CN109325810B - Recharge conversion improving method, electronic equipment and computer storage medium - Google Patents
Recharge conversion improving method, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method for improving recharge conversion, electronic equipment and a computer storage medium, which are used for improving the condition of a recharge conversion result of a recharge page, wherein the method comprises the following steps: predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user; and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule. The embodiment of the invention solves the problem of poor recharging conversion result of the recharging page accessed by the user in the prior art, provides a personalized recharging strategy mode for the target user, improves the actual recharging behavior of the target user, and improves the recharging conversion proportion of the recharging page.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a recharge conversion improving method, electronic equipment and a computer storage medium.
Background
Currently, with the popularization of mobile terminals such as mobile phones and the development of electronic book readers, electronic books are more and more favored by reading users. As a modern reading trend, the advantages of electronic reading are quite obvious: low carbon, convenience, low cost and large storage capacity.
For electronic reading enterprises, the electronic reading enterprise can find the defects of application products in time, help the enterprises to improve the application functions continuously, improve the product competitiveness, improve the operation efficiency and the user experience, and is an important responsibility of the enterprises. However, in the aspect of reading and recharging, a personalized recharging mode for the user still lacks in the prior art, so that the actual recharging conversion rate of the user is not high.
Disclosure of Invention
In view of the above, the present invention has been made to provide an improved method of top-up conversion, an electronic device and a computer storage medium that overcome or at least partially solve the above-mentioned problems.
According to an aspect of the present invention, there is provided a recharge conversion improvement method, the method comprising: predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user; recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
According to another aspect of the present invention, there is provided an electronic apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is configured to store at least one executable instruction that causes the processor to: predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user; recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
According to yet another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to: predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user; recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
According to the method for improving the recharging conversion, the electronic equipment and the computer storage medium, the recharging intention of the target user is predicted according to the reading behavior characteristics of the target user, and the recharging strategy corresponding to the target recharging gear of the target user is recommended to the target user by combining the recharging intention, so that the problem that the recharging conversion result of a user accessing a recharging page is poor in the prior art is solved, a personalized recharging strategy mode is provided for the target user, the actual recharging behavior of the target user is improved, and the recharging conversion proportion of the recharging page is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for improving recharge conversion according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating another method of load conversion enhancement provided by an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a flowchart illustrating a method for improving a recharge conversion result of a recharge page according to an embodiment of the present invention, where the method may be executed by an electronic device supporting installation of an e-book reading application. As shown in fig. 1, the method comprises the steps of:
and step S101, predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user.
In this embodiment, the reading behavior feature of the target user refers to a behavior feature having a correlation with the recharging behavior, and thus, the obtained reading behavior feature of the target user can be used as an input of the intention prediction model to predict the recharging intention of the target user. The recharge intent represents a probability that the user is currently recharging. For example, the intention prediction model represents the recharging intention of the user in the form of output probability, and the higher the output probability value is, the higher the probability that the user currently charges is represented.
Optionally, the intention prediction model is a binary classification model, and the training process includes:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as an intention prediction model.
The sample users are a large number of reading users who randomly grab the training intention prediction model. The positive sample characteristics comprise behavior characteristics related to the fact that a sample user firstly or repeatedly accesses a recharging page within a specific time period and finishes recharging within preset time, wherein the preset time is adaptively set according to factors such as loading time of the recharging page and loading time of a payment plug-in, for example, 60 seconds. The negative sample characteristics include behavioral characteristics involved in a user accessing a top-up page during the same time period but never successfully top-up. The accuracy of predicting the recharging intention by using the model can be ensured by distinguishing the reading behavior of the sample user according to the success or failure of recharging and then using the behavior for training the model.
Specifically, the reading behavior characteristics of the target user or the sample user include current reading behavior characteristics and historical reading behavior characteristics. The current reading behavior characteristic and the historical reading behavior characteristic are distinguished by taking the time of the day as a boundary, the reading behavior of the user on the day is attributed to the current reading behavior, and the reading behavior characteristic in a certain time period before the time of the day is attributed to the historical reading behavior characteristic. For the target user, the time of the day is the day on which the prediction of the recharging intention is performed; for the sample user, the time of day described herein is the day on which payment was made or was not made. Exemplary, current reading behavior characteristics include: the dimension characteristics of the user such as book reading progress, downloading progress, payment condition, current balance state of the user, attention of the user to recharging preference and the like in the current day; the historical reading behavior characteristics comprise: and the adoption conditions of downloading, paying, reading history, recharging and preferential recommendation strategies in a certain time period before the current day, the attention degree of the recharging preferential and other dimensionality characteristics. The storage positions of the current reading behavior characteristic and the historical reading behavior characteristic are different, so that corresponding data needs to be called from different storage positions.
And step S102, recommending a recharging strategy corresponding to the target recharging position for the target user based on the predicted recharging intention and the target recharging position of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging position and set according to a preset rule.
The target recharging gear represents the most possibly selected recharging amount of the target user if recharging is performed currently, and can be determined through behavior analysis of the target user. Optionally, the method further includes: and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user. The target recharge gear may include a historical used recharge gear or a recent recharge gear. For example, by analyzing the personal information and the historical recharging information of the target user in a preset time period, it is obtained that the purchasing power of the target user does not change, and the frequency of recharging by using the recharging gear c is very high, the recharging gear c can be determined as the target recharging gear, and at this time, the recharging gear c belongs to a historical customary recharging gear; or, in the process of analyzing the personal information of the target user, it is found that the identity information of the target user changes, for example, a student changes into an engineer, which indicates that the purchasing power of the target user changes, and the frequency of charging by using the charging gear z after the identity changes is high, the charging gear z can be determined as the target charging gear, and at this time, the charging gear z belongs to the recent charging gear.
When the probability that the target user currently charges is predicted to be smaller, recommending a charging strategy with higher preferential strength for the target user according to the target charging gear; when the probability that the target user currently charges is predicted to be large, a charging strategy with low preferential degree can be recommended for the target user. The purpose of recommending the recharging strategy is to improve the actual recharging behavior of the user with low probability of currently recharging so as to improve the recharging conversion proportion of the user accessing the recharging page, namely the proportion between the number of the users accessing the recharging page and successfully recharging and the total number of the users accessing the recharging page.
The preset rules in the recharging strategy are set by the reading application service provider, and determine the specific preferential ways displayed to the user, such as giving a recharging coupon or recharging a discount.
For example, the target recharging shift position of the target user a is x1, and if the probability of recharging currently is predicted to be small, a recharging preference with a large discount can be recommended to the target user a in the x1 shift position; the target recharging gear of the target user B is x2, the probability of recharging the target user B at present is predicted to be high, recharging offers with small discount can be recommended to the target user B in the x2 gear, and the target user B can also choose not to recommend the recharging offers.
Optionally, before predicting the recharge intention of the target user by using the intention prediction model according to the reading behavior characteristics of the target user, the method further includes:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
That is, the implementation of the embodiment is premised on the target user accessing the top-up page, for example, the target user accesses the top-up page in the process of reading the electronic book, or switches from the current page to the top-up page according to the pushed active link, and so on. And when the current page is determined to be the recharging page, the prediction of the recharging intention and the subsequent operation are executed. The page detection can be realized by detecting page layout information, page controls and the like.
According to the technical scheme, the recharging intention of the target user is predicted by the intention prediction model according to the reading behavior characteristics of the target user, then the recharging strategy corresponding to the target recharging gear is recommended for the target user based on the predicted recharging intention and the target recharging gear of the target user, the problem that the recharging conversion result of a user accessing a recharging page is poor in the prior art is solved, the actual recharging behavior of the user is improved by providing a personalized recharging strategy for the target user, and the recharging conversion proportion of the recharging page is improved.
Fig. 2 is a flowchart illustrating another method for improving recharge conversion according to an embodiment of the present invention, and as a refinement and an expansion of the technical solution of the above embodiment, reference may be made to the description in the above embodiment for what is not described in detail in this embodiment. As shown in fig. 2, the method comprises the steps of:
step S201, predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user.
And step S202, predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user.
For example, the plurality of refill steps displayed on the current refill page include: k1, k2, k3, k4, k5 and k6, according to the reading behavior characteristics of the target user A, predicting the recharging probability of the user to the 6 recharging gears k1 to k6 as follows: 12%, 8%, 45%, 15%, 3%, 17%, the greater the recharging probability, the greater the frequency of selecting the corresponding recharging gear by the user. The recharging probability of the target user to each recharging gear on the recharging page is predicted by using the gear prediction model, so that the accuracy of determining the subsequent target recharging gear can be ensured, and the adoption rate of the recommended recharging strategy by the user is improved.
Optionally, the gear prediction model is a multi-classification model, and a training process of the gear prediction model includes:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by utilizing the reading behavior characteristics to obtain a multi-classifier which is used as a gear prediction model.
And step S203, determining a target recharging position of the target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold.
The probability threshold is adaptively set according to different users, and the recharging gear with the maximum recharging probability of the target user is determined as the target recharging gear according to the probability threshold. Continuing with the above example as an example, if the probability threshold is set to 40%, then the target top-up gear of the target user is determined to be k 3; if the probability threshold is set to 15%, the alternative recharging positions of the target user can be determined to comprise k3, k4 and k6, and at this time, the recharging position with the highest recharging probability in the multiple alternative recharging positions can be selected to be determined as the target recharging position.
And step S204, recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention of the target user.
If the probability of predicting that the target user currently carries out recharging is small, a recharging strategy with high preferential degree can be recommended for the user under the target recharging gear.
According to the technical scheme, the target recharging gear of the target user is determined by firstly predicting the recharging intention of the target user by using an intention prediction model and secondly predicting the recharging probability of the target user to each recharging gear on a recharging page by using a gear prediction model; and finally, recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention of the target user, solving the problem of poor recharging conversion result of the user accessing the recharging page in the prior art, and further improving the actual recharging behavior of the user and increasing the recharging conversion proportion of the recharging page by providing a personalized recharging strategy for the target user in a mode of predicting the recharging intention and the personalized target recharging gear.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.
As shown in fig. 3, the electronic device may include: a processor (processor)302, a communication Interface 304, a memory 306, and a communication bus 308.
Wherein:
the processor 302, communication interface 304, and memory 306 communicate with each other via a communication bus 308.
A communication interface 304 for communicating with network elements of other devices, such as clients or other electronic devices.
The processor 302 is configured to execute the program 310, and may specifically execute the relevant steps in the above-mentioned improvement method embodiment of recharge conversion.
In particular, program 310 may include program code comprising computer operating instructions.
The processor 302 may be a central processing unit CPU, or an application specific Integrated circuit (asic), or one or more Integrated circuits configured to implement an embodiment of the present invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 306 for storing a program 310. Memory 306 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 310 may specifically be configured to cause the processor 302 to perform the following operations:
predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user;
recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
In an alternative manner, the program 310 may specifically be further configured to cause the processor 302 to perform the following operations:
and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user.
In an alternative embodiment, where the intent prediction model is a binary model, the program 310 may be further specifically configured to cause the processor 302 to:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as the intention prediction model.
In an alternative manner, the program 310 may be further specifically configured to cause the processor 302 to perform the following operations:
predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user;
determining a target recharging position of a target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold value;
and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention.
In an alternative manner, where the gear prediction model is a multi-classification model, the program 310 may be further specifically configured to cause the processor 302 to:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by using the reading behavior characteristics to obtain a multi-classifier which is used as the gear prediction model.
In an alternative, the reading behavior characteristics include a current reading behavior characteristic and a historical reading behavior characteristic.
In an optional manner, the program 310 may be further specifically configured to cause the processor 302 to perform the following operations:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
The embodiment of the invention also provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the recharge conversion improving method in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user;
recommending a recharging strategy corresponding to the target recharging gear for the target user based on the predicted recharging intention and the target recharging gear of the target user, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
In an alternative, the executable instructions further cause the processor to:
and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user.
In an alternative, the intent prediction model is a binary model, the executable instructions further cause the processor to:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as the intention prediction model.
In an alternative, the executable instructions further cause the processor to:
predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user;
determining a target recharging position of a target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold value;
and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention.
In an alternative, the gear prediction model is a multi-classification model, the executable instructions further cause the processor to:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by using the reading behavior characteristics to obtain a multi-classifier which is used as the gear prediction model.
In an alternative, the reading behavior characteristics include a current reading behavior characteristic and a historical reading behavior characteristic.
In an alternative, the executable instructions further cause the processor to:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (18)
1. A method of increasing recharge conversion, the method comprising:
predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user;
predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user;
determining a target recharging position of a target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold value;
and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
2. The method of claim 1, wherein the method further comprises:
and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user.
3. The method of claim 1, wherein the intent prediction model is a binary model, and the training process of the intent prediction model comprises:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as the intention prediction model.
4. The method of claim 1, wherein the gear prediction model is a multi-classification model, and the training process of the gear prediction model comprises:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by using the reading behavior characteristics to obtain a multi-classifier which is used as the gear prediction model.
5. The method according to any one of claims 1-4, wherein the reading behavior characteristics include current reading behavior characteristics and historical reading behavior characteristics.
6. The method of claim 5, wherein before the predicting the target user's top-up intent using the intent prediction model based on the target user's reading behavior characteristics, the method further comprises:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
7. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to:
predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user;
predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user;
determining a target recharging position of a target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold value;
and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
8. The electronic device of claim 7, the executable instructions further cause the processor to:
and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user.
9. The electronic device of claim 7, wherein the intent prediction model is a binary model, the executable instructions further causing the processor to:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as the intention prediction model.
10. The electronic device of claim 7, wherein the gear prediction model is a multi-classification model, the executable instructions further causing the processor to:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by using the reading behavior characteristics to obtain a multi-classifier which is used as the gear prediction model.
11. The electronic device of any of claims 7-10, wherein the reading behavior characteristics include current reading behavior characteristics and historical reading behavior characteristics.
12. The electronic device of claim 11, the executable instructions further cause the processor to:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
13. A computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to:
predicting the recharging intention of the target user by using an intention prediction model according to the reading behavior characteristics of the target user;
predicting the recharging probability of the target user to each recharging gear on the recharging page by using a gear prediction model based on the reading behavior characteristics of the target user;
determining a target recharging position of a target user according to the recharging probability, wherein the recharging probability of the target recharging position is greater than or equal to a probability threshold value;
and recommending a recharging strategy corresponding to the target recharging gear for the target user based on the recharging intention, wherein the recharging strategy comprises a preferential mode corresponding to the target recharging gear set according to a preset rule.
14. The computer storage medium of claim 13, the executable instructions further causing the processor to:
and determining the target recharging gear of the target user according to the personal information and the historical recharging information of the target user.
15. The computer storage medium of claim 13, wherein the intent prediction model is a binary model, the executable instructions further causing the processor to:
acquiring reading behavior characteristics of a sample user;
distinguishing the reading behavior characteristics to obtain positive sample characteristics and negative sample characteristics, wherein the positive sample characteristics refer to characteristics related to successful recharging in the reading behavior characteristics, and the negative sample characteristics refer to characteristics related to unsuccessful recharging in the reading behavior characteristics;
and training by using the positive sample characteristics and the negative sample characteristics to obtain a two-classifier which is used as the intention prediction model.
16. The computer storage medium of claim 13, wherein the gear prediction model is a multi-classification model, the executable instructions further causing the processor to:
the method comprises the steps of obtaining reading behavior characteristics of a sample user, wherein the reading behavior characteristics comprise corresponding reading behavior characteristics when recharging is carried out at different recharging gears;
and training by using the reading behavior characteristics to obtain a multi-classifier which is used as the gear prediction model.
17. The computer storage medium of any of claims 13-16, wherein the reading behavior characteristics include current reading behavior characteristics and historical reading behavior characteristics.
18. The computer storage medium of claim 17, the executable instructions further causing the processor to:
detecting a current page of a reading application;
and if the current page is a recharging page, executing the prediction of the recharging intention.
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| CN112307308A (en) * | 2019-07-26 | 2021-02-02 | 腾讯科技（深圳）有限公司 | Data processing method, device, equipment and medium |
| CN112035548A (en) * | 2020-08-31 | 2020-12-04 | 北京嘀嘀无限科技发展有限公司 | Identification model acquisition method, identification method, device, equipment and medium |
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| US20140278994A1 (en) * | 2013-03-14 | 2014-09-18 | Richard J. Greenwood | Interactive Mobile Engagement Recharging Terminal System and Device |
| US9472965B2 (en) * | 2014-09-08 | 2016-10-18 | Google Technology Holdings LLC | Battery cycle life through smart overnight charging |
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