CN112258238A - User life value cycle detection method and device and computer equipment - Google Patents
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Abstract
The application relates to a user life value cycle detection method and device, computer equipment and a storage medium. Acquiring behavior data corresponding to a user account; inputting the behavior data into a trained classification model to obtain a user type corresponding to a user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type; if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period; and if the user type is a second user type, inputting the behavior data corresponding to the user account into the trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type. The user type is determined by using the historical behavior data of the user, and the user LTV is determined according to different user types, so that the error of user LTV detection is reduced.
Description
Technical Field
The present application relates to the field of mobile application technologies, and in particular, to a method and an apparatus for detecting a user life value cycle, a computer device, and a storage medium.
Background
With the full-area coverage of mobile devices, the mobile application market has rapidly developed. Up to now there are millions of mobile applications in both App Store and Google Play Store. Although the number of mobile applications has proliferated, the number of applications actually used by users has decreased slowly, and the data display users spend more time on fewer applications. In addition to retaining existing users, new users need to be obtained by using efficient and accurate popularization methods. For an application, there are many ways to acquire a new user, such as: advertisement placement, mall recommendation, friend recommendation, etc., wherein new users obtained by advertisement placement account for about 70% of all new users. Advertisement delivery needs controllable cost, reasonably selects channels, and accurately detects user LTV (Life Value), thereby maximizing input-output ratio.
The traditional user LTV detection method adopts a fitting method to fit a change function of the user LTV along with time, and then the user LTV is detected.
However, the fitting method cannot accurately obtain the LTV function of the user, and therefore the detected LTV error of the user is large.
Disclosure of Invention
In view of the above, it is necessary to provide a user life value cycle detection method, apparatus, computer device and storage medium capable of accurately detecting a user life value cycle.
A user life value cycle detection method comprises the following steps:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period;
and if the user type is a second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
In one embodiment, after acquiring the behavior data corresponding to the user account, the method further includes: and preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the preprocessing the behavior data corresponding to the user account includes: classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain numerical type behavior data and non-numerical type behavior data; carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data; and merging the behavior data of the numerical user account and the encoded behavior data of the non-numerical user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the behavior data corresponding to the user account includes: the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
In one embodiment, the training process of the classification model includes: taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples; and training the initial classification model according to the positive sample and the negative sample to obtain a trained classification model.
In one embodiment, the training process of the prediction model includes: and acquiring a training sample, wherein the training sample comprises historical behavior data corresponding to a sample user account belonging to a second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain a trained prediction model.
In one embodiment, determining a life value cycle LTV of a user corresponding to the user account according to a preset attribute value and a number of users belonging to a first user type in a preset time period includes: and multiplying the preset attribute value by the number of the users belonging to the first user type in the preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
A user life value cycle prediction apparatus, the apparatus comprising:
the acquisition module is used for acquiring behavior data corresponding to the user account;
the classification module is used for inputting the behavior data of the user account into a trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
the first determining module is used for determining the life value cycle LTV of the user corresponding to the user account according to the preset attribute value and the number of the users belonging to the first user type in the preset time period if the user type is the first user type;
and the second determining module is used for inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second type of user if the user type is the second user type, and the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
A computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program:
a user life value cycle detection method comprises the following steps:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period;
and if the user type is a second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
a user life value cycle detection method comprises the following steps:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period;
and if the user type is a second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
The user life value cycle detection method, the user life value cycle detection device, the computer equipment and the storage medium acquire behavior data corresponding to the user account; inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type; if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period; and if the user type is a second user type, inputting the behavior data corresponding to the user account into the trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type. The user type is determined by using the historical behavior data of the user, and the user LTV is determined according to different user types, so that the error of user LTV detection is reduced.
Drawings
FIG. 1 is a diagram of an exemplary environment in which a user's life cycle detection method may be implemented;
FIG. 2 is a schematic flow chart diagram illustrating a user life value cycle detection method according to one embodiment;
FIG. 3 is a logic diagram of a user's life value cycle detection method in one embodiment;
FIG. 4 is a logic diagram of a user's life value cycle detection method in another embodiment;
FIG. 5 is a block diagram showing the structure of a user life cycle detection apparatus according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The user life value cycle detection method provided by the application can be applied to the application environment shown in fig. 1. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices.
In one embodiment, as shown in fig. 2, a method for detecting a user life value cycle is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
Specifically, the behavior data corresponding to the user account includes basic information of the user account and behavior data generated by the user account operating a service in a corresponding application scenario, and the behavior data corresponding to the user account is stored in a database of the data center. Optionally, the behavior data corresponding to the user account may include basic information, payment information, social information, game behavior information, game information corresponding to the user account, and the like of the user account, and the terminal acquires the behavior data corresponding to the user account from the database of the data center at a fixed time through the acquisition interface, where the fixed time may be 7 days, 10 days, 15 days, or other days after the new user account is registered, and is not limited herein. The application scenario in the embodiment of the present application may be a game scenario or other service scenarios, such as a video conference service scenario, a video playing service scenario, and other service scenarios, the behavior data of the user account in the embodiment of the present application is described by taking the game service scenario as an example, and the behavior data of the user account in other service scenarios may also be applicable to the user life value cycle detection method provided in the embodiment of the present application.
Specifically, the trained classification model is used for determining a user type corresponding to a user account according to behavior data corresponding to the user account, wherein the user type comprises a first user type and a second user type, the first user type can be a non-paid user, the second user type can be a paid user, the classification model takes historical behavior data of the first user type and behavior data of the second user as training samples, historical behavior data corresponding to the user account as features, training sets of a plurality of training samples are obtained through a plurality of times of replaced random sampling operations, for each training set, k features are randomly selected from the features of the behavior data corresponding to the user account, an optimal segmentation feature is selected as a node of a decision tree to establish the decision tree model, a random forest model with a plurality of decision tree models is established, and d is the number of feature values of the behavior data corresponding to the user account, k is typically the logarithm of the number of eigenvalues to base 2.
In step 206, if the user type is the first user type, the life value cycle LTV of the user corresponding to the user account is determined according to the preset attribute value and the number of users belonging to the first user type in the preset time period.
Specifically, the preset attribute value is an average payment value of the first user type in a preset time period, and may be an average payment value of a non-payment user, where the preset time period is a time period for acquiring behavior data corresponding to a user account, and may be 7 days, 10 days, 30 days, or other days, where no limitation is imposed on the preset time period, and a life value cycle of the first user type, that is, an expected payment value of the non-payment user, is obtained by performing mathematical operation on the average payment value of the first user type and the number of users classified into the first user type by using the trained classification model in the preset time period. Optionally, the life value cycle of the first user type may be obtained by multiplying the average payment value of the first user type by the number of users classified as the first user type per day, or may be obtained by multiplying the average payment value of the first user type by the number of users classified as the first user type per day and then multiplying by a coefficient.
And 208, if the user type is the second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
Specifically, the trained prediction model is used for constructing a regression tree by dividing the attributes of the historical behavior data of the second user type according to the historical behavior data classified as the second user type as a training sample, the life value cycle of the second user type as a training target, obtaining a plurality of regression trees through training, wherein leaf nodes of each tree correspond to a score, and finally adding the scores corresponding to the leaf nodes to obtain the life value cycle of the second user type, namely the payment value of the paying user.
In the user life value cycle detection method, behavior data corresponding to a user account is obtained; inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type; if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period; and if the user type is a second user type, inputting the behavior data corresponding to the user account into the trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type. The user type is determined by using the historical behavior data of the user, and the user LTV is determined according to different user types, so that the error of user LTV detection is reduced.
In one embodiment, after acquiring the behavior data corresponding to the user account, the method further includes: and preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
Specifically, after the terminal acquires the behavior data corresponding to the user account, the behavior data corresponding to the user account is preprocessed, wherein the preprocessing refers to performing standardized processing on behavior characteristics corresponding to the user account, the standardized processing includes data cleaning and characteristic coding, and the data cleaning refers to correcting error information in the acquired behavior data corresponding to the user account, deleting repeated information, and ensuring data consistency. The feature code is to perform unique hot coding on the behavior data corresponding to the user account after the data is cleaned, for example, the behavior data corresponding to the user account may be basic information, payment information, social information, game behavior information, game information corresponding to the user account, and the like, some of the information may be numerical data, such as payment information, and some of the information may be non-numerical information, such as social information, game behavior information, game information corresponding to the game information, and the like, and optionally, the preprocessing may be to code and convert non-numerical information, such as the social information, the game behavior information, and game information corresponding to the social information, and the like, into numerical information.
In one embodiment, the preprocessing the behavior data corresponding to the user account includes: classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain numerical type behavior data and non-numerical type behavior data; carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data; and merging the behavior data of the numerical user account and the encoded behavior data of the non-numerical user account to obtain the preprocessed behavior data of the user account.
Specifically, the behavior data corresponding to the user account is preprocessed, the behavior data corresponding to the user account is cleaned firstly, then classified, and the classified behavior data corresponding to the user account which needs characteristic coding is coded, so that the behavior data corresponding to all the user accounts become standard. The behavior data corresponding to the user account is classified according to a numerical type and a non-numerical type, for example, payment information is classified into numerical type information, basic information, social information, game behaviors and game information corresponding to the payment information are classified into non-numerical type information, then the non-numerical type information is subjected to unique hot coding, for example, the basic information has the characteristics of male and female, the male can be subjected to unique hot coding to obtain corresponding numerical information 10, the female can be subjected to unique hot coding to obtain corresponding numerical information 01, only one bit of the information after the unique hot coding is valid, namely only one bit is 1, and other bits are 0. And combining the coded non-numerical user account behavior data with the numerical user account behavior data to obtain the preprocessed user account behavior data, which is beneficial to the calculation of feature similarity in the prediction model.
In one embodiment, the behavior data corresponding to the user account includes: the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
Specifically, the behavior data corresponding to the user account includes basic information of the user account and other behavior data corresponding to the user account, where the other behavior data may be payment information, social information, game behavior information and game information corresponding to the game behavior information. Wherein, the basic information of the user can be the country, sex, age, etc. of the user, the payment information can be the payment related information such as payment amount when the user operates the object in the application scene, gift bag amount obtained through payment, etc., the social information can be the friend amount of the user, the interaction information between the user and friends in the application scene or other users in the guild in the application scene, the game behavior information can be the operation performed on the service object in the application scene, the game behavior information and the game information corresponding to the game behavior information refer to information related to the operation service, such as credit increase after online, gold coins obtained after successful attack in the game, credit increase of watching paid video in video playing, and the like.
In one embodiment, the training process of the classification model includes: taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples; and training the initial classification model according to the positive sample and the negative sample to obtain a trained classification model.
Specifically, as shown in fig. 3, a classification model is obtained by training using historical behavior data corresponding to a user account as a training sample and using an F2-score function as an evaluation function. For example, a random forest model is used as a classification model, a sample user account and corresponding historical behavior data belonging to a first user type are used as negative samples, a sample user account and corresponding historical behavior data belonging to a second user type are used as positive samples, an F2-score function is used as an evaluation function, parameters of the random forest model are trained in SageMaker service to obtain a trained random forest model, the classification model can be trained in other modes, and the method is only required to obtain a better classification model.
In one embodiment, the training process of the predictive model includes: and acquiring a training sample, wherein the training sample comprises historical behavior data corresponding to a sample user account belonging to a second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain a trained prediction model.
Specifically, as shown in fig. 3, classifying the user account in the trained classification model, determining the user type corresponding to the user account, when the user type corresponding to the user account is the second user type, obtaining historical behavior data of the second user type as a training sample, taking the life value cycle of the user of the second user type as a training target, taking the root mean square error as a loss function, and training to obtain the prediction model. For example, the second user type is a paying user, historical behavior data of the paying user within 7 registered days is used as a training sample, the life value cycle of the paying user is used as a training target, a root mean square error is used as a loss function, namely an evaluation function, an XGBoost (irregular Gradient Boosting, Gradient Boosting decision tree) is used as a prediction model, and parameters of the XGBoost model are trained in a SageMaker service to obtain the trained prediction model.
In one embodiment, the determining, according to the preset attribute value and the number of users belonging to the first user type in the preset time period, a life value cycle LTV of the user corresponding to the user account includes: and multiplying the preset attribute value by the number of the users belonging to the first user type in the preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
Specifically, as shown in fig. 4, the preset attribute value is an average payment value of the first user type in a preset time period, the average payment value is obtained by dividing an actual total payment value of all users classified as the first user type in the preset time period by the number of users of the first user type, and then, a mathematical operation is performed according to the number of users of the first user type and the average payment value of the first user type daily to obtain a life value cycle of the first user type. For example, the preset time period is 7 days, the behavior data of the user account within 7 days of registration is acquired, the behavior data of the user account within 7 days of registration is input into a trained classification model, the user type corresponding to the user account is determined, and the life value cycle LTV of the user corresponding to the first user type is obtained by multiplying the average payment value of the first user type by the number of users of the first user type per day.
In the embodiment, behavior data corresponding to a user account is acquired; inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type; if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period; and if the user type is a second user type, inputting the behavior data corresponding to the user account into the trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type. The user type is determined by using the historical behavior data of the user, and the user LTV is determined according to different user types, so that the error of user LTV detection is reduced.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In one embodiment, as shown in fig. 5, there is provided a life value cycle detecting apparatus including: an obtaining module 502, a classifying module 504, a first determining module 506, and a second determining module 508, wherein:
the obtaining module 502 is configured to obtain behavior data corresponding to a user account.
A classification module 504, configured to input behavior data of the user account into a trained classification model, so as to obtain a user type corresponding to the user account; the classification model is trained based on historical behavior data of the first user type and historical behavior data of the second user type.
A first determining module 506, configured to determine, if the user type is the first user type, a life value cycle LTV of the user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period.
A second determining module 508, configured to, if the user type is the second user type, input behavior data corresponding to the user account into a trained prediction model to obtain an LTV of the second type of user, where the prediction model is obtained by training based on historical behavior data belonging to the second user type.
In an embodiment, after the obtaining module 502 obtains the behavior data corresponding to the user account, the method further includes: the preprocessing module is used for preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the preprocessing module preprocesses the behavior data corresponding to the user account, including: the sub-module pretreatment classification module of the pretreatment module is used for classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain the behavior data of the numerical type and the behavior data of the non-numerical type; the sub-module preprocessing coding module of the preprocessing module is used for carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data; and the sub-module pretreatment merging module of the pretreatment module is used for merging the behavior data of the numerical user account and the coded behavior data of the non-numerical user account to obtain the pretreated behavior data of the user account.
In one embodiment, the behavior data unit corresponding to the user account includes: the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
In one embodiment, the sub-module of the classification module 504 includes the training process module of the classification model: taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples; and training the initial classification model by a training process module of the classification model according to the positive sample and the negative sample to obtain the trained classification model.
In one embodiment, the training process modules of the sub-module predictive model of the classification module 504 include: and the training process module of the prediction model is used for acquiring a training sample, the training sample comprises historical behavior data corresponding to a sample user account belonging to the second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain the trained prediction model.
In one embodiment, the determining module 506 is configured to determine, according to the preset attribute value and the number of users belonging to the first user type in the preset time period, a life value cycle LTV of the user corresponding to the user account, and includes: and multiplying the preset attribute value by the number of the users belonging to the first user type in the preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
For the specific limitation of the user life value cycle detection device, reference may be made to the above limitation on the user life value cycle detection method, which is not described herein again. All or part of the modules in the user life cycle detection device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a user life value cycle detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period;
and if the user type is the second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
In one embodiment, the processor, when executing the computer program, further performs the steps of: after the behavior data corresponding to the user account is acquired, the method further comprises the following steps: and preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the processor, when executing the computer program, further performs the steps of: preprocessing the behavior data corresponding to the user account, including: classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain numerical type behavior data and non-numerical type behavior data; carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data; and merging the behavior data of the numerical user account and the encoded behavior data of the non-numerical user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the behavior data corresponding to the user account includes: the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the training process of the classification model comprises the following steps: taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples; and training the initial classification model according to the positive sample and the negative sample to obtain a trained classification model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the training process of the prediction model comprises the following steps: and acquiring a training sample, wherein the training sample comprises historical behavior data corresponding to a sample user account belonging to a second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain a trained prediction model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to a first user type in a preset time period, wherein the LTV comprises the following steps: and multiplying the preset attribute value by the number of the users belonging to the first user type in the preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into the trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type in a preset time period;
and if the user type is the second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
In one embodiment, the computer program when executed by the processor further performs the steps of: after the behavior data corresponding to the user account is acquired, the method further comprises the following steps: and preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the computer program when executed by the processor further performs the steps of: preprocessing the behavior data corresponding to the user account, including: classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain numerical type behavior data and non-numerical type behavior data; carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data; and merging the behavior data of the numerical user account and the encoded behavior data of the non-numerical user account to obtain the preprocessed behavior data of the user account.
In one embodiment, the computer program when executed by the processor further performs the steps of: the behavior data corresponding to the user account includes: the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
In one embodiment, the computer program when executed by the processor further performs the steps of: the training process of the classification model comprises the following steps: taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples; and training the initial classification model according to the positive sample and the negative sample to obtain a trained classification model.
In one embodiment, the computer program when executed by the processor further performs the steps of: the training process of the prediction model comprises the following steps: and acquiring a training sample, wherein the training sample comprises historical behavior data corresponding to a sample user account belonging to a second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain a trained prediction model.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to a first user type in a preset time period, wherein the LTV comprises the following steps: and multiplying the preset attribute value by the number of the users belonging to the first user type in the preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A user life value cycle detection method is characterized by comprising the following steps:
acquiring behavior data corresponding to a user account;
inputting the behavior data of the user account into a trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
if the user type is the first user type, determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type within a preset time period;
and if the user type is the second user type, inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
2. The method according to claim 1, wherein after acquiring the behavior data corresponding to the user account, the method further comprises:
and preprocessing the behavior data corresponding to the user account to obtain the preprocessed behavior data of the user account.
3. The method according to claim 2, wherein the preprocessing the behavior data corresponding to the user account includes:
classifying the behavior data of the user account according to a numerical type and a non-numerical type to obtain numerical behavior data and non-numerical behavior data;
carrying out characteristic coding on the non-numerical behavior data to obtain coded non-numerical behavior data;
and merging the behavior data of the numerical user account and the encoded behavior data of the non-numerical user account to obtain the preprocessed behavior data of the user account.
4. The method of claim 1, wherein the behavior data corresponding to the user account comprises:
the game system comprises basic information, payment information, social information, game behavior information and game information corresponding to the basic information, the payment information, the social information and the game behavior information of the user account.
5. The method of claim 1, wherein the training process of the classification model comprises:
taking a sample user account belonging to a first user type and corresponding historical behavior data as negative samples, and taking a sample user account belonging to a second user type and corresponding historical behavior data as positive samples;
and training an initial classification model according to the positive sample and the negative sample to obtain a trained classification model.
6. The method of claim 1, wherein the training process of the predictive model comprises:
and acquiring a training sample, wherein the training sample comprises historical behavior data corresponding to a sample user account belonging to a second user type and a user life value cycle corresponding to the sample user account, and the root mean square error is used as a loss function to obtain a trained prediction model.
7. The method according to claim 1, wherein the determining the life value cycle LTV of the user corresponding to the user account according to the preset attribute value and the number of users belonging to the first user type in a preset time period comprises:
and multiplying the preset attribute value by the number of the users belonging to the first user type in a preset time period to obtain the life value cycle LTV of the user corresponding to the user account.
8. An apparatus for predicting a life cycle of a user, the apparatus comprising:
the acquisition module is used for acquiring behavior data corresponding to the user account;
the classification module is used for inputting the behavior data of the user account into a trained classification model to obtain a user type corresponding to the user account; the classification model is obtained by training based on historical behavior data of a first user type and historical behavior data of a second user type;
the first determining module is used for determining a life value cycle (LTV) of a user corresponding to the user account according to a preset attribute value and the number of users belonging to the first user type within a preset time period if the user type is the first user type;
and the second determining module is used for inputting the behavior data corresponding to the user account into a trained prediction model to obtain the LTV of the second type of user if the user type is the second user type, wherein the prediction model is obtained by training based on the historical behavior data belonging to the second user type.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN202011188855.3A CN112258238A (en) | 2020-10-30 | 2020-10-30 | User life value cycle detection method and device and computer equipment |
| CN202110993108.5A CN113538070B (en) | 2020-10-30 | 2021-08-25 | User life value cycle detection method and device and computer equipment |
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| CN113743991A (en) * | 2021-09-03 | 2021-12-03 | 上海幻电信息科技有限公司 | Life cycle value prediction method and device |
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| CN115938590B (en) * | 2023-02-09 | 2023-05-02 | 四川大学华西医院 | Construction method and prediction system of colorectal cancer postoperative LARS prediction model |
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| US20150235239A1 (en) * | 2014-02-19 | 2015-08-20 | International Business Machines Corporation | Predicting demand of a newly introduced short lifecycle product within an assortment |
| US10832260B2 (en) * | 2017-01-27 | 2020-11-10 | Walmart Apollo Lllc | Systems and methods for determining customer lifetime value |
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| CN109711859A (en) * | 2018-11-12 | 2019-05-03 | 平安科技（深圳）有限公司 | Prediction technique and device, storage medium, the computer equipment of mixed railway |
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| CN113052383A (en) * | 2021-03-29 | 2021-06-29 | 上海酷量信息技术有限公司 | Income prediction method and device based on machine learning |
| CN113743991A (en) * | 2021-09-03 | 2021-12-03 | 上海幻电信息科技有限公司 | Life cycle value prediction method and device |
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