CN112104505A

CN112104505A - Application recommendation method and device, server and computer-readable storage medium

Info

Publication number: CN112104505A
Application number: CN202010996415.4A
Authority: CN
Inventors: 李婷婷
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd; Shenzhen Huantai Technology Co Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd; Shenzhen Huantai Technology Co Ltd
Priority date: 2020-09-21
Filing date: 2020-09-21
Publication date: 2020-12-18
Anticipated expiration: 2040-09-21
Also published as: CN112104505B

Abstract

The application discloses an application recommendation method, an application recommendation device, a server and a computer readable storage medium. The method comprises the following steps: receiving an acquisition request aiming at an application recommendation page sent by a target terminal, wherein the acquisition request carries a terminal identifier of the target terminal; according to the terminal identification, acquiring user characteristic data corresponding to a target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended, and acquiring a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data; and determining a target application program to be displayed in the application recommendation page and the position of the target application program in the application recommendation page based on the predicted download probability value corresponding to each application program, generating an application recommendation page based on the target application program and the position, and sending an application recommendation interface to a target terminal. By adopting the method, the flexibility of recommending the application program can be improved.

Description

Application recommendation method and device, server and computer-readable storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to an application recommendation method, an application recommendation apparatus, a server, and a computer-readable storage medium.

Background

With the popularization of intelligent terminals, various application programs are developed, and users can select required application programs to download and install through the application market of the intelligent terminals.

At present, a plurality of application programs to be recommended are generally manually sequenced according to a fixed sequence, then the plurality of application programs are manually configured at fixed positions of an application recommendation page according to the sequencing sequence, after a user opens an application market, an intelligent terminal displays the application recommendation page so as to recommend the plurality of application programs in the application recommendation page to the user, and the application programs can be game applications, video applications and the like. In this way, if a user is interested in a certain application recommended in the application recommendation page, the application will be downloaded.

However, in the application recommendation method, the download rate of the application program generated based on the application recommendation is low, and the recommendation flexibility of the application program needs to be improved.

Disclosure of Invention

The embodiment of the application recommendation method and device, the server and the computer readable storage medium can improve the flexibility of recommending the application program.

In a first aspect, an embodiment of the present application provides an application recommendation method, where the method includes:

receiving an acquisition request aiming at an application recommendation page sent by a target terminal, wherein the acquisition request carries a terminal identifier of the target terminal;

acquiring user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to the terminal identification, and acquiring a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data;

and determining a target application program to be displayed in an application recommendation page and the position of the target application program in the application recommendation page based on the predicted download probability value corresponding to each application program, generating the application recommendation page based on the target application program and the position, and sending the application recommendation interface to the target terminal.

In a second aspect, an embodiment of the present application provides an application recommendation apparatus, where the apparatus includes:

the system comprises a receiving module, a recommending module and a recommending module, wherein the receiving module is used for receiving an obtaining request aiming at an application recommending page sent by a target terminal, and the obtaining request carries a terminal identification of the target terminal;

the acquisition module is used for acquiring user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to the terminal identification, and acquiring a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data;

and the recommendation module is used for determining a target application program to be displayed in an application recommendation page and the position of the target application program in the application recommendation page based on the predicted download probability value corresponding to each application program, generating the application recommendation page based on the target application program and the position, and sending the application recommendation interface to the target terminal.

In a third aspect, an embodiment of the present application provides a server, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method according to the first aspect when executing the computer program.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method according to the first aspect as described above.

After receiving an acquisition request aiming at an application recommendation page sent by a target terminal, acquiring user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to a terminal identifier of the target terminal carried by the acquisition request, and then acquiring a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data, wherein the predicted download probability value of each application program can represent the possibility that each application program is downloaded by a user in the target terminal, so that the target application program to be displayed in the application recommendation page is determined based on the predicted download probability value corresponding to each application program, and the target application program can be recommended to the user in a targeted manner by combining the possibility that each application program is downloaded by the user in the target terminal, the download rate of the application program is improved. In addition, different positions of the application programs in the application recommendation page also have influence on whether the user downloads the application programs, so that the positions of the target application programs in the application recommendation page are determined based on the predicted download probability values of the application programs, the application recommendation page is generated based on the target application programs and the positions, and the application recommendation interface is sent to the target terminal. The method and the device for recommending the application program improve the recommendation flexibility of the application program.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a diagram of an application environment in which a recommendation method is applied in one embodiment;

FIG. 2 is a flow diagram of a method for application recommendation in one embodiment;

FIG. 3 is a diagram of an exemplary application recommendation page provided in one embodiment;

FIG. 4 is a flow diagram of a server adjusting model parameters of a download probability prediction model in one embodiment;

FIG. 5 is a flow chart of step 203 in one embodiment;

FIG. 6 is a flow diagram that illustrates a method for application recommendation, according to one embodiment;

FIG. 7 is a flow diagram that illustrates a method for application recommendation, in accordance with an embodiment;

FIG. 8 is a block diagram showing an example of the structure of an application recommendation apparatus;

fig. 9 is a schematic diagram of an internal configuration of a server in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In application recommendation in the conventional technology, generally, a plurality of application programs to be recommended are manually sequenced according to a fixed sequence, the sequence may be determined by different customer levels or promotion fees of application manufacturers, then the plurality of application programs are manually configured at fixed positions of an application recommendation page according to the sequencing sequence, and after a user opens an application market, an intelligent terminal displays the application recommendation page so as to recommend the plurality of application programs in the application recommendation page to the user.

Taking a game application as an example, within a set exposure range, for each user, the game application is currently manually configured at a fixed position of an application recommendation page, and other application programs, such as a video application, a social application, and the like, are recommended at other positions of the application recommendation page. Therefore, after each user opens the application recommendation page, the application programs and the positions of the application programs which can be seen by the user are the same, the recommendation mode is not targeted, the exposure of the game application is possibly large, but users who are interested in the game application and download the game application are few, the download rate of the game application is low, and the recommendation flexibility of the application programs needs to be improved.

In view of this, an embodiment of the present application provides an application recommendation method, in which after receiving an acquisition request for an application recommendation page sent by a target terminal, according to a terminal identifier of the target terminal carried by the acquisition request, user feature data corresponding to the target terminal, terminal feature data of the target terminal, and application feature data of each application program to be recommended are acquired, and then according to the user feature data, the terminal feature data, and the application feature data, predicted download probability values corresponding to the application programs are acquired, the predicted download probability values of the application programs can represent the possibility that each application program is downloaded by a user in the target terminal, so that the target application program to be displayed in the application recommendation page is determined based on the predicted download probability values corresponding to the application programs, and the target application program can be recommended to the user in a targeted manner in combination with the possibility that each application program is downloaded by the user in the target terminal, the download rate of the application program is improved. In addition, different positions of the application programs in the application recommendation page also have influence on whether the user downloads the application programs, so that the positions of the target application programs in the application recommendation page are determined based on the predicted download probability values of the application programs, the application recommendation page is generated based on the target application programs and the positions, and the application recommendation interface is sent to the target terminal. The method improves the recommendation flexibility of the application program.

In the following, a brief description will be given of an implementation environment related to the application recommendation method provided in the embodiment of the present application.

As shown in fig. 1, the implementation environment may include a server 101 and a plurality of terminals 102 (fig. 1 only exemplarily shows one target terminal 102, and the target terminal 102 may be any one of the plurality of terminals 102), and the target terminal 102 may communicate with the server 101 through a wired or wireless network.

The server 101 may be one server or a server cluster composed of a plurality of servers, where the server 101 may be a tower server, a rack server, a blade server, a high-density server, a single-path server, a dual-path server, or a multi-path server, and the type of the server 101 is not specifically limited in this embodiment of the present application. The target terminal 102 may be a personal computer, a notebook computer, a media player, a smart television, a smart phone, a tablet computer, a portable wearable device, and the like, and the type of the target terminal 102 is not particularly limited in this embodiment of the application.

FIG. 2 is a flow diagram of a method for application recommendation in one embodiment. The application recommendation method in this embodiment is described by taking the server running in fig. 1 as an example. As shown in fig. 2, the application recommendation method includes

steps

201, 202, and 203:

step 201, a server receives an acquisition request for an application recommendation page sent by a target terminal.

In this embodiment, the target terminal may be any user terminal, the application recommendation page may be a page that is presented to the user by the target terminal through an application recommendation program, and the application recommendation program may be, for example, a software store, an application market, and the like installed in the target terminal.

Taking an application recommendation program as an example of a software store, as an implementation manner, if a target terminal detects an operation of opening the software store by a user, or the target terminal detects an operation, such as a page refresh operation, a page turning operation, and the like, input by the user based on the software store, the target terminal sends an acquisition request for an application recommendation page, and a server receives the acquisition request for the application recommendation page sent by the target terminal. And the target terminal requests the server for the application recommendation page needing to be displayed in the target terminal through the acquisition request.

In this embodiment of the application, the obtaining request carries a terminal Identifier of the target terminal, and the terminal Identifier of the target terminal may be an IMEI (International Mobile Equipment Identity) of the target terminal or an OAID (Open Anonymous Identifier) of the target terminal, and the like, which is not limited specifically herein.

Step 202, the server obtains user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to the terminal identification, and obtains a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data.

In the embodiment of the application, the user characteristic data corresponding to the target terminal can represent the user attribute of the user, the historical behavior characteristic of the user based on the target terminal and other information. For example, the user characteristic data may include information of user age, user gender, and the like, and the user characteristic data may further include at least one of application download data, application click data, application browsing data, and application search data detected based on the target terminal. As an embodiment, the application downloading data may be the number of times that the user downloads the application of the same type as the application in a preset historical time period, the application clicking data may be the number of times that the user clicks the application of the same type as the application in the preset historical time period, the application browsing data may be the duration that the user browses the application of the same type as the application in the preset historical time period, and the application searching data may be the number of times that the user searches the application of the same type as the application in the preset historical time period.

The terminal characteristic data of the target terminal may characterize a terminal attribute of the target terminal, for example, the terminal characteristic data may include at least one of model data and network type data of the target terminal.

The application characteristic data of each application program to be recommended may characterize the application attribute of the corresponding application program, for example, the application characteristic data of each application program at least includes application level data of the application program, and the application characteristic data of each application program may also include application classification data, online date and other data of the corresponding application program.

In the embodiment of the application, the server recalls data resources from each data resource library, and after the recalling, the user characteristic data, the terminal characteristic data and the application characteristic data of each application program in the terminal are stored in the server in a way of corresponding to the terminal identifier of each terminal. The server can find the user characteristic data corresponding to the target terminal associated with the terminal identifier of the target terminal, the terminal characteristic data of the target terminal and the application characteristic data of each application program to be recommended according to the terminal identifier of the target terminal. And then, the server acquires the predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and the application characteristic data of each application program.

In a possible implementation manner, for each application program, the server may predict the user feature data, the terminal feature data, and the application feature data of the application program by using a trained neural network-based download probability prediction model, so as to obtain a predicted download probability value corresponding to the application program. In another possible implementation manner, for each application program, the server may further calculate a target hash value of the user characteristic data, the terminal characteristic data, and the application characteristic data of the application program, and then, the server searches a preset mapping table including mapping relationships between the hash values and the download probabilities for a target download probability corresponding to the target hash value, so as to obtain a predicted download probability value corresponding to the application program.

For an application, the predicted download probability value corresponding to the application may represent the likelihood that the application will be downloaded by the user in the target terminal. The user characteristic data corresponding to the target terminal, the terminal characteristic data of the target terminal and the application characteristic data of the application program all have certain relation with the possibility that the application program is downloaded by a user in the target terminal.

For example, taking the game application of which the application program is the first issue as an example, if the user age in the user characteristic data indicates that the user is a young person, then the probability that the game application is downloaded by the user in the target terminal is higher; if the application program downloading data, the application program clicking data, the application program browsing data and the application program searching data in the user characteristic data represent the application programs of the historical preference game class of the user, the probability that the game application is downloaded by the user in the target terminal is higher; if the terminal characteristic data of the target terminal represents that the model of the target terminal is a high-end machine, the probability that the game application is downloaded by a user in the target terminal and pays for the game application is higher; if the terminal characteristic data of the target terminal represents that the network of the target terminal is better, the probability that the game application is downloaded by the user in the target terminal is higher; if the application characteristic data of the game application indicates that the application level of the game application is high, for example, S level, the game application is highly likely to be downloaded by the user in the target terminal, and so on.

In view of the existing relationship, the server trains a download probability prediction model or establishes a mapping relationship between the hash values and the download probabilities, so that for each application program in the target terminal, the server can determine the possibility that the application program is downloaded by the user in the target terminal based on the user characteristic data, the terminal characteristic data and the application characteristic data of the application program.

Step 203, the server determines a target application program to be displayed in the application recommendation page and the position of the target application program in the application recommendation page based on the predicted download probability value corresponding to each application program, generates an application recommendation page based on the target application program and the position, and sends the application recommendation interface to the target terminal.

After the server acquires the predicted download probability value corresponding to each application program in the target terminal by adopting the embodiment, the server determines the target application program to be displayed in the application recommendation page from each application program according to the predicted download probability value corresponding to each application program.

In one possible implementation, the server may set a download probability threshold, compare the predicted download probability value of each application with the download probability threshold, and then determine the application with the predicted download probability value greater than the download probability threshold in each application as the target application. The download probability threshold may be set by itself when implemented. The target application program is the application program with the predicted downloading probability value being larger in each application program, so that the possibility that the target application program is downloaded by a user is higher, and the downloading amount of the target application program is favorably improved.

In another possible implementation, the server may also sort the application programs in the descending order of the predicted download probability values to obtain a sorting result, and then the server selects a preset number of application programs sorted in the top from the sorting result as target application programs, where the predicted download probability value of the application program sorted in the top is larger, so that the probability that the target application program is downloaded by the user is higher, which is beneficial to increasing the download volume of the target application program. The preset number may be the maximum number of the application programs that can be displayed in the application recommendation page, or the preset number may also be smaller than the maximum number, which is not limited herein.

After the server determines the target application programs to be displayed in the application recommendation page, the server determines the positions of the target application programs in the application recommendation page according to the predicted downloading probability values of the target application programs. It is understood that different positions of the application program in the application recommendation page may also affect whether the user downloads the application program, for example, the application program is shown in a position which is easy to attract the attention of the user in the application recommendation page, and then the application program is likely to be downloaded by the user. In the embodiment of the application, as an implementation manner, the server may rank the priorities of the positions in the application recommendation page, the position with the highest priority is most likely to attract the attention of the user, the server determines the position with the highest priority as the position of the target application with the largest predicted download probability value, determines the position with the second priority as the position of the target application with the second predicted download probability value, and the like, so that the target application is recommended to the user by combining with the position factor on the basis of the predicted download probability value, and the download rate of the application is further improved.

In this way, the server configures the target application program with the maximum predicted download probability value at the position with the highest priority, configures the target application program with the second predicted download probability value at the position with the second priority, and so on, the server generates an application recommendation page based on the target application program and the position, sends an application recommendation interface to the target terminal, and the target terminal displays the application recommendation interface, so that each target application program is displayed for the user according to the configuration of the server.

FIG. 3 is a diagram of an exemplary application recommendation page. As shown in fig. 3, the priorities of the positions of the applications in the application recommendation page are sequentially decreased from top to bottom, and the predicted download probability value of the target application "cloud recipe 2" in the target terminal is the largest, so that the server configures the "cloud recipe 2" in the first position of the application recommendation page. The predicted download probability values of the target application programs of 'tremble short video', 'Jingdong', 'tremble super-speed version' and 'Shikui Bin' are sequentially reduced, so that the 'tremble short video', 'Jingdong', 'tremble super-speed version' and 'Shikui Bin' are sequentially configured at the second-priority position, the third-priority position, the fourth-priority position and the fifth-priority position of the application recommendation page by the server and then displayed to the user.

The application recommendation method in this embodiment receives an acquisition request for an application recommendation page sent by a target terminal, acquires user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal, and application characteristic data of each application program to be recommended according to a terminal identifier of the target terminal carried by the acquisition request, and then acquires a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data, and each application characteristic data, wherein the predicted download probability value of each application program can represent the possibility that each application program is downloaded by a user in the target terminal, so that the target application program to be displayed in the application recommendation page is determined based on the predicted download probability value corresponding to each application program, and the target application program can be recommended to the user in a targeted manner in combination with the possibility that each application program is downloaded by the user in the target terminal, the download rate of the application program is improved. In addition, different positions of the application programs in the application recommendation page also have influence on whether the user downloads the application programs, so that the positions of the target application programs in the application recommendation page are determined based on the predicted download probability values of the application programs, the application recommendation page is generated based on the target application programs and the positions, and the application recommendation interface is sent to the target terminal. The method and the device for recommending the application program improve the recommendation flexibility of the application program.

For a game application initiated for the first time, experimental data show that, under the condition of a fixed exposure, compared with the conventional technology, the application recommendation method in the embodiment of the present application can improve the distribution efficiency by 76%, where the distribution efficiency refers to a proportion of the download amount of the game application in the exposure of the game application, and thus, the application recommendation method in the embodiment of the present application greatly improves the download rate of the application program.

In an embodiment, on the basis of the embodiment shown in fig. 2, this embodiment relates to a process how the server obtains the predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data, and each application characteristic data. The process may include step a 1:

and step A1, for each application program, the server splices the user characteristic data, the terminal characteristic data and the application characteristic data of the application program to obtain spliced characteristic data, and inputs the spliced characteristic data into a download probability prediction model to obtain a predicted download probability value of the application program.

In the embodiment of the application, after the server obtains the user characteristic data corresponding to the target terminal, the terminal characteristic data of the target terminal and the application characteristic data of each application program to be recommended, for each application program, the server splices the user characteristic data, the terminal characteristic data and the application characteristic data of the application program to obtain the splicing characteristic data of the application program.

As an embodiment, for each application, the server may perform hash processing on the user feature data, the terminal feature data, and the features in the application feature data of the application, and combine the hash value of each feature and the feature number corresponding to each feature to form the concatenation feature data of the application.

Illustratively, for an application, the user characteristic data comprises user age, user gender, application downloading data detected based on a target terminal, application clicking data, application browsing data and application searching data, the terminal characteristic data comprises model data and network type data of the target terminal, and the application characteristic data of the application comprises application grade data of the application; the data format of the splicing feature data of the application program is sign1: slot1, sign2: slot2.. wherein sign represents the hash value of each feature (age, user gender, application program downloading data, application program clicking data, application program browsing data, application program searching data, model data of a target terminal, network type data or application grade data), and slot represents the feature number of each feature, and the feature number can be set according to the feature sequence required by a downloading probability prediction model. After the server performs hash processing on each feature, according to the feature number of each feature, the hash value of the feature with the feature number of 1 is taken as sign1, the slot1 is 1, the hash value of the feature with the feature number of 2 is taken as sign2, the slot2 is 2, and so on, and the concatenation feature data of the application program is obtained.

And the server inputs the splicing characteristic data of the application program into the download probability prediction model to obtain the predicted download probability value of the application program.

In the embodiment of the application, as an implementation manner, the server may use a large amount of sample splicing characteristic data to train the neural network model in advance, in the model training process, the server supervises the download probability value corresponding to each sample splicing characteristic data, repeats iterative training, and obtains the download probability prediction model after the model converges. Similar to the splicing feature data, one sample splicing feature data is obtained by splicing the sample application feature data, the user feature data and the terminal feature data of one application program.

Therefore, the server can obtain the predicted downloading probability value of each application program in the target terminal, and carries out application recommendation based on the predicted downloading probability value of each application program.

In one possible embodiment, referring to fig. 4, fig. 4 relates to a process of how the server adjusts model parameters of the download probability prediction model. As shown in fig. 4, the process may include steps 401 and 402:

step 401, the server obtains training adjustment samples according to the download data of different terminals to different application programs.

The training adjustment sample comprises sample user characteristic data, sample terminal characteristic data, sample application characteristic data of an application program and a sample downloading probability value.

In the embodiment of the application, the server generates the application recommendation page based on the target application program and the position, and the target terminal displays the application recommendation interface after sending the application recommendation interface to the target terminal. Therefore, different terminals display corresponding application recommendation interfaces, and the terminals collect download data of application programs and send the download data to the server based on the displayed application recommendation interfaces.

And the server analyzes the download data sent by each terminal to obtain sample user characteristic data, sample terminal characteristic data and sample application characteristic data of each application program corresponding to the terminal. The server determines whether the terminals with the same sample user characteristic data, sample terminal characteristic data and sample application characteristic data download the corresponding application program by analyzing the download data sent by each terminal, so that the server can determine the sample download probability value of the sample user characteristic data, the sample terminal characteristic data and the sample application characteristic data for the application program. Similarly, the server may obtain sample user characteristic data, sample terminal characteristic data, sample application characteristic data of each terminal, and a sample download probability value of each terminal for each application program by analyzing the download data.

Step 402, the server adjusts model parameters of the download probability prediction model based on the training adjustment samples.

For each application program of each terminal, the server splices the sample user characteristic data of the terminal, the sample terminal characteristic data and the sample application characteristic data of the application program to obtain spliced characteristic data corresponding to the terminal and the application program; and the server inputs the spliced characteristic data corresponding to each application program and each terminal into the download probability prediction model, and adopts the sample download probability value corresponding to each application program in each terminal as supervision to iteratively train the download probability prediction model and adjust the model parameters of the download probability prediction model.

Therefore, the server is beneficial to improving the prediction accuracy of the download probability prediction model by adjusting the model parameters of the download probability prediction model, so that the accuracy of the predicted download probability value corresponding to each application program in the target terminal is improved, and the accuracy of application recommendation is favorably improved.

In an embodiment, on the basis of the embodiment shown in fig. 2, referring to fig. 5, this embodiment relates to a process of how the server determines the target application to be presented in the application recommendation page and the position of the target application in the application recommendation page based on the predicted download probability value corresponding to each application. As shown in fig. 5, step 203 may include step 2031, step 2032, and step 2033:

step 2031, the server obtains the predicted resource numerical value transfer quantity corresponding to each application program.

In the embodiment of the application, for each application program in the target terminal, the server obtains the predicted resource numerical value transfer quantity corresponding to the application program. As one embodiment, the server may determine the predicted number of resource value transfers for each application over a future period of time, such as 30 days in the future, 90 days in the future, etc., based on measured data for the application or paid data for applications at the same application level as the application.

For example, taking the application program as a game application as an example, the server determines the payment data of the game application in the internal test period of 30 days as the predicted resource value transfer amount corresponding to the game application.

Step 2032, for each application program, the server corrects the predicted download probability value of the application program according to the resource value transfer number of the application program, and obtains the first value of the application program.

The server corrects the predicted download probability value of the application program according to the resource value transfer number of the application program, for example, the server may multiply the resource value transfer number of the application program by the predicted download probability value of the application program to obtain the first value of the application program.

Step 2033, the server determines the target application to be shown in the application recommendation page and the position of the target application in the application recommendation page based on the first value of each application.

In an actual service scene, various factors are always required to be considered during application recommendation, and in the embodiment of the application, the target application program is recommended by combining the resource value transfer quantity of the application program on the basis of the predicted download probability of the application program, so that the data base of the application recommendation is enriched, the data dimension is increased, and the flexibility of the application recommendation is improved.

In one possible implementation of step 2033, step 2033 may include the following steps a, b, c, and d:

step a, the server obtains the conversion rate corresponding to each application program.

In the embodiment of the application, for each application program, the conversion rate is used for representing the proportion of the historical registration amount of the application program to the historical download amount of the application program. The server can count the historical download data and the historical registration data of each application program, and the ratio of the historical registration amount of the application program to the historical download amount of the application program is the conversion rate corresponding to the application program.

And b, for each application program, the server corrects the first value of the application program according to the conversion rate of the application program to obtain a second value of the application program.

The server performs the correction process on the first value of the application according to the conversion rate of the application, and may obtain the second value of the application by multiplying the conversion rate of the application and the first value of the application. In this way, the server obtains the second value of each application program in the target terminal.

And c, the server performs descending order arrangement on the second values of the application programs to obtain an ordering result.

And d, the server determines the target application program to be displayed in the application recommendation page from the application programs according to the sequencing result and determines the position of the target application program in the application recommendation page.

And the server performs descending arrangement on the second values of the application programs to obtain a sequencing result, and then selects a preset number of application programs in the top sequence from the sequencing result as target application programs. The server can configure the target application program with the maximum second value at the position with the highest priority of the application recommendation interface, configure the target application program with the second value at the position with the second priority, and so on, the server generates an application recommendation page and sends the application recommendation interface to the target terminal, and the target terminal displays the application recommendation interface.

In the embodiment of the application, the second value of the application program ranked at the top is larger, that is, the comprehensive recommendation score of the application program is larger, so that the target application program can be recommended to the user by combining factors (the predicted download probability value, the predicted resource value transfer quantity and the conversion rate) of multiple dimensions, and the rationality and the flexibility of application recommendation are improved.

In one embodiment, based on the embodiment shown in fig. 2, referring to fig. 6, the present embodiment relates to a process of how the server determines the application to be recommended. As shown in fig. 6, the application recommendation method of the present embodiment further includes step 204 and step 205:

in step 204, the server obtains a plurality of candidate applications and determines a recommended transformation value corresponding to each candidate application.

In the embodiment of the application, the server can obtain recommended conversion values respectively corresponding to each candidate application program in the target terminal, wherein the recommended conversion values comprise at least one of application per-person download data, application exposure and application resource numerical value transfer quantity.

As an embodiment, the server may determine the exposure of the application, the downloaded data of each terminal, the resource value transfer amount, and the like by querying the return log sent by each terminal, so as to obtain the downloaded data per application.

In step 205, the server screens out candidate applications whose recommendation conversion values do not reach a preset recommendation index value from the plurality of candidate applications, and uses the screened candidate applications as the applications to be recommended.

In the embodiment of the application, the preset recommended index value may be a preset per-application download index, an application exposure index and a resource numerical value transfer quantity index, and the preset recommended index value may be set by itself in implementation. If the recommendation conversion value of a certain application program reaches the preset recommendation index value, the application program does not need to be recommended, therefore, the server screens out candidate application programs of which the recommendation conversion values do not reach the preset recommendation index value from the multiple candidate application programs, and uses the screened candidate application programs as the application programs to be recommended, so that the application programs which do not need to be recommended can be prevented from being recommended to a user by mistake, and the business compliance of application recommendation is ensured.

Taking the case that the recommended conversion value comprises the per-user download data of the application, the application is the game application, if the recommended conversion value of the game application does not reach the per-user download index, the server determines the game application as the application to be recommended, and the server continues to recommend the game application in a targeted manner by combining the predicted download probability value of the game application, so as to improve the download amount of the game application.

Taking the recommended conversion value including the application exposure amount and the application program as an example, if the recommended conversion value of the game application does not reach the application exposure amount index, the server determines the game application as the application program to be recommended, and continues to recommend the game application in a targeted manner by combining with the predicted download probability value of the game application, so as to improve the exposure amount of the game application.

Taking the example that the recommended conversion value comprises the application resource numerical value transfer quantity and the application program is the game application, if the recommended conversion value of the game application does not reach the resource numerical value transfer quantity index, the server determines the game application as the application program to be recommended and continues to recommend the game application in a targeted manner by combining the predicted download probability value of the game application so as to improve the resource numerical value transfer quantity of the game application.

Therefore, the embodiment combines different business requirements (the downloading index of all the application persons, the application exposure index or the resource numerical value transfer quantity index), can realize flexible recommendation of the application program, and improves the recommendation flexibility of the application program.

Referring to fig. 7, a flowchart of another application recommendation method provided in an embodiment of the present application is shown, where the application recommendation method may be applied to a server in the implementation environment shown in fig. 1. As shown in fig. 7, the application recommendation method may include the steps of:

step 7001, the server receives an acquisition request aiming at the application recommendation page sent by the target terminal.

The obtaining request carries a terminal identifier of the target terminal.

Step 7002, the server acquires a plurality of candidate application programs and determines recommended conversion values corresponding to the candidate application programs respectively.

And the recommended conversion value comprises at least one of the per-user downloaded data, the application exposure and the application resource numerical value transfer quantity.

And 7003, screening out candidate application programs of which the recommendation conversion values do not reach preset recommendation index values from the plurality of candidate application programs by the server, and taking the screened candidate application programs as the application programs to be recommended.

7004, the server obtains user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to the terminal identification.

The user characteristic data comprises at least one of application program downloading data, application program clicking data, application program browsing data and application program searching data detected based on the target terminal; the terminal characteristic data comprises at least one of model data and network type data of the target terminal; the application characteristic data of each application program at least includes application level data of the application program.

And 7005, for each application program, splicing the user characteristic data, the terminal characteristic data and the application characteristic data of the application program by the server to obtain spliced characteristic data, and inputting the spliced characteristic data into the download probability prediction model to obtain the predicted download probability value of the application program.

7006, the server acquires the predicted resource numerical value transfer quantity corresponding to each application program; and for each application program, correcting the predicted downloading probability value of the application program according to the resource value transfer quantity of the application program to obtain a first value of the application program.

7007, the server acquires the conversion rate corresponding to each application program; for each application program, correcting the first value of the application program according to the conversion rate of the application program to obtain a second value of the application program; performing descending order arrangement on the second values of the application programs to obtain an ordering result; and according to the sequencing result, determining a target application program to be displayed in the application recommendation page from the application programs, and determining the position of the target application program in the application recommendation page.

The conversion rate is used for representing the proportion of the historical registration amount of the application program to the historical download amount of the application program.

And 7008, the server generates an application recommendation page based on the target application program and the position, and sends an application recommendation interface to the target terminal.

7009, the server obtains training adjustment samples according to the download data of different application programs by different terminals.

The training adjustment sample comprises sample user characteristic data, sample terminal characteristic data, sample application characteristic data of an application program and a sample download probability value;

step 7010, the server adjusts model parameters of the download probability prediction model based on the training adjustment samples.

It should be understood that although the various steps in the flow charts of fig. 2-7 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-7 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.

Fig. 8 is a block diagram of an application recommendation apparatus according to an embodiment. As shown in fig. 8, the application recommendation apparatus includes:

the system comprises a receiving module 10, a processing module and a display module, wherein the receiving module is used for receiving an acquisition request aiming at an application recommendation page sent by a target terminal, and the acquisition request carries a terminal identifier of the target terminal;

an obtaining module 20, configured to obtain, according to the terminal identifier, user feature data corresponding to the target terminal, terminal feature data of the target terminal, and application feature data of each application program to be recommended, and obtain, according to the user feature data, the terminal feature data, and each application feature data, a predicted download probability value corresponding to each application program;

and the recommendation module 30 is configured to determine, based on the predicted download probability value corresponding to each application program, a target application program to be displayed in an application recommendation page and a position of the target application program in the application recommendation page, generate the application recommendation page based on the target application program and the position, and send the application recommendation interface to the target terminal.

Optionally, the obtaining module 20 is specifically configured to, for each application program, splice the user feature data, the terminal feature data, and the application feature data of the application program to obtain spliced feature data, and input the spliced feature data into a download probability prediction model to obtain a predicted download probability value of the application program.

Optionally, the apparatus further comprises:

the system comprises a sample acquisition module, a training adjustment module and a training adjustment module, wherein the sample acquisition module is used for acquiring training adjustment samples according to download data of different terminals to different application programs, and the training adjustment samples comprise sample user characteristic data, sample terminal characteristic data, sample application characteristic data of the application programs and sample download probability values;

and the training module is used for adjusting the model parameters of the download probability prediction model based on the training adjustment samples.

Optionally, the user feature data includes at least one of application download data, application click data, application browsing data, and application search data detected based on the target terminal; the terminal characteristic data comprises at least one of model data and network type data of the target terminal; the application characteristic data of each of the application programs at least includes application level data of the application program.

Optionally, the recommending module 30 includes:

an obtaining unit, configured to obtain a predicted resource numerical value transfer quantity corresponding to each application program;

the correction unit is used for correcting the predicted downloading probability value of each application program according to the resource value transfer quantity of the application program to obtain a first value of the application program;

and the recommending unit is used for determining a target application program to be displayed in an application recommending page and the position of the target application program in the application recommending page based on the first value of each application program.

Optionally, the recommending unit is specifically configured to obtain a conversion rate corresponding to each application program, where the conversion rate is used to characterize a ratio of a historical registration amount of the application program to a historical download amount of the application program; for each application program, correcting the first value of the application program according to the conversion rate of the application program to obtain a second value of the application program; performing descending order arrangement on the second values of the application programs to obtain an ordering result; and according to the sequencing result, determining the target application program to be shown in the application recommendation page from the application programs, and determining the position of the target application program in the application recommendation page.

Optionally, the apparatus further comprises:

the screening module is used for obtaining a plurality of candidate application programs, determining a recommended conversion value corresponding to each candidate application program, screening the candidate application programs of which the recommended conversion values do not reach preset recommended index values from the candidate application programs, and taking the screened candidate application programs as the application programs to be recommended, wherein the recommended conversion values comprise at least one of application per-capita download data, application exposure and application resource numerical value transfer quantity.

For specific limitations of the application recommendation device, reference may be made to the above limitations of the application recommendation method, which are not described herein again. The modules in the application recommendation device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the server, and can also be stored in a memory in the server in a software form, so that the processor can call and execute the operations of the modules.

The division of each module in the application recommendation device is only used for illustration, and in other embodiments, the application recommendation device may be divided into different modules as needed to complete all or part of the functions of the application recommendation device.

Fig. 9 is a schematic diagram of an internal structure of a server (or a cloud, etc.) in one embodiment. As shown in fig. 9, the server includes a processor and a memory connected by a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing an application recommendation method provided in the following embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers. Those skilled in the art will appreciate that the architecture shown in fig. 9 is a block diagram of only a portion of the architecture associated with the subject application, and does not constitute a limitation on the servers to which the subject application applies, as a particular server may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment of the present application, there is provided a server comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:

receiving an acquisition request aiming at an application recommendation page sent by a target terminal, wherein the acquisition request carries a terminal identifier of the target terminal; acquiring user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal and application characteristic data of each application program to be recommended according to the terminal identification, and acquiring a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data; and determining a target application program to be displayed in an application recommendation page and the position of the target application program in the application recommendation page based on the predicted download probability value corresponding to each application program, generating the application recommendation page based on the target application program and the position, and sending the application recommendation interface to the target terminal.

In one embodiment, the processor, when executing the computer program, further performs the steps of:

and for each application program, splicing the user characteristic data, the terminal characteristic data and the application characteristic data of the application program to obtain spliced characteristic data, and inputting the spliced characteristic data into a download probability prediction model to obtain a predicted download probability value of the application program.

acquiring training adjustment samples according to download data of different application programs by different terminals, wherein the training adjustment samples comprise sample user characteristic data, sample terminal characteristic data, sample application characteristic data of the application programs and sample download probability values; and adjusting model parameters of the download probability prediction model based on the training adjustment samples.

In one embodiment, the user characteristic data comprises at least one of application download data, application click data, application browsing data and application search data detected based on the target terminal; the terminal characteristic data comprises at least one of model data and network type data of the target terminal; the application characteristic data of each of the application programs at least includes application level data of the application program.

obtaining the predicted resource numerical value transfer quantity corresponding to each application program; for each application program, correcting the predicted downloading probability value of the application program according to the resource numerical value transfer quantity of the application program to obtain a first value of the application program; and determining a target application program to be shown in an application recommendation page and the position of the target application program in the application recommendation page based on the first value of each application program.

obtaining a conversion rate corresponding to each application program, wherein the conversion rate is used for representing the proportion of the historical registration quantity of the application program to the historical download quantity of the application program; for each application program, correcting the first value of the application program according to the conversion rate of the application program to obtain a second value of the application program; performing descending order arrangement on the second values of the application programs to obtain an ordering result; and according to the sequencing result, determining the target application program to be shown in the application recommendation page from the application programs, and determining the position of the target application program in the application recommendation page.

acquiring a plurality of candidate application programs, and determining a recommended conversion value corresponding to each candidate application program, wherein the recommended conversion value comprises at least one of application per-person download data, application exposure and application resource numerical value transfer quantity; and screening out candidate application programs of which the recommendation conversion values do not reach preset recommendation index values from the plurality of candidate application programs, and taking the screened candidate application programs as the application programs to be recommended.

The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media embodying computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of:

In one embodiment, the computer-executable instructions, when executed by one or more processors, cause the processors to further perform the steps of:

Embodiments of the present application also provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the steps of:

In one embodiment, when run on a computer, causes the computer to further perform the steps of:

Any reference to memory, storage, database, or other medium used herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. An application recommendation method, characterized in that the method comprises:

2. The method of claim 1, wherein the obtaining a predicted download probability value corresponding to each application program according to the user characteristic data, the terminal characteristic data and each application characteristic data comprises:

3. The method of claim 2, further comprising:

acquiring training adjustment samples according to download data of different application programs by different terminals, wherein the training adjustment samples comprise sample user characteristic data, sample terminal characteristic data, sample application characteristic data of the application programs and sample download probability values;

and adjusting model parameters of the download probability prediction model based on the training adjustment samples.

4. The method of claim 1, wherein the user characteristic data comprises at least one of application download data, application click data, application browsing data, and application search data based on detection by the target terminal; the terminal characteristic data comprises at least one of model data and network type data of the target terminal; the application characteristic data of each of the application programs at least includes application level data of the application program.

5. The method of claim 1, wherein determining a target application to be presented in an application recommendation page and a location of the target application in the application recommendation page based on the predicted download probability value corresponding to each of the applications comprises:

obtaining the predicted resource numerical value transfer quantity corresponding to each application program;

for each application program, correcting the predicted downloading probability value of the application program according to the resource numerical value transfer quantity of the application program to obtain a first value of the application program;

and determining a target application program to be shown in an application recommendation page and the position of the target application program in the application recommendation page based on the first value of each application program.

6. The method of claim 5, wherein determining the target application to be exposed in the application recommendation page and the position of the target application in the application recommendation page based on the first value of each of the applications comprises:

obtaining a conversion rate corresponding to each application program, wherein the conversion rate is used for representing the proportion of the historical registration quantity of the application program to the historical download quantity of the application program;

for each application program, correcting the first value of the application program according to the conversion rate of the application program to obtain a second value of the application program;

performing descending order arrangement on the second values of the application programs to obtain an ordering result;

and according to the sequencing result, determining the target application program to be shown in the application recommendation page from the application programs, and determining the position of the target application program in the application recommendation page.

7. The method according to claim 1, wherein before the obtaining, according to the terminal identifier, user characteristic data corresponding to the target terminal, terminal characteristic data of the target terminal, and application characteristic data of each application program to be recommended, the method further comprises:

acquiring a plurality of candidate application programs, and determining a recommended conversion value corresponding to each candidate application program, wherein the recommended conversion value comprises at least one of application per-person download data, application exposure and application resource numerical value transfer quantity;

and screening out candidate application programs of which the recommendation conversion values do not reach preset recommendation index values from the plurality of candidate application programs, and taking the screened candidate application programs as the application programs to be recommended.

8. An application recommendation apparatus, characterized in that the apparatus comprises:

9. A server comprising a memory and a processor, the memory having stored thereon a computer program, wherein the computer program, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.