CN107729544A - A kind of method and apparatus for recommending application - Google Patents

A kind of method and apparatus for recommending application Download PDF

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Publication number
CN107729544A
CN107729544A CN201711055130.5A CN201711055130A CN107729544A CN 107729544 A CN107729544 A CN 107729544A CN 201711055130 A CN201711055130 A CN 201711055130A CN 107729544 A CN107729544 A CN 107729544A
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application
user
label
preset
resource library
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CN201711055130.5A
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CN107729544B (en
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潘岸腾
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Alibaba China Co Ltd
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Guangzhou Youshi Network Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Abstract

The invention provides a kind of method and apparatus for recommending application.This method includes：The probability of user installation intended application is obtained using the Forecasting Methodology of user installation intended application probability；A number of apply to the user is chosen based on the probability by predetermined manner to recommend.Wherein the Forecasting Methodology includes：It is mounted with that the accounting of the application with label i predicts that the user installs the accounting of the application with label i in the t+1 times using Markov process principle in the t times based on user；It has when calculating any a application in the preset application resource storehouse of the user installation at least one label and predict that application when same label be present between at least one label possessed by intended application in its preset application resource storehouse to be mounted is the Maximum-likelihood estimation probability of the intended application；The probability of intended application in the preset application resource storehouse of the user installation is predicted based on the Maximum-likelihood estimation probability and the prediction accounting.

Description

Application recommending method and device
Technical Field
The invention relates to the technical field of information processing, in particular to a method and a device for predicting the probability of installing a target application by a user and a method and a device for recommending an application based on the probability.
Background
With the development of the internet, the popularization of 3G and 4G mobile communication networks and the popularization of intelligent terminals, a large number of applications are generated. In order to better attract users and improve user experience, many APP stores or APP markets are developed, such as pea pods, PP assistants, APP markets developed by mobile phone manufacturers, and the like, and the APP stores or APP markets are originally designed to show users and provide downloads of various third-party application software (APP) suitable for smart phones, including but not limited to: the system comprises a system tool class, an office business class, a news reading class, a video playing class, a communication social class, a financial and financial class, a life and leisure class, an online shopping class, a game and entertainment class and the like. In order to attract more user attention and provide user loyalty, more new functions have been developed by the powerful application stores or application market providers, rather than just third party application software download functions.
Currently, basically, application stores or application market products have application recommendation functions, but the existing idea of application recommendation is to recommend popular applications based on counting the number of users who have downloaded and installed certain types of applications, and the existing recommendation method lacks personalization, for example, a game application a with a first popular ranking is not necessarily the game type that a user B likes to play, for example, a shooting game with a first popular ranking is not necessarily the favorite game type for many female users. Similarly, in many reading applications of the same type and audio/video playing applications of the same type, different users have their favorite personalized application products, which is not necessarily the application product with the highest installation amount. Therefore, if a new application recommendation method is developed to implement personalized recommendation for different users, the user will have a good experience when using the application store or application market product with the personalized recommendation application function, and the satisfaction of the user is improved.
In addition, the maintenance and re-development of various app stores or app markets for applications in the market requires capital investment, and app stores or app market providers can continue to provide better-experienced app store or app market products only if balance is achieved. And the income sources of the application store or application market products are mainly commercial promotion and various advertisement playing of third-party application software. In the process of commercial promotion of third-party application software such as game entertainment popular with users, how to accurately predict the probability of installing a certain third-party application by a user is very important for application stores or application market providers, which is closely related to the commercial promotion effect of the third-party application software and indirectly influences the income of the application stores or the product using the market. In order to maximize income and profit in limited resources (including user resources, application display resources and the like), it is one of the key elements to accurately predict the probability of a user installing a certain third-party application.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the probability of installing a target application by a user and a method and a device for recommending an application based on the probability of installing the target application so as to improve the problems.
A first embodiment of the present invention provides a method for predicting a probability of a user installing a target application, where the target application is from a preset application resource library, the method includes:
a) The method comprises the following steps Based on the actual proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t, predicting the proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t +1 by utilizing a Markov process principle;
b) The method comprises the following steps Calculating a ratio of the number of users, in a preset application resource library, who have installed target applications with at least 1 label predicted to be installed by the user u, to the number of users, in the preset application resource library, who have installed applications with at least 1 label identical to the label of the target application, and taking the ratio as a maximum likelihood estimation probability that an application is a target application predicted to be installed when the same label exists between at least 1 label that the user u has when installing any application in the preset application resource library and at least 1 label that the target application has in the preset application resource library predicted to be installed by the user u;
c) The method comprises the following steps And predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction proportion of the application with the label i in the preset application resource library installed by the user u at the time of t + 1.
Wherein, in the step A),
predicting the proportion of the applications with the labels i in the preset application resource library installed by the user u at t time by utilizing a Markov process principle based on the proportion of the applications with the labels i in the preset application resource library installed by the user u at t-1 time, the proportion of the applications with the labels a in the preset application resource library newly installed by the user u from t-1 time to t time and the proportion of the applications with the labels d in the preset application resource library uninstalled by the user u during the period;
calculating to obtain the required transition probability based on the obtained predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t and the obtained actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t;
and according to the obtained transition probability and the actual proportion of the application with the label i in the preset application resource library installed by the user u at the time t, predicting the proportion of the application with the label i in the preset application resource library installed by the user u at the time t +1 by utilizing a Markov process principle.
A second embodiment of the present invention provides an application recommendation method, where a probability that a user installs a target application in a preset application resource library is obtained according to the method described in the first embodiment or the method described in the combination of the first embodiment and the preferred embodiment, and a certain number of applications are selected in a preset manner based on the probability to recommend to the user u.
A third embodiment of the present invention provides an application recommendation method, wherein a probability that a user installs a target application in a preset application resource library is obtained according to the method described in the first embodiment or the method described in the combination of the first embodiment and the preferred embodiment, a product of the obtained probability that the user u installs the target application and a charged price of a downloaded application is used as an expected revenue for recommending the target application to the user u, and a certain number of applications are selected to be recommended to the user u in a preset manner based on the expected revenue.
Wherein the charged price for downloading the application comprises: the price charged for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application.
The fourth embodiment of the present invention further provides an apparatus for predicting a probability of a user installing a target application, where the target application is from a preset application resource library, the apparatus includes:
the application installation duty ratio prediction unit is used for predicting the duty ratio of the application with the label i in the preset application resource library installed by the user u at t +1 time by utilizing the Markov process principle based on the actual duty ratio of the application with the label i in the preset application resource library installed by the user u at t time;
a maximum likelihood estimation probability determination unit, configured to calculate a ratio of the number of users, in a preset application resource library, who have installed a target application with at least 1 label predicted to be installed by the user u, to the number of users, in the preset application resource library, who have installed an application with at least 1 label identical to a label that the target application has, where the ratio is used as a maximum likelihood estimation probability that the user u is the target application predicted to be installed when there is an identical label between at least 1 label that the user u has when installing any one application in the preset application resource library and at least 1 label that the target application has in the preset application resource library predicted to be installed;
and the installation target application probability prediction unit is used for predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction ratio of the application with the label i in the preset application resource library installed by the user u at the time t + 1.
Wherein the application installation proportion prediction unit further comprises:
a first prediction unit, configured to predict, by using a markov process principle, an occupancy rate of an application with a label i in a preset application resource library installed by a user u at t-1 time based on the occupancy rate of the application with the label i in the preset application resource library already installed by the user u at t-1 time, the occupancy rate of an application with a label a in the preset application resource library newly installed by the user u from t-1 time to t time, and the occupancy rate of an application with a label d from the preset application resource library uninstalled during the period;
and the transition probability obtaining unit is used for calculating and obtaining the required transition probability based on the obtained predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t and the obtained actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t.
The fifth embodiment of the present invention also provides an application recommendation apparatus, which includes:
the apparatus for predicting the probability of the user installing the target application according to the fourth embodiment or the combination of the fourth embodiment and the preferred embodiment thereof;
and the recommending unit is used for selecting a certain number of applications to recommend to the user according to a preset mode based on the probability of the target application in the preset application resource library installed by the user, which is obtained by the device for predicting the probability of the target application installed by the user.
The sixth embodiment of the present invention also provides an application recommendation apparatus, including:
the apparatus for predicting the probability of the user installing the target application according to the fourth embodiment or the combination of the fourth embodiment and the preferred embodiment thereof;
an expected profit obtaining unit configured to take a product of a probability that the user installs a target application in a preset application resource base, which is obtained by the user installation target application probability predicting unit, and a charged price of the downloaded application as an expected profit for recommending the target application to the user;
and the recommending unit is used for selecting a certain number of applications to recommend to the user according to a preset mode based on the expected income.
Wherein the charged price for downloading the application comprises: the price charged for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application.
The seventh embodiment of the present invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method as described above in the first embodiment or in combination with the preferred embodiment.
The eighth embodiment of the present invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method according to the second embodiment or the method according to the second embodiment in combination with the preferred embodiment.
The ninth embodiment of the present invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method as described in the third embodiment or the method as described in the third embodiment in combination with the preferred embodiments.
According to the method and the device for predicting the probability of installing the target application by the user, the probability that the user downloads and installs a certain APP by using an application store or an application market product is predicted by using the Markov process principle, the prediction accuracy is high, the method and the device are also favorable for more accurately recommending relevant applications to the user, the target application with high installation probability is recommended to a certain user, the purpose of recommending the application to the user in a personalized manner is further realized, and the good experience and satisfaction degree of the user are improved. And accurately recommending the implementation of the related application to the user, which is beneficial to the marketing promotion of the application store or the application market product, is beneficial to the commercial cooperation with the application software product of the third-party developer and various advertisement broadcasts, and when the provider of the application store or the application market product achieves balance through a commercial cooperation mode, the provider of the application store or the application market product is beneficial to the maintenance and the redevelopment of the application store or the application market product, thereby further improving the use experience of the user.
Drawings
Fig. 1 is a flowchart of a method for predicting a probability of a user installing a target application according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method for predicting a probability of a user installing a target application according to a second embodiment of the present invention;
fig. 3 is a flowchart of an application recommendation method according to a third embodiment of the present invention;
FIG. 4 is a flowchart of an application recommendation method according to a fourth embodiment of the present invention;
fig. 5 is a schematic block diagram of an apparatus for predicting a probability of a user installing a target application according to a fifth embodiment of the present invention;
fig. 6 is a schematic block diagram of an application installation proportion prediction unit according to a sixth embodiment of the present invention;
fig. 7 is a schematic block diagram of an application recommendation apparatus according to a seventh embodiment of the present invention;
fig. 8 is a schematic block diagram of an application recommendation apparatus according to an eighth embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention and the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
As mentioned in the background, many software companies provide users with APP stores or APP market products, such as pea pods or PP assistants, and respective APP market products, such as provided by watson corporation, and users using smart terminals (smartphones, tablets, etc.) are also becoming more accustomed to downloading and installing their own desired third party APP products through various APP stores or APP market products. The downloading and installation of the application can be realized by an application store or an application market APP installed on the terminal, so that the time, labor and convenience are saved. The service providers of the APP products in the APP stores or APP markets can pre-establish an application resource library, namely a preset application resource library, and a large number of various third-party applications which can be downloaded and installed by users are stored in the preset application resource library, wherein the various third-party applications include office business APPs, audio-video playing APPs, communication social APPs, online shopping APPs, game APPs and the like. Different users have different frequencies and numbers of downloading and installing third-party applications through APP products of APP stores or APP markets and APP products, some users may frequently download APP of game types, for example, download and install a new game application product every other week, some users may only download APP of office business types and APP of communication social types, the interval time may be very long, and it may be possible to download and install the APP once every few weeks or months; of course, if the downloading of the APP with the same upgrade is considered as downloading, it may happen within 1 or 2 weeks. When a user downloads and installs a third party application using a different APP store or APP marketplace APP product, the click behavior, download and installation behavior of the user will be logged on a server associated with the APP store or APP marketplace APP product used.
Based on the above, the applications related to the present invention all come from the preset application resource library, that is, the user-installed applications or the applications predicted to be installed by the present invention all refer to application resource libraries (preset application resource libraries) that are installed by downloading from APP product service providers and pre-established on backend servers through APP products of certain APP stores or APP markets installed on the smart terminals used by the users. And 1 or more labels of the application related to the invention are from a label library which is established by an application store or an application market APP product service provider used by a user in advance, namely a preset application label library. In addition, in the whole text, the user installation application or the target application refers to that the user installs a certain application APP product on the intelligent terminal used by the user, and the description will not bring misunderstanding to those skilled in the art.
Fig. 1 is a flowchart of a method for predicting a probability of a user installing a target application according to a first embodiment of the present invention. As shown in fig. 1, the method for predicting the probability of installing the target application by the user of the present invention includes the following steps:
s1: and predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t by utilizing a Markov process principle based on the occupation ratio of the application with the label i in the preset application resource library already installed by the user u at the time t-1, the occupation ratio of the application with the label a in the preset application resource library newly installed by the user u from the time t-1 to the time t and the occupation ratio of the application with the label d in the preset application resource library unloaded by the user u during the period.
The third party applications provided by the application store or application marketplace will typically have 1 or more tags from a set of tags preset at the time the application store or application marketplace was developed by the facilitator, i.e., the tag i is from a set of all tags in a library of preset application tags, which is well known to those skilled in the art and is not described in great detail herein.
According to the definition of the Markov process, the inventor generalizes:
next time state = current state + amount of roll-out, whereby the predicted fraction of applications with label i in the preset application repository installed by user u at time t is:
the sum of the occupancy of the application with the label i in the preset application repository already installed by the user u at time t-1 and the occupancy of the application with the label a in the preset application repository newly installed by the user u from time t-1 to time t is subtracted by the value after the occupancy of the application with the label d from the preset application repository unloaded during the period.
Here, the tag i, the tag a, and the tag d may be the same or different.
As introduced above, the number of applications with tag i in the preset application repository that user u has installed at time t-1, the number of applications with tag a in the preset application repository that user u has newly installed during time t-1 to time t, and the number of applications with tag d from the preset application repository that user u has uninstalled during time t-1 to time t may be obtained by reading the log record, the number of applications with tag i in the preset application repository that user u has installed at time t-1 is divided by the number of all third party applications that user u has installed on its smart terminal, the number of applications with tag a in the preset application repository that user u has installed on its smart terminal is divided by the number of applications with tag a in the obtained user u has installed on its smart terminal during time t-1 to time t, the number of applications with tag a in the preset application repository that user u has installed on its smart terminal is divided by the number of applications with tag a in the obtained user u has installed on its smart terminal, the number of applications with tag a in the obtained user u has uninstalled during time t-1 to time t-1, and the number of applications with tag a installed in the preset application repository during time t-1 to t-1, and the user u has uninstalled the preset application at time t-1 to time t-1.
Namely, the proportion of the applications with the labels i in the preset application resource library which is already installed by the user u at the time t-1 is the ratio of the number of the applications with the labels i in the preset application resource library which is already installed by the user u at the time t-1 to the number of all the applications installed by the user u; the proportion of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t is the ratio of the number of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t to the number of all the applications installed by the user u; the proportion of the applications with the labels d from the preset application resource library, which are unloaded by the user u from the time t-1 to the time t, is the ratio of the number of the applications with the labels d, which are unloaded by the user u from the preset application resource library from the time t-1 to the time t, to the number of all the applications installed by the user u.
S2: and calculating to obtain the required transition probability based on the obtained predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t and the obtained actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t.
Obtaining the predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t through the step S1; the number of the applications with the labels i in the preset application resource library installed by the user u at the time t is obtained by reading log records, and is divided by the number of all third-party applications installed by the user u on the intelligent terminal used by the user u, so that the actual proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t is obtained.
According to the definition of the markov process, the method for predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t can also adopt the following formula to calculate:
wherein:
i represents a set of all labels in a preset application label library, wherein I belongs to I, and j belongs to I;
p′ u,t,i representing the predicted occupation ratio of the application with the label i in a preset application resource library installed by the user u at t time;
p u,t-1,i representing the actual proportion of the application with the label i in the preset application resource library which is already installed by the user u at the time t-1;
q i,j to transition probabilities, it means that user u installs the application with label i in the preset application repositoryTransferring the occupation ratio to the user u to install the application occupation ratio quantity with the label j in the preset application resource library;
p u,t-1,j representing the actual proportion of the application with the label j in the preset application resource library which is installed by the user u at the time t-1;
q j,i and the transition probability represents the quantity of the application occupation ratio with the label j in the user u installation preset application resource library transferred to the application occupation ratio with the label i in the user u installation preset application resource library.
And the label i comes from the preset application label library.
Any known method may be used to calculate the required transition probability q i,j And q is j,i . For example, in a mastery paper "research on a solution method for a markov state transition probability matrix" published in 2013 by students of northeast agronomy, various methods for solving a transition probability matrix of a markov process are discussed in detail.
In addition, the embodiment of the invention provides a method for calculating the required transition probability by a gradient descent method.
With respect to the relationship between the transition probabilities of the markov process and the transition probability matrix, the transition probability matrix is a matrix in which the transition probabilities are arranged in a certain order, which is well known in the art and is not explained herein.
Let transition probability matrix { q i,j The objective loss function for I, j ∈ I } is:
wherein q = (q) 1,1 ,q 1,2 ,…)
Here, I represents a set of all tags in the preset application tag library, where I ∈ I;
u represents the set of all users installed with the same app store or app market product;
p′ u,t,i showing user u installs preset application resource library with label at time ti is a predicted occupation ratio of the application;
p u,t,i indicating that user u has installed the actual percentage of applications with label i in the preset application repository at time t.
A simple explanation is made here for the set U, i.e. a set of all users who have APP products of the same type, such as APP stores or APP markets, installed on the terminals used by the users. Of course, the set of all users counted here refers to a set of users installed with APP products such as APP stores or APP markets of the same type (which may be different versions), for example, a set of all users installed with PP helper products on the terminals used. Those skilled in the art will appreciate that the application store or application market product referred to in the embodiments described herein is an application store or application market product that uses or integrates the methods and apparatus provided by the present invention.
This objective loss function is used to represent the error between the actual and said predicted share of the application with label i that user u has installed in the preset pool of application resources at time t, with the goal of minimizing this error.
The target loss function is one with respect to q = (q) 1,1 ,q 1,2 8230in (a) convex function (the first derivative is a monotonic function), and the optimum q = (q) can be obtained by the gradient descent method 1,1 ,q 1,2 8230;).
Solving the equation by gradient descent method i,j The best estimation value of | I, j ∈ I } comprises the following specific steps:
step 1: randomly given a set of transition probability matrices between 0-1 q i,j I, j belongs to I, and q is set as (0) Initializing iteration step number k =0;
step 2: and (3) iterative calculation:
where θ is the number of steps in the iteration, here taken to be 0.001;
and step 3: judging whether convergence occurs:
Δf(q (k+1) )＝|f(q (k+1) )-f(q (k) )|
if | Δ f (q) (k+1) )-Δf(q (k) )|&If t, then q is returned (k+1) I.e. the estimated transition probability matrix, otherwise, go back to step 2 to continue the calculation, where α is a small value, which may be taken as α =0.1 · θ.
Therefore, the required transition probability can be calculated by adopting the gradient descent method introduced by the invention.
S3: and according to the obtained transition probability and the actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t, predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t +1 by utilizing the Markov process principle.
After the required transition probability is obtained, based on the obtained transition probability and the actual proportion of the application with the label i in the preset application resource library installed by the user u at the time t, the proportion of the application with the label i in the preset application resource library installed by the user u at the time t +1 can be predicted by utilizing the Markov process principle.
The method for predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at t +1 time by using the Markov process principle comprises the following steps:
wherein:
i represents a set of all labels in a preset application label library, wherein I belongs to I, and j belongs to I;
p′ u,t+1,i representing that the user u installs the occupation ratio of the application with the label i in the preset application resource library at the time of t + 1;
p u,t,i representing the actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t;
q i,j to move toA rate, which represents the amount of the application proportion of the user u with the label i in the installation preset application resource library transferred to the application proportion of the user u with the label j in the installation preset application resource library;
p u,t,j representing the actual occupation ratio of the application with the label j in the preset application resource library installed by the user u at the time t;
q j,i the transition probability represents the amount of the application proportion with the label j in the preset application resource library installed by the user u compared with the application proportion with the label i in the preset application resource library installed by the user u.
The application has a tag i from the library of preset application tags.
S4: and calculating the ratio of the number of users, in a preset application resource library, of target applications with at least 1 label predicted to be installed by the user u to the number of users, in the preset application resource library, of applications with at least 1 label identical to the label of the target application, and taking the ratio as the maximum likelihood estimation probability that the application is the target application predicted to be installed when the same label exists between at least 1 label of any application in the preset application resource library installed by the user u and at least 1 label of the target application in the preset application resource library predicted to be installed by the user u.
The purpose of this step is to calculate the maximum likelihood estimation probability that an application z is predicted to be installed when the user installs a certain application z in the preset application repository.
For example, assuming that any application b in the preset application resource library is a target application predicted to be installed by the user u, the target application b is set to have n tags,
by c b,j J =1,2, \ 8230;, n;
by x b Indicating that the target application b event is installed on the intelligent terminal used by the user;
with C b,j To representThe label c of the target application b is contained in 1 or more labels of any application in a preset application resource library installed by a user b,j Event (2), namely C b,j Indicating that the same event as the jth label of the target application b exists in 1 or more labels of any application in a preset application resource library installed by a user;
calculating a ratio of a number of users, who have installed a target application b having at least 1 tag in a preset application repository for a predetermined period of time, to a number of users, who have installed an application having at least 1 tag identical to the tag of the target application b in the preset application repository:
wherein:
InstAppNums(x b ) Indicating the number of users who have installed the target application b having at least 1 tag in the preset application repository for a predetermined period of time;
InstCategNums(C b,j ) Indicating the number of users having at least 1 application with the same tag as the target application b in the preset application repository installed during the predetermined period of time.
Here, the installation of an application having at least 1 tag identical to the tag of the target application b in the preset application repository means: when any application z in the preset application resource library is installed, the same label exists between 1 or more labels of the application z and 1 or more labels of the target application b, the same label may be 1 or more, and even the plurality of labels of the application z may include all the labels of the target application b. Apparently, instAppnums (x) b ) Is InstCategNums (C) b,j ) A subset of (a).
For example, assume that the target application b predicted to be installed by the user u is a bookmarking novel APP provided in a PP assistant, which has 2 tags: news reading and novels; when any user downloads and installs the palm reading APP through the PP assistant, the tags of the palm reading APP are as follows: novels and electronic books; the palm-reading APP installed has the same tag "novel" as the target application b has.
The obtained ratio is the probability that the installed application is the application b when the user installs the application having at least 1 label identical to the label of the application b in the preset application resource library. With P (x) b |C b,j ) When this probability is expressed, then
In addition, the predetermined period of time is only a predetermined period of time for calculating the ratio, and in practice, a suitable period of time may be selected according to the balance point between the required accuracy and the calculation amount, and may be, for example, but not limited to, 7 days, 15 days, 30 days, and the like.
The ratio P (x) to be obtained b |C b,j ) And the maximum likelihood estimation probability that an application y is the target application b predicted to be installed when the same label exists between at least 1 label of any application y in the preset application resource library installed by the certain user u and at least 1 label of the target application b in the preset application resource library predicted to be installed by the user u. Setting P for maximum likelihood estimation probability u (x b |C b,j ) Indicates that P (x) is to be b |C b,j ) As P u (x b |C b,j ) An estimate of (d).
S5: and predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction ratio of the application with the label i in the preset application resource library installed by the user u at the time of t + 1.
After obtaining the maximum likelihood estimation probability that the application is the target application predicted to be installed when the same label exists between at least 1 label of any application in the preset application resource library installed by the user u and at least 1 label of the target application in the preset application resource library predicted to be installed by the user u, and the prediction occupation ratio of the third-party application with the label i in the preset application resource library installed by the user u at t +1 time, the probability that the target application in the preset application resource library is installed by the user u can be predicted by using a total probability method.
The method for predicting the probability of the user u for installing the target application in the preset application resource library comprises the following steps:
wherein:
P u (x b ) Representing the probability of the user u installing the target application b in the preset application resource library;
n represents the number of labels of the target application b;
c b,j j =1,2, \ 8230;, n;
representing that the user u installs the proportion of the application with the jth label of the target application b in a preset application resource library at the time of t + 1;
P(x b |C b,j ) And the maximum likelihood estimation probability is represented, that is, the maximum likelihood estimation probability that an application is the predicted target application b to be installed when the same label exists between at least 1 label that the user has when installing any application in the preset application resource library and at least 1 label that the target application b in the preset application resource library to be installed by the user is predicted.
In addition, as described by way of example above, with x b Representing the user to install the target application b event, using C b,j Showing arbitrary in a user-installed pool of pre-set application resourcesThe label of one application has the same event as the jth label of the target application b.
About obtaining calculation P u (x b ) The deduction process of the formula (1) is as follows:
for any user u mentioned in the present invention, according to the total probability formula, the probability of the user u installing the target application b in the preset application resource library can be written as follows:
wherein:
P u (x b ) Representing the probability of the user u to install the target application b in the preset application resource library;
P u (C b,j ) Representing the probability that the user u installs the application containing at least 1 label of the target application b in a preset application resource library; in other words, the probability that the same event as the jth label of the target application b exists in 1 or more labels of any application installed in a preset application resource library by the user u is represented;
n represents the number of labels of the target application b;
P u (x b |C b,j ) And the maximum likelihood estimation probability is represented, namely, the maximum likelihood estimation probability that the application is the target application b predicted to be installed when the same label exists between at least 1 label of any application installed in the preset application resource library by the user u and at least 1 label of the target application b predicted to be installed by the user u in the preset application resource library.
With P (x) obtained above b |C b,j ) As P u (x b |C b,j ) Replacing the estimated value of (a);
by usingAs P u (C b,j ) Replacing the estimated value of (a) to obtain:
in a preferred embodiment, the time t referred to throughout the present invention may be in time units of days, weeks, months, preferably in units of weekly time, i.e. a week of 7 days. While t-1 time represents the previous time unit and t +1 time represents the next time unit.
Thus, according to the method for predicting the probability of the user installing the target application, which is described above, the probability of the user downloading and installing a certain APP application by using an application store or an application market product can be predicted by using the Markov process principle, and the prediction accuracy is high.
Although the above provides a specific implementation method for predicting the proportion of the application with the label i in the preset application resource library installed by the user u at t +1 time based on the actual proportion of the application with the label i in the preset application resource library installed by the user u at t time by using the markov process principle, as known by those skilled in the art, the markov process principle is proposed by russian mathematician markov in 1907, and through research and development for so many years, more derivative principles and implementation methods have been deduced, and all applications with the label i in the preset application resource library installed by the user u at t +1 time by using the markov process principle and the derivative principles thereof are suitable for the present invention.
To this end, fig. 2 is a flowchart of a method for predicting a probability of a user installing a target application according to a second embodiment of the present invention. As shown in fig. 2, the method for predicting the probability of installing the target application by the user of the present invention includes:
a) The method comprises the following steps Based on the actual proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t, predicting the proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t +1 by utilizing a Markov process principle;
b) The method comprises the following steps Calculating a ratio of the number of users, in a preset application resource library, who have installed target applications with at least 1 label predicted to be installed by the user u, to the number of users, in the preset application resource library, who have installed applications with at least 1 label identical to the label of the target application, and taking the ratio as a maximum likelihood estimation probability that an application is a target application predicted to be installed when the user u installs any one of the applications in the preset application resource library and the same label exists between at least 1 label of the application and at least 1 label of the target application in the preset application resource library predicted to be installed by the user u;
c) The method comprises the following steps And predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction ratio of the application with the label i in the preset application resource library installed by the user u at the time of t + 1.
The implementation process of step a here may adopt any method for predicting the proportion of applications with label i installed in the preset application resource library by the user u at time t +1 based on the well-known markov process principle and its derivatives.
Here, the implementation process of step B is completely the same as the implementation process of step S4 in the first embodiment, and is not described here again.
Here, the implementation process of step C is completely the same as the implementation process of step S5 in the first embodiment, and is not described here again.
Thus, according to the prediction method for the user installation target application probability, the probability that the user downloads and installs a certain APP application by using an application store or an application market product can be predicted, and the prediction accuracy is high.
Fig. 3 is a flowchart of an application recommendation method according to a third embodiment of the present invention; as shown in fig. 3, the application recommendation method of the present invention includes:
according to the method described in the first embodiment, or the method described in the combination of the first embodiment and the preferred embodiment thereof, or the method described in the second embodiment, the probability that the user u installs the target application in the preset application resource library is obtained, and based on the probability, a certain number of applications are selected in a preset manner to be recommended to the user u.
The step of selecting a certain number of applications to recommend to the user u according to the probability in a preset mode comprises the following steps: selecting a certain number of applications to recommend to a user u based on the probability from big to small; or randomly selecting a certain number of applications from the applications corresponding to the probability greater than or equal to the preset threshold value to recommend the applications to the user u.
And the user can know which applications are more interested by the user through the obtained installation target application probability, so that a certain number of applications are selected to be recommended to the user according to the installation target application probability in a preset mode. Preferably, a certain number of applications are selected from the sequence from large to small based on the probability to be recommended to the user. The specific number can be arbitrarily selected according to the actual application scenario, for example, the specific number may be 5 to 20, or 20 to 50, or may also be a greater number of applications, such as 100, 200, and the like. In addition, a threshold may be preset, and a certain number of applications may be randomly selected from the applications corresponding to the probability greater than or equal to the preset threshold to be recommended to the user, for example, 5 to 20, or 20 to 50, or a greater number of applications may be selected, such as 100, 200, and the like.
According to the application recommendation method provided by the invention, the probability of installing the target application by the user with high accuracy is used, and the target application with high installation probability is recommended to a certain user, so that the purpose of recommending the application to the user in a personalized manner is realized, and the good experience and satisfaction of the user are improved.
FIG. 4 is a flowchart of an application recommendation method according to a fourth embodiment of the present invention; as shown in fig. 4, the application recommendation method of the present invention includes:
according to the method described in the first embodiment, or the method described in the combination of the first embodiment and the preferred embodiment thereof, or according to the method described in the second embodiment, the probability that the user u installs the target application in the preset application resource library is obtained, the product of the obtained probability that the user u installs the target application and the charged price of the downloaded application is used as the expected revenue of recommending the target application to the user u, and a certain number of applications are selected to be recommended to the user u in a preset manner based on the expected revenue.
The method comprises the following steps of selecting a certain number of applications to recommend to a user u according to a preset mode based on the expected income, wherein the steps comprise: selecting a certain number of applications to recommend to a user u based on the expected income from big to small; or randomly selecting a certain number of applications from the applications corresponding to the expected income which is greater than or equal to the preset threshold value to recommend the applications to the user u.
Wherein, the charging price for downloading the application comprises 2 conditions that the charging price for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application. When the charge price for downloading each application is the same, the charge price is determined by a service provider who provides an application store or application market product; when the price charged for downloading each application is different, the price charged is determined by the agreement of the application store or the service provider of the application market product with the provider of the application or with the agreement of the advertiser who binds the application to play the advertisement.
The applications which are more interesting to the user can be known through the obtained expected income, and simultaneously, income profit maximization can be realized, so that a certain number of applications are selected to be recommended to the user according to a preset mode based on the expected income. Preferably, a certain number of applications are selected to be recommended to the user in the descending order based on the expected profit value. The specific number can be arbitrarily selected according to the actual application scenario, for example, the specific number may be 5 to 20, or 20 to 50, or may also be a greater number of applications, such as 100, 200, and the like. In addition, a threshold may be preset, and a certain number of applications may be randomly selected from the applications corresponding to the expected profit greater than or equal to the preset threshold to be recommended to the user, for example, 5 to 20, or 20 to 50, or a greater number of applications may be selected, such as 100, 200, and the like.
According to the application recommendation method provided by the invention, the probability of installing the target application by the user with high accuracy is used, and the target application with high installation probability is recommended to a certain user, so that the purpose of recommending the application to the user in a personalized manner is realized, and the good experience and satisfaction of the user are improved. In addition, the probability of installing the target application is multiplied by the unit price of the commercial download of the application, so that the expected income of the commercial application can be shown, the commercial application with high expected income is recommended to the user, or the commercial application with high expected income is placed in a high-quality position to preferentially show the advertisement bound with the commercial application, and the goal of maximizing income profit can be realized. In this way, it is advantageous for marketing of application stores or application market products, facilitating business collaboration with third party application software developers for commercial promotion of their application software products and various advertising, both from the perspective of accurately recommending relevant applications to users and from the perspective of maximizing revenue margins. When the providers of the application stores or the application market products achieve balance through the business cooperation mode, the maintenance and the re-development of the application stores or the application market products are facilitated, and therefore the use experience of the users is further improved.
Fig. 5 is a schematic block diagram of an apparatus for predicting a probability of a user installing a target application according to a fifth embodiment of the present invention. As shown in fig. 5, in the apparatus for predicting a probability of a user installing a target application, the target application being from a preset application resource library, the apparatus includes:
the application installation duty ratio prediction unit is used for predicting the duty ratio of the application with the label i in the preset application resource library installed by the user u at t +1 time by utilizing the Markov process principle based on the actual duty ratio of the application with the label i in the preset application resource library installed by the user u at t time;
a maximum likelihood estimation probability determination unit, configured to calculate a ratio between the number of users, in a preset application resource library, who have installed a target application with at least 1 tag predicted to be installed by the user u, and the number of users, in the preset application resource library, who have installed an application with at least 1 tag identical to the tag of the target application, and use the ratio as a maximum likelihood estimation probability that the application is the target application predicted to be installed when the user u installs any one application in the preset application resource library, and when there is an identical tag between at least 1 tag that the user u has when installing the application and at least 1 tag that the target application has in the preset application resource library predicted to be installed;
and the installation target application probability prediction unit is used for predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction proportion of the application with the label i in the preset application resource library installed by the user u at the time t + 1.
The method for predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at t +1 time by using the Markov process principle comprises the following steps:
wherein:
i represents a set of all labels in a preset application label library, wherein I belongs to I;
p′ u,t+1,i representing that the user u installs the occupation ratio of the application with the label i in the preset application resource library at the time of t + 1;
p u,t,i representing the actual occupation ratio of the application with the label i in a preset application resource library installed by the user u at t time;
q i,j a transition probability, which represents the amount of user u installing an application with label i to transition to user u installing an application with label j;
p u,t,j representing the actual occupation ratio of the application with the label j in the preset application resource library installed by the user u at the time t;
q j,i to transition probability, it means that user u installs application with label j in proportion to transition toThe user u installs the proportion of applications with tag i;
the label i comes from the preset application label library.
The method for predicting the probability of the user u installing the target application in the preset application resource library by the installation target application probability prediction unit comprises the following steps:
wherein:
P u (x b ) Representing the probability of the user u installing the target application b in the preset application resource library;
n represents the number of labels of the target application b;
c b,j j =1,2, \ 8230;, n;
representing the occupation ratio of the application with the jth label of the target application b in a preset application resource library installed by the user u at t +1 time;
P(x b |C b,j ) And the maximum likelihood estimation probability is represented, that is, the maximum likelihood estimation probability that an application is the predicted target application b to be installed when the same label exists between at least 1 label that the user has when installing any application in the preset application resource library and at least 1 label that the target application b in the preset application resource library to be installed is predicted.
It will be clear to those skilled in the art that, for the convenience and simplicity of description, the specific working process of the apparatus described above can be referred to the corresponding process of the implementation method described in the foregoing second embodiment, and the examples and related descriptions listed in the foregoing second embodiment are also applicable to the explanation of the working process of the apparatus, and will not be repeated here.
Just as the first embodiment described above provides a specific implementation method for predicting the occupation ratio of an application with a label i in a preset application resource library installed by a user u at t +1 time by using the markov process principle based on the actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at t time. In correspondence with the first embodiment, the sixth embodiment of the present invention also provides a description about a specific composition structure of the application installation proportion prediction unit.
Fig. 6 is a schematic block diagram of an application installation proportion prediction unit according to a sixth embodiment of the present invention. As illustrated in fig. 6, the application installation proportion prediction unit may include:
a first prediction unit, configured to predict, by using a markov process principle, an occupancy of an application with a label i in a preset application resource library installed by a user u at time t, based on an occupancy of an application with a label i in the preset application resource library already installed by the user u at time t-1, an occupancy of an application with a label a in a preset application resource library newly installed by the user u during a period from time t-1 to time t, and an occupancy of an application with a label d from the preset application resource library uninstalled during the period;
and the transition probability obtaining unit is used for calculating and obtaining the required transition probability based on the obtained predicted proportion of the application with the label i in the preset application resource base installed by the user u at the time t and the obtained actual proportion of the application with the label i in the preset application resource base installed by the user u at the time t.
In this way, the application installation duty prediction unit can predict the duty of the application with the label i in the preset application resource library installed by the user u at the time t +1 by using the markov process principle according to the obtained transition probability and the actual duty of the application with the label i in the preset application resource library installed by the user u at the time t.
In the first prediction unit, the proportion of the applications with the labels i in the preset application resource library which is already installed by the user u at the time t-1 is the ratio of the number of the applications with the labels i in the preset application resource library which is already installed by the user u at the time t-1 to the number of all the applications installed by the user u; the proportion of the applications with the labels a in the preset application resource library which is newly added and installed by the user u from the time t-1 to the time t is the ratio of the number of the applications with the labels a in the preset application resource library which is newly added and installed by the user u from the time t-1 to the time t to the number of all the applications installed by the user u; the proportion of the applications with the labels d unloaded by the user u from the time t-1 to the time t is the ratio of the obtained number of the applications with the labels d unloaded by the user u from the preset application resource library from the time t-1 to the time t to the number of all the applications installed by the user u.
The method for predicting the proportion of the applications with the labels i in the preset application resource library installed by the user u at the time t by the first prediction unit comprises the following steps:
the proportion of the application with the label i in the preset application resource library which is already installed by the user u at the time t-1 is added with the proportion of the application with the label a in the preset application resource library which is newly installed by the user u from the time t-1 to the time t, and then the proportion of the application with the label d from the preset application resource library which is unloaded in the period is subtracted.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the application installation duty prediction unit described above can refer to the corresponding process of the implementation method described in the foregoing first embodiment, and the example and the related description listed in the foregoing first embodiment are also applicable to explain the working process of the application installation duty prediction unit, and will not be described repeatedly herein.
Thus, according to the device for predicting the probability of the user installing the target application, which is described above, the probability that the user downloads and installs a certain APP by using an application store or an application market product can be predicted by using the Markov process principle, and the prediction accuracy is high.
Fig. 7 is a schematic block diagram of an application recommendation apparatus according to a seventh embodiment of the present invention. As shown in fig. 7, the application recommendation apparatus of the present invention includes:
the apparatus for predicting the probability of a user installing a target application according to the fifth embodiment or the fifth embodiment in combination with the preferred embodiment or the sixth embodiment thereof;
and the recommending unit is used for selecting a certain number of applications to recommend to the user according to a preset mode based on the probability of the target application in the preset application resource library installed by the user, which is obtained by the device for predicting the probability of the target application installed by the user.
Wherein selecting a number of applications in a preset manner based on the expected revenue comprises: selecting a certain number of applications from large to small based on the expected income; or randomly selecting a certain number of applications from the applications corresponding to the expected income greater than or equal to the preset threshold value.
And the user can know which applications are more interested by the user through the obtained installation target application probability, so that a certain number of applications are selected to be recommended to the user according to the installation target application probability in a preset mode. Preferably, a certain number of applications are selected from the large to the small based on the probability to recommend to the user. The specific number can be arbitrarily selected according to the actual application scenario, for example, the specific number may be 5 to 20, or 20 to 50, or may also be a greater number of applications, such as 100, 200, and the like. In addition, a threshold may be preset, and a certain number of applications may be randomly selected from the applications corresponding to the probability greater than or equal to the preset threshold to be recommended to the user, for example, 5 to 20, or 20 to 50, or a greater number of applications may be selected, such as 100, 200, and the like.
It will be clear to those skilled in the art that for the convenience and brevity of description, the specific operation of the apparatus described above can be realized by referring to the corresponding operation of the method described in the foregoing third embodiment, and the example and the related description listed in the foregoing third embodiment are also applicable to the operation of the apparatus, and will not be repeated here.
According to the application recommendation device provided by the invention, the probability of installing the target application by the user with high accuracy is used, and the target application with high installation probability is recommended to a certain user, so that the purpose of recommending the application to the user in a personalized manner is realized, and the good experience and satisfaction of the user are improved.
Fig. 8 is a schematic block diagram of an application recommendation apparatus according to an eighth embodiment of the present invention. As shown in fig. 8, the application recommendation device of the present invention includes:
the apparatus for predicting the probability of a user installing a target application according to the fifth embodiment or the fifth embodiment in combination with the preferred embodiment or the sixth embodiment thereof;
an expected profit obtaining unit configured to take a product of a probability that the user installs a target application in a preset application resource base, which is obtained by the user installation target application probability predicting unit, and a charged price of the downloaded application as an expected profit for recommending the target application to the user;
and the recommending unit is used for selecting a certain number of applications to recommend to the user according to a preset mode based on the expected income.
Wherein selecting a number of applications in a preset manner based on the expected revenue comprises: selecting a certain number of applications from large to small based on the expected income; or randomly selecting a certain number of applications from the applications corresponding to the expected income greater than or equal to the preset threshold value.
The charging price of the downloaded application comprises 2 conditions that the charging price for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application. When the charge price for downloading each application is the same, the charge price is determined by a service provider who provides an application store or application market product; when the price charged for downloading each application is different, the price charged is determined by the agreement of the application store or the service provider of the application market product with the provider of the application or with the agreement of the advertiser who binds the application to play the advertisement.
The applications which are more interesting to the user can be known through the obtained expected income, and simultaneously, income profit maximization can be realized, so that a certain number of applications are selected to be recommended to the user according to a preset mode based on the expected income. Preferably, a certain number of applications are selected from the largest to the smallest based on the expected profit value and recommended to the user. The specific number can be arbitrarily selected according to the actual application scenario, for example, the specific number may be 5 to 20, or 20 to 50, or may be a greater number of applications, such as 100, 200, and so on. In addition, a threshold may be preset, and a certain number of applications may be randomly selected from the applications corresponding to the expected profit greater than or equal to the preset threshold to be recommended to the user, for example, 5 to 20, or 20 to 50, or a greater number of applications may be selected, such as 100, 200, and the like.
It is clear to those skilled in the art that for the convenience and brevity of description, the specific working process of the above-described apparatus can be referred to the corresponding process of the implementation method described in the foregoing fourth embodiment, and the examples and related description listed in the foregoing fourth embodiment are also applicable to the working process of the apparatus, and will not be repeated herein.
According to the application recommendation device provided by the invention, the probability of installing the target application by the user with high accuracy is used, and the target application with high installation probability is recommended to a certain user, so that the purpose of recommending the application to the user in a personalized manner is realized, and the good experience and satisfaction degree of the user are improved. In addition, the probability of installing the target application is multiplied by the unit price of the commercial download of the application, so that the expected income of the commercial application can be shown, the commercial application with high expected income is recommended to the user, or the commercial application with high expected income is placed in a high-quality position to preferentially show the advertisement bound with the commercial application, and the goal of maximizing income profit can be realized. In this way, it is advantageous for marketing of application stores or application market products, facilitating business collaboration with third party application software developers for their commercial promotion of application software products and various advertising, both from the perspective of accurately recommending relevant applications to users and from the perspective of maximizing revenue margins. When the providers of the application stores or the application market products achieve balance through the business cooperation mode, the maintenance and the re-development of the application stores or the application market products are facilitated, and therefore the use experience of the users is further improved.
Embodiments of the present invention further provide a computer program product for executing the method for predicting the probability of the user installing the target application and a computer program product for executing the method for recommending an application, where the computer program product includes a computer-readable storage medium storing program codes, and instructions included in the program codes may be used to execute the method described in the foregoing method embodiments, and specific implementation of the method embodiments may be referred to, and is not described herein again.
To this end, the invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to carry out the method as described above in the first embodiment or in combination with the preferred embodiments thereof.
To this end, the invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method as described above in the second embodiment or in combination with the preferred embodiments thereof.
To this end, the invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to carry out the method according to the third embodiment or the method according to the third embodiment in combination with its preferred embodiments as described above.
To this end, the invention also provides a storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method as described above in the fourth embodiment or in combination with the preferred embodiments thereof.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a smart tablet, a smart phone, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or the like.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (24)

1. A method for predicting the probability of a user installing a target application, wherein the target application is from a preset application resource library, the method comprises the following steps:
a) The method comprises the following steps Based on the actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t, predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t +1 by utilizing the Markov process principle;
b) The method comprises the following steps Calculating a ratio of the number of users, in a preset application resource library, who have installed target applications with at least 1 label predicted to be installed by the user u, to the number of users, in the preset application resource library, who have installed applications with at least 1 label identical to the label of the target application, and taking the ratio as a maximum likelihood estimation probability that an application is a target application predicted to be installed when the user u installs any one of the applications in the preset application resource library and the same label exists between at least 1 label of the application and at least 1 label of the target application in the preset application resource library predicted to be installed by the user u;
c) The method comprises the following steps And predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction ratio of the application with the label i in the preset application resource library installed by the user u at the time of t + 1.
2. The method of claim 1, wherein in step a), the method for predicting the occupation ratio of the application with the label i installed in the preset application resource library by the user u at t +1 time by using markov process principle comprises:
wherein:
i represents a set of all labels in a preset application label library, wherein I belongs to I;
p′ u,t+1,i representing that the user u installs the occupation ratio of the application with the label i in the preset application resource library at the time of t + 1;
p u,t,i representing the actual occupation ratio of the application with the label i in a preset application resource library installed by the user u at t time;
q i,j a transition probability representing the amount of the user u installing the application proportion with the label i transitioning to the user u installing the application proportion with the label j;
p u,t,j representing the actual occupation ratio of the application with the label j in the preset application resource library installed by the user u at the time t;
q j,i a transition probability, which represents the amount by which user u installs the application having label j to transition to user u installs the application having label i;
and the label i comes from the preset application label library.
3. The method according to claim 1 or 2, characterized in that in step A),
predicting the occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t by utilizing a Markov process principle based on the occupation ratio of the application with the label i in the preset application resource library already installed by the user u at the time t-1, the occupation ratio of the application with the label a in the preset application resource library newly installed by the user u from the time t-1 to the time t and the occupation ratio of the application with the label d in the preset application resource library unloaded by the user u during the period;
calculating to obtain the required transition probability based on the obtained predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t and the obtained actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t;
and according to the obtained transition probability and the actual proportion of the application with the label i in the preset application resource library installed by the user u at the time t, predicting the proportion of the application with the label i in the preset application resource library installed by the user u at the time t +1 by utilizing a Markov process principle.
4. The method according to claim 3, wherein the proportion of the applications with labels i in the preset application resource library already installed by the user u at time t-1 is a ratio of the obtained number of the applications with labels i in the preset application resource library already installed by the user u at time t-1 to the number of all the applications installed by the user u; the proportion of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t is the ratio of the number of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t to the number of all the applications installed by the user u; the proportion of the applications with the labels d from the preset application resource library, which are unloaded by the user u from the time t-1 to the time t, is the ratio of the number of the applications with the labels d, which are unloaded by the user u from the preset application resource library from the time t-1 to the time t, to the number of all the applications installed by the user u.
5. The method of claim 3, wherein the predicted percentage of time t that the user u installs the application with tag i in the preset application resource library is:
the sum of the occupancy of the application with the label i in the preset application repository already installed by the user u at time t-1 and the occupancy of the application with the label a in the preset application repository newly installed by the user u from time t-1 to time t is subtracted by the value after the occupancy of the application with the label d from the preset application repository unloaded during the period.
6. The method of claim 2, wherein predicting the probability that user u installs the target application in the pre-configured application resource pool comprises:
wherein:
P u (x b ) Representing the probability of the user u to install the target application b in the preset application resource library;
n represents the number of labels of the target application b;
c b,j j =1,2, \8230;, n;
representing the occupation ratio of the application with the jth label of the target application b in a preset application resource library installed by the user u at t +1 time;
P(x b |C b,j ) And the maximum likelihood estimation probability is expressed, namely the maximum likelihood estimation probability of the application which is the target application b predicted to be installed when the same label exists between at least 1 label of any application in the preset application resource library and at least 1 label of the target application b predicted to be installed when the user installs the application.
7. An application recommendation method, characterized in that the method according to one of claims 1 to 6 obtains the probability of installing a target application in a preset application resource library by a user u, and selects a certain number of applications to recommend to the user u in a preset manner based on the probability.
8. The application recommendation method according to claim 7, wherein the step of selecting a certain number of applications to recommend to the user u in a preset manner based on the probability comprises: based on the probability, selecting a certain number of applications from large to small in sequence to recommend to a user u; or randomly selecting a certain number of applications from the applications corresponding to the probability greater than or equal to the preset threshold value to recommend the applications to the user u.
9. An application recommendation method, characterized in that the method according to any one of claims 1 to 6 obtains the probability of installing a target application in a preset application resource library by user u, takes the obtained product of the probability of installing the target application by user u and the charging price of downloading the application as an expected revenue for recommending the target application to user u, and selects a certain number of applications to recommend to user u in a preset manner based on the expected revenue.
10. The method of claim 9, wherein the step of selecting a number of applications to recommend to user u in a predetermined manner based on the expected revenue comprises: selecting a certain number of applications to recommend to a user u based on the expected income from big to small; or randomly selecting a certain number of applications from the applications corresponding to the expected income greater than or equal to the preset threshold value and recommending the applications to the user u.
11. The application recommendation method according to claim 9, wherein the charged price for downloading the application comprises: the price charged for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application.
12. A memory device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the method of any of claims 1 to 6.
13. A memory device having stored therein a plurality of instructions adapted to be loaded by a processor and to carry out the method of one of claims 7 to 11.
14. An apparatus for predicting a probability of a user installing a target application, wherein the target application is from a preset application resource library, the apparatus comprising:
the application installation duty ratio prediction unit is used for predicting the duty ratio of the application with the label i in the preset application resource library installed by the user u at t +1 time by utilizing a Markov process principle based on the actual duty ratio of the application with the label i in the preset application resource library installed by the user u at t time;
a maximum likelihood estimation probability determination unit, configured to calculate a ratio of the number of users, in a preset application resource library, who have installed a target application with at least 1 label predicted to be installed by the user u, to the number of users, in the preset application resource library, who have installed an application with at least 1 label identical to a label that the target application has, where the ratio is used as a maximum likelihood estimation probability that the user u is the target application predicted to be installed when there is an identical label between at least 1 label that the user u has when installing any one application in the preset application resource library and at least 1 label that the target application has in the preset application resource library predicted to be installed;
and the installation target application probability prediction unit is used for predicting the probability of the user u installing the target application in the preset application resource library by adopting a total probability method based on the maximum likelihood estimation probability and the prediction ratio of the application with the label i in the preset application resource library installed by the user u at the time t + 1.
15. The apparatus according to claim 14, wherein the method for the application installation duty prediction unit to predict the duty of the application with tag i in the preset application resource pool installed by the user u at time t +1 using markov process principle comprises:
wherein:
i represents a set of all labels in a preset application label library, wherein I belongs to I;
p′ u,t+1,i representing that the user u installs the occupation ratio of the application with the label i in the preset application resource library at the time of t + 1;
p u,t,i representing the actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t;
q i,j a transition probability, which represents the amount of user u installing an application with label i to transition to user u installing an application with label j;
p u,t,j representing the actual occupation ratio of the application with the label j in the preset application resource library installed by the user u at the time t;
q j,i a transition probability representing the amount of the user u installing the application proportion with the label j transitioning to the user u installing the application proportion with the label i;
the label i comes from the preset application label library.
16. The apparatus according to claim 15, wherein the application installation proportion prediction unit further comprises:
a first prediction unit, configured to predict, by using a markov process principle, an occupancy rate of an application with a label i in a preset application resource library installed by a user u at t-1 time based on the occupancy rate of the application with the label i in the preset application resource library already installed by the user u at t-1 time, the occupancy rate of an application with a label a in the preset application resource library newly installed by the user u from t-1 time to t time, and the occupancy rate of an application with a label d from the preset application resource library uninstalled during the period;
and the transition probability obtaining unit is used for calculating and obtaining the required transition probability based on the obtained predicted occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t and the obtained actual occupation ratio of the application with the label i in the preset application resource library installed by the user u at the time t.
17. The apparatus according to claim 16, wherein in the first prediction unit, the proportion of the applications with the label i in the preset application resource library already installed by the user u at time t-1 is the ratio of the obtained number of the applications with the label i in the preset application resource library already installed by the user u at time t-1 to the number of all the applications installed by the user u; the proportion of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t is the ratio of the number of the applications with the labels a in the preset application resource library newly installed by the user u from the time t-1 to the time t to the number of all the applications installed by the user u; the proportion of the applications with the labels d from the preset application resource library, which are unloaded by the user u from the time t-1 to the time t, is the ratio of the number of the applications with the labels d, which are unloaded by the user u from the preset application resource library from the time t-1 to the time t, to the number of all the applications installed by the user u.
18. The apparatus of claim 16 wherein the first prediction unit is configured to predict the fraction of applications with tag i installed by user u in the preset application repository at time t by:
the proportion of the application with the label i in the preset application resource library which is already installed by the user u at the time t-1 is added with the proportion of the application with the label a in the preset application resource library which is newly installed by the user u from the time t-1 to the time t, and then the proportion of the application with the label d from the preset application resource library which is unloaded in the period is subtracted.
19. The apparatus of claim 15, wherein the means for predicting the probability of the installation target application probability of the user u to install the target application in the preset application resource library comprises:
wherein:
P u (x b ) Representing the probability of the user u installing the target application b in the preset application resource library;
n represents the number of labels of the target application b;
c b,j j =1,2, \8230;, n;
representing that the user u installs the proportion of the application with the jth label of the target application b in a preset application resource library at the time of t + 1;
P(x b |C b,j ) And the maximum likelihood estimation probability is represented, that is, the maximum likelihood estimation probability that an application is the predicted target application b to be installed when the same label exists between at least 1 label that the user has when installing any application in the preset application resource library and at least 1 label that the target application b in the preset application resource library to be installed is predicted.
20. An application recommendation device, comprising:
means for predicting a probability of a user installing a target application according to one of claims 14-19;
and the recommending unit is used for selecting a certain number of applications to recommend the user according to a preset mode based on the probability of the target application in the user installation preset application resource library, which is obtained by the device for predicting the probability of the target application installed by the user.
21. The application recommendation device of claim 20, wherein selecting a number of applications in a predetermined manner based on the probability comprises: selecting a certain number of applications from large to small based on the expected income; or randomly selecting a certain number of applications from the applications corresponding to the expected income greater than or equal to the preset threshold value.
22. An application recommendation device, comprising:
means for predicting a probability of a user installing a target application according to one of claims 14-19;
an expected benefit obtaining unit configured to use a product of the probability of the user installing the target application in the preset application resource base, which is obtained by the apparatus for predicting the probability of the user installing the target application, and a charged price of downloading the application as an expected benefit for recommending the target application to the user;
and the recommending unit is used for selecting a certain number of applications to recommend to the user according to a preset mode based on the expected income.
23. The application recommendation device of claim 22, wherein selecting a number of applications in a preset manner based on the expected revenue comprises: selecting a certain number of applications from large to small based on the expected income; or randomly selecting a certain number of applications from the applications corresponding to the expected income greater than or equal to the preset threshold value.
24. The application recommendation device of claim 22, wherein the charged price for downloading an application comprises: the price charged for downloading each application is the same or different; when the charging price for downloading each application is different, the charging price for downloading the application is the charging price for downloading the target application.
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