by using the attribute information of the target user, the service content display operation, the service trigger operation or the user marketing result, and the result of whether the target user performs the service trigger operation for the target service as a new second training sample.
And step S210, continuously predicting the probability value of the user to be screened by using the first model and the second model after repeated training.
Fig. 5 is a schematic structural diagram of a service processing server according to an embodiment of the present application, and as shown in the drawing, the service processing server includes:
a first probability value prediction module 301, configured to predict, according to a user characteristic of a user to be screened, a first probability value of the user to be screened, for performing a service content display operation on a target service;
a second probability value predicting module 302, configured to predict, according to the user characteristics of the user to be screened, a second probability value of the user to be screened for performing a service triggering operation on the target service;
a target probability value obtaining module 303, configured to execute a weighted average algorithm on the first probability value and the second probability value according to a preset weight to obtain a target probability value;
a target user determining module 304, configured to determine that the user to be filtered is a target user when the target probability value is within a threshold range.
In one embodiment, the first probability value prediction module 301 comprises:
the first model execution submodule is used for predicting a first probability value of the user to be screened for executing service content display operation aiming at the target service through a first model based on the user characteristics of the user to be screened;
the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service;
the second probability value prediction module 302 comprises:
the second model execution submodule is used for predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened;
the second model is obtained by training according to a second training sample, and the second training sample includes the user characteristics of the sample user and the result of whether the sample user executes the service triggering operation for the target service.
Fig. 6 is a schematic structural diagram of a service processing server provided in an embodiment of the present application, as shown in the figure, the service processing server further includes:
a reminding information sending module 305, configured to send reminding information that is capable of performing the service content display operation and/or the service triggering operation on the target service to the target user, where the reminding information includes a request instruction for requesting to perform the service content display operation and/or the service triggering operation;
an execution determining module 306, configured to determine, according to an instruction signal fed back according to the request instruction, whether the target user executes the result of the service content display operation and/or the service triggering operation for the target service.
Fig. 7 is a schematic structural diagram of a service processing server provided in an embodiment of the present application, as shown in the figure, the service processing server further includes:
a first model repeated training module 307, configured to train the first model by using, as a new first training sample, the user characteristics of the target user and a result of whether the target user performs the service content display operation for the target service;
and/or, a second model repetitive training module 308, configured to train the second model using the user characteristics of the target user and a result of whether the target user performs the service triggering operation for the target service as a new second training sample.
Fig. 8 is a schematic structural diagram of a service processing server according to an embodiment of the present application, where the electronic device terminal may include at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the user screening method provided by the embodiments shown in fig. 1 to 4 of the present specification.
The embodiment of the present application further provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions, when executed by a processor, implement the steps of the user screening method. The readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
In the description of embodiments of the invention, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present description in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present description.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It should be noted that the terminal referred to in the embodiments of the present application may include, but is not limited to, a Personal Computer (PC), a Personal Digital Assistant (PDA), a wireless handheld device, a tablet computer (tablet computer), a mobile phone, an MP3 player, an MP4 player, and the like.
In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present description may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods described in the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims (10)

1. A method for user screening, the method comprising:
predicting a first probability value of a user to be screened for executing service content display operation aiming at a target service according to user characteristics of the user to be screened;
predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service according to the user characteristics of the user to be screened;
executing a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value;
and when the target probability value is within a threshold value range, determining the user to be screened as a target user.
2. The method of claim 1,
the predicting a first probability value of the user to be screened for performing service content display operation aiming at the target service comprises:
predicting a first probability value of the user to be screened for executing service content display operation aiming at a target service through a first model based on the user characteristics of the user to be screened;
the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service;
the predicting, according to the user characteristics of the user to be screened, a second probability value of the user to be screened for executing the service triggering operation for the target service includes:
predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened;
the second model is obtained by training according to a second training sample, and the second training sample includes the user characteristics of the sample user and the result of whether the sample user executes the service triggering operation for the target service.
3. The method of claim 2, wherein the method further comprises:
sending a reminding message which can execute the service content display operation and/or the service trigger operation aiming at the target service to the target user, wherein the reminding message comprises a request instruction for requesting to execute the service content display operation and/or the service trigger operation;
and determining whether the target user executes the result of the service content display operation and/or the service triggering operation aiming at the target service according to the instruction signal fed back aiming at the request instruction.
4. The method of claim 3, wherein the method further comprises:
training the first model by taking the user characteristics of the target user and the result of whether the target user executes the business content display operation aiming at the target business as a new first training sample;
and/or training the second model by taking the user characteristics of the target user and the result of whether the target user executes the service triggering operation aiming at the target service as a new second training sample.
5. The method according to any one of claims 1 to 4,
the user characteristics include at least one or a combination of: user attributes, user behavior characteristics, and user marketing result characteristics.
6. A traffic processing server, characterized in that the server comprises:
the first probability value prediction module is used for predicting a first probability value of the user to be screened for executing the service content display operation aiming at the target service according to the user characteristics of the user to be screened;
the second probability value predicting module is used for predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service according to the user characteristics of the user to be screened;
the target probability value obtaining module is used for executing a weighted average algorithm on the first probability value and the second probability value according to preset weight to obtain a target probability value;
and the target user determination module is used for determining the user to be screened as the target user when the target probability value is within a threshold range.
7. The server according to claim 6,
the first probability value prediction module comprises:
the first model execution submodule is used for predicting a first probability value of the user to be screened for executing business content display operation aiming at the target business through a first model based on the user characteristics of the user to be screened;
the first model is obtained by training according to a first training sample, and the first training sample comprises user characteristics of a sample user and a result of whether the sample user executes service content display operation aiming at the target service;
the second probability value prediction module comprises:
the second model execution submodule is used for predicting a second probability value of the user to be screened for executing the service triggering operation aiming at the target service through a second model based on the user characteristics of the user to be screened;
the second model is obtained by training according to a second training sample, and the second training sample includes the user characteristics of the sample user and the result of whether the sample user executes the service triggering operation for the target service.
8. The server of claim 7, wherein the server further comprises:
a reminding information sending module, configured to send reminding information that is capable of performing the service content display operation and/or the service trigger operation for the target service to the target user, where the reminding information includes a request instruction for requesting execution of the service content display operation and/or the service trigger operation;
and the execution determining module is used for determining whether the target user executes the result of the service content display operation and/or the service triggering operation aiming at the target service according to the instruction signal fed back aiming at the request instruction.
9. A transaction server, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1 to 5.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, carry out the steps of the method according to any one of claims 1 to 5.
CN202210128798.2A 2022-02-11 2022-02-11 User screening method, service processing server and storage medium Pending CN114663127A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
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Publications (1)

Publication Number Publication Date
CN114663127A true CN114663127A (en) 2022-06-24

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