CN107844554B - Application recommended method, device, terminal equipment and storage medium - Google Patents

Abstract

The embodiment of the application discloses an application recommendation method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring a target application list according to the target keyword; acquiring a pushing parameter of a first application in a target application list; determining a target pushing application according to the pushing parameters; and pushing the target push application to a user. According to the application recommendation method provided by the embodiment of the application, the target push application is determined according to the push parameter of the first application in the target application list, so that the recommendation accuracy of the application can be improved, and the downloading efficiency of the application can be improved.

Description

Application recommended method, device, terminal equipment and storage medium

technical field

The embodiments of the present application relate to the technical field of terminals and the Internet, and in particular, to an application recommendation method, apparatus, terminal device, and storage medium.

Background technique

At present, with the rapid development of Internet technology and the popularization of terminal devices, terminal devices such as smart phones and tablet computers have more and more functions. Users can realize different functions of terminal devices through the application programs provided by the terminal devices. With the development of various types of applications, in order to make new applications known to users, various application recommendation services emerge as the times require.

In the related art, when a user searches for an application through a keyword, some popular applications will appear in the recommendation list even if they are not associated with the keyword, resulting in a lower accuracy of the recommended application.

SUMMARY OF THE INVENTION

The embodiments of the present application provide an application recommendation method, apparatus, terminal device, and storage medium, which can improve the accuracy of application recommendation.

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

Obtain the target application list according to the target keyword;

obtaining the push parameters of the first application in the target application list;

Determine the target push application according to the push parameters;

Push the target push application to the user.

In a second aspect, an embodiment of the present application further provides an application recommendation device, the device comprising:

The target application list acquisition module is used to acquire the target application list according to the target keyword;

a push parameter acquisition module, used to obtain the push parameters of the first application in the target application list;

a target push application determination module, configured to determine a target push application according to the push parameters;

The target push application push module is used for pushing the target push application to the user.

In a third aspect, an embodiment of the present application further provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and running on the processor, where the processor implements the following when executing the computer program. The application recommendation method described in the first aspect.

In a fourth aspect, an embodiment of the present application further provides a storage medium on which a computer program is stored, and when the program is executed by a processor, implements the application recommendation method described in the first aspect.

In the embodiment of the present application, the target application list is obtained first according to the target keyword, then the push parameters of the first application in the target application list are obtained, the target push application is determined according to the push parameters, and finally the target push application is pushed to the user. In the application recommendation method provided by the embodiment of the present application, the target push application is determined according to the push parameter of the first application in the target application list, which can not only improve the accuracy of application recommendation, but also improve the download efficiency of the application.

Description of drawings

1 is a flowchart of an application recommendation method in an embodiment of the present application;

2 is a flowchart of another application recommendation method in the embodiment of the present application;

3 is a flowchart of another application recommendation method in an embodiment of the present application;

4 is a schematic structural diagram of an application recommendation device in an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a terminal device in an embodiment of the present application;

FIG. 6 is a schematic structural diagram of another terminal device in an embodiment of the present application.

Detailed ways

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, the drawings only show some but not all the structures related to the present application.

In the related art, when a user searches for an application according to a keyword, some popular applications will appear in the recommendation list of the application regardless of whether the popular application is related to the keyword due to the large number of related users, so that the application recommendation receives some popular application weights. The impact of this reduces the accuracy of application recommendations, thereby reducing the efficiency of users downloading applications.

FIG. 1 is a flowchart of an application recommending method provided by an embodiment of the present application. This embodiment is applicable to recommending an application program to a user. The method can be executed by an application recommending device, and the device can be integrated into a mobile phone or a tablet. computer and other terminals. As shown in Figure 1, the method includes the following steps.

Step 110: Acquire a target application list according to the target keyword.

Wherein, the target keyword may be a word determined according to attribute information such as function or classification when the user needs to install an application in order to enable the terminal device to realize a certain function, so as to search for the application in the application store according to the keyword. Exemplarily, assuming that the user wants to install an application that can beautify pictures, he or she can search for the application using "Meitu" as the target keyword. The target application list may be a list consisting of applications related to the target keyword and some popular applications.

Specifically, the process of obtaining the target application list according to the target keyword may be: after the user enters the target keyword in the search box of the application store, the application server searches the application library according to the target keyword, and compares the information contained in the name with the target keyword. Applications with similar keywords are extracted to form a target application list. Or traverse the tags of applications in the application library, and extract applications with similar information to the target keyword in the tags to form a target application list. Or combine the two methods, that is, first extract applications whose names contain information similar to the target keyword, then traverse the tags of the remaining applications according to the target keyword, and then extract the applications with similar information to the target keyword in the tags. Finally, the applications obtained twice are combined to form a target application list.

Step 120: Acquire the push parameters of the first application in the target application list.

The push parameter may be a parameter used to reflect whether the first application is pushed. Specifically, the push parameter may be a number between 0 and 1. In this embodiment, the recommended parameter may be calculated by using a term frequency-inverse document frequency (term frequency-inverse document frequency, TF-IDF) algorithm. The TF-IDF algorithm is a weighting technique used for information retrieval and data mining. The basic idea of the algorithm is that if a word appears more frequently in one article and less frequently in other articles, then The word or phrase with the word is considered to be class-distinguishing and suitable for classification.

In this embodiment, acquiring the push parameters of the first application in the target application list may be acquiring the push parameters of each first application in the target application list. Optionally, the method of acquiring the push parameters of the first application in the target application list may be: firstly calculating the term frequency (term frequency, TF) of the first application, and then calculating the inverse document frequency (inverse document frequency, IDF) of the first application. , and finally calculate the push parameters of the first application according to the word frequency and the reverse file frequency. The word frequency is the ratio of the historical download times of the first application to the historical download times of the applications used in the application library.

The number of downloads of the application can be obtained by statistics on the server where the application store is located. When the first application is downloaded, the server accumulates the number of downloads of the first application, so that the current data can accurately reflect the history of the first application being downloaded. frequency. The server accumulates the downloaded times of all applications in the application library to obtain the total downloaded times. The word frequency is the ratio of the historical download times of the first application to the historical download times of the applications used in the application library. Exemplarily, assuming that the number of historical downloads of the first application A is 300,000, and the number of historical downloads of all applications in the application library is 50 million, the word frequency of the first application A is 30/5000=0.006.

In this embodiment, the file reverse frequency may be calculated and obtained from the total number of applications in the application library and the number of times the first application appears in other application lists.

Optionally, the way of calculating the reverse file frequency of the first application may be: according to the total number of applications in the application library and the number of times the first application appears in other application lists, the reverse file frequency is calculated, wherein the other application list is the key except the target key. List of applications corresponding to keywords other than words. Correspondingly, the method of calculating the push parameters of the first application according to the word frequency and the reverse file frequency may be to multiply the word frequency and the reverse file frequency to obtain the push parameters of the first application.

Wherein, the process of calculating the reverse file frequency according to the total number of applications in the application library and the number of times the first application appears in other application lists may be as follows: first, divide the total number of applications in the application library by the number of times the first application appears in other application lists. times, and then take the logarithm of the obtained quotient to obtain the reverse file frequency of the first application. Assuming that the total number of applications in the application library is m, and the number of times the first application appears in other application lists is n, the formula for calculating the reverse file frequency of the first application is: reverse file frequency=log(m/n).

The push parameter is obtained by multiplying the word frequency and the reverse file frequency, and the calculation formula of the push parameter is: push parameter = word frequency * reverse file frequency.

Exemplarily, the calculation process of the push parameter of the first application is as follows. Assuming that the number of historical downloads of the first application is 100,000, the number of historical downloads of all applications in the application library is 50 million, the total number of applications in the application library is 90,000, and the number of times the first application appears in the list of other applications is 1000 times. Then the word frequency of the first application is 10/5000=0.002, the reverse file frequency is log(90000/1000)=1.9542, and the push parameter of the first application is 0.002*1.9542=0.0039.

Step 130: Determine the target push application according to the push parameters.

The target push application is the first application pushed to the user. In this embodiment, the larger the value of the push parameter, the more recommendable the first application is. Specifically, the method of determining the target push application according to the push parameters may be: determining the first application whose push parameters meet the preset requirements as the target push application. The preset requirement may be a threshold determined according to the push parameters of all the first applications in the target application list, or a preset number of first applications arranged in descending order of the push parameters.

Step 140: Push the target push application to the user.

Specifically, after the target push application is determined, the terminal device pushes the target push application to the interface where the application store is currently located for the user to select.

The technical solution of this embodiment firstly obtains the target application list according to the target keyword, then obtains the push parameters of the first application in the target application list, then determines the target push application according to the push parameters, and finally pushes the target push application to the user. In the application recommendation method provided by the embodiment of the present application, the target push application is determined according to the push parameter of the first application in the target application list, which can not only improve the accuracy of application recommendation, but also improve the download efficiency of the application.

Optionally, determining the target push application according to the push parameters may be implemented in the following manner: determining the push sequence of the first applications according to the push parameters; and determining a preset number of first applications ahead of the push sequence as the target push applications.

Specifically, the manner of determining the push sequence of the first applications according to the push parameters may be to sort the first applications in descending order of the push parameters. Exemplarily, the push parameters of the first application in the target application list are 0.02 for the first application A, 0.0056 for the first application B, 0.036 for the first application C, 0.004 for the first application D, and 0.012 for the first application E. , the sorting results in descending order of the push parameters are: first application C, first application A, first application E, first application B, and first application D.

The preset number may be set by the server of the application store, and may be set to any value between 3 and 8. Optionally, if the number of first applications is greater than the preset number, the preset number of related applications in the first push order will be pushed to the user; if the number of first applications is less than the preset number, the current number of first applications will be pushed Apps are recommended to users. Exemplarily, assuming that the preset number is set to 4 and the number of first applications is 8, the top 4 first applications are determined as the target push applications. When the number of the first applications is three, the three first applications are all determined as target push applications. The advantage of pushing a preset number of related applications to the user is that it can not only achieve the role of application recommendation, but also save the current recommendation resource space.

In the technical solution of this embodiment, the push sequence of the first applications is determined according to the push parameters, and the preset number of first applications with the push sequence ahead are determined as the target push applications. The most recommended first application in the target application list can be pushed to the user, which not only improves the accuracy of application recommendation, but also improves the efficiency of the user's search for applications.

Optionally, determining the target push application according to the push parameters may also be implemented in the following manner: calculating the average value of the push parameters of the first application in the target application list; obtaining the first application whose push parameters are greater than the average value, and determining it as the second application. ; Determine the second application as the target push application.

Specifically, after obtaining the push parameters of all the first applications in the target application list, the push parameters of all the first applications are weighted and averaged, and then the push parameters of each first application are compared with the average value, and the push parameters are extracted. The first application whose parameter is greater than the average value is determined as the second application, and finally the extracted second application is determined as the target push application to be pushed to the user. Exemplarily, the push parameters of the first application in the target application list are 0.02 for the first application A, 0.0056 for the first application B, 0.036 for the first application C, 0.004 for the first application D, and 0.012 for the first application E. , the average value of all the first application push parameters is 0.01552, then the first application whose push parameter is greater than the average value includes the first application A and the first application C, then the first application A and the first application C are determined as the second application , and the first application A and the first application C are determined as target push applications to be pushed to the user.

Optionally, the manner of determining the second application as the target push application for pushing may be to sort the second applications according to the push parameters in descending order, and push the sorted second applications to the user.

Optionally, determining the second application as the target push application may also be implemented in the following manner: if the number of the second applications is greater than the preset number, the preset number of second applications is determined as the target push application.

The preset number may be set by the server of the application store, and may be set to any value between 3 and 8. The preset number of second applications may be randomly selected, or a preset number of second applications with a larger push parameter may be pushed to the user. Determining a preset number of second applications as the target push application has the advantage of not only achieving the role of application recommendation, but also saving the current recommendation resource space.

Optionally, FIG. 2 is a flowchart of another application recommendation method provided by an embodiment of the present application. As shown in Figure 2, the method includes the following steps.

Step 210: Acquire a target application list according to the target keyword.

Step 220: Acquire the push parameters of the first application in the target application list.

Step 230: Obtain the current geographic location information of the user.

Specifically, the method of obtaining the current geographic location information of the user may be that the global positioning system (Global Positioning System, GPS) in the customs clearance terminal device locates the current location of the user, and obtains the current geographic location of the user. information.

Step 240: Determine a target push application according to the geographic location information and push parameters.

Specifically, after obtaining the current geographic location information of the user, use the geographic location information to traverse the tags of the first application in the target application list, and determine whether there is a tag similar to the geographic location information, and the first application corresponding to the Apps are pushed to users first. Exemplarily, assuming that the current geographic location information of the user is a certain food court, then the tag representing the geographic location information may be a tag related to food or movies, and the first application with the tag representing the geographic location information will be. Preferably pushed to the user. The remaining first applications are pushed according to the size order of the push parameters.

Step 250: Push the target push application to the user.

In the technical solution of this embodiment, the target push application is determined according to the geographic location information and the push parameters, and the geographic location information is used as a condition for determining the push application, which can improve the accuracy of application recommendation.

Optionally, FIG. 3 is a flowchart of another application recommendation method provided by an embodiment of the present application. As a further explanation for the above embodiment, as shown in FIG. 3 , the method includes the following steps.

Step 310: Acquire a target application list according to the target keyword.

Step 320: Calculate the word frequency of the first application, where the word frequency is the ratio of the historical download times of the first application to the historical download times of the applications used in the application library.

Step 330: Calculate the reverse file frequency according to the total number of applications in the application library and the number of times the first application appears in other application lists.

Step 340: Multiply the word frequency and the reverse file frequency to obtain the push parameters of the first application.

Step 350: Determine the push sequence of the first application according to the push parameters.

Step 360: Determine a preset number of first applications ahead of the push sequence as the target push application.

Step 370, push the target push application to the user.

In the technical solution of this embodiment, the target application list is obtained according to the target keyword, the word frequency of the first application is calculated, the reverse file frequency is calculated according to the total number of applications in the application library and the number of times the first application appears in other application lists, and the word frequency is calculated with The push parameters of the first application are obtained by multiplying the reverse file frequencies, the push sequence of the first application is determined according to the push parameters, the preset number of first applications with the first push sequence are determined as the target push application, and the target push application is pushed to users. It can improve the accuracy of application recommendation.

FIG. 4 is a schematic structural diagram of an application recommendation apparatus provided by an embodiment of the present application. As shown in FIG. 4 , the apparatus includes: a target application list acquisition module 410 , a push parameter acquisition module 420 , a target push application determination module 430 and a target push application push module 440 .

A target application list obtaining module 410, configured to obtain a target application list according to the target keyword;

a push parameter obtaining module 420, configured to obtain push parameters of the first application in the target application list;

a target push application determining module 430, configured to determine a target push application according to the push parameters;

The target push application push module 440 is configured to push the target push application to the user.

Optionally, the push parameter acquisition module 420 is further used for:

Calculate the word frequency of the first application, where the word frequency is the ratio of the number of historical downloads of the first application to the number of historical downloads of the applications used in the application library;

Calculate the reverse file frequency of the first application;

The push parameters of the first application are calculated according to the word frequency and the reverse file frequency.

Optionally, the push parameter acquisition module 420 is further used for:

Calculate the reverse file frequency according to the total number of applications in the application library and the number of times the first application appears in other application lists, where the other application lists are the application lists corresponding to other keywords except the target keyword;

Correspondingly, the push parameters of the first application are calculated according to the word frequency and the reverse file frequency, including:

The push parameters of the first application are obtained by multiplying the word frequency and the reverse file frequency.

Optionally, the target push application determination module 430 is further configured to:

Determine the push sequence of the first application according to the push parameters;

A preset number of first applications ahead of the push sequence are determined as the target push application.

Optionally, the target push application determination module 430 is further configured to:

Calculate the average value of the push parameters of the first application in the target application list;

Obtain the first application whose push parameter is greater than the average value, and determine it as the second application;

The second application is determined as the target push application.

Optionally, the target push application determination module 430 is further configured to:

If the number of second applications is greater than the preset number, the preset number of second applications is determined as the target push application.

Optionally, also include:

A geographic location information acquisition module, used to acquire the current geographic location information of the user;

Correspondingly, the target push application determination module 430 is further configured to:

Determine the target push application according to the geographic location information and push parameters.

FIG. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in FIG. 5, the terminal device 500 includes a memory 501 and a processor 502, wherein the processor 502 is configured to perform the following steps:

Obtain the target application list according to the target keyword;

Obtain the push parameters of the first application in the target application list;

Determine the target push application according to the push parameters;

Push the target push application to the user.

FIG. 6 is a schematic structural diagram of another terminal device provided by an embodiment of the present application. As shown in FIG. 6 , the terminal may include: a casing (not shown in the figure), a memory 601 , a central processing unit (Central Processing Unit, CPU) 602 (also called a processor, hereinafter referred to as CPU), a storage device stored in the memory 601 A computer program, a circuit board (not shown) and a power supply circuit (not shown) on the processor 602 and executable on the processor 602. The circuit board is arranged inside the space enclosed by the casing; the CPU 602 and the memory 601 are arranged on the circuit board; the power circuit is used to supply power to each circuit or device of the terminal; The memory 601 is used for storing executable program codes; the CPU 602 runs a program corresponding to the executable program codes by reading the executable program codes stored in the memory 601 .

The terminal further includes: a peripheral interface 603, an RF (Radio Frequency, radio frequency) circuit 605, an audio circuit 606, a speaker 611, a power management chip 608, an input/output (I/O) subsystem 609, a touch screen 612, and other inputs /Control device 610 and external ports 604, these components communicate through one or more communication buses or signal lines 607.

It should be understood that the illustrated terminal 600 is merely an example of a terminal, and that the terminal 600 may have more or fewer components than those shown in the figure, may combine two or more components, or may have Different part configurations. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.

The following describes the terminal device for application recommendation provided in this embodiment in detail. The terminal device takes a smart phone as an example.

Memory 601, which can be accessed by CPU 602, peripheral interface 603, etc., said memory 601 can include high-speed random access memory, and can also include non-volatile memory, such as one or more disk storage devices, flash memory devices , or other volatile solid-state storage devices.

A peripheral interface 603 that can connect the input and output peripherals of the device to the CPU 602 and the memory 601 .

I/O subsystem 609 , which can connect input and output peripherals on the device, such as touch screen 612 and other input/control devices 610 , to peripherals interface 603 . The I/O subsystem 609 may include a display controller 6091 and one or more input controllers 6092 for controlling other input/control devices 610 . Wherein, one or more input controllers 6092 receive electrical signals from or send electrical signals to other input/control devices 610, which may include physical buttons (push buttons, rocker buttons, etc. ), dial pad, slide switch, joystick, click wheel. Notably, the input controller 6092 can be connected to any of the following: a keyboard, an infrared port, a USB interface, and a pointing device such as a mouse.

Wherein, according to the working principle of the touch screen and the classification of the medium for transmitting information, the touch screen 612 may be a resistive type, a capacitive induction type, an infrared type or a surface acoustic wave type. According to the type of installation, the touch screen 612 can be: external, built-in or integral. Classified according to technical principles, the touch screen 612 may be: a vector pressure sensing technology touch screen, a resistive technology touch screen, a capacitive technology touch screen, an infrared technology touch screen or a surface acoustic wave technology touch screen.

The touch screen 612, which is an input interface and an output interface between the user terminal and the user, displays visual output to the user, and the visual output may include graphics, text, icons, videos, and the like. Optionally, the touch screen 612 sends an electrical signal triggered by the user on the touch screen (eg, an electrical signal on a contact surface) to the processor 602 .

Display controller 6091 in I/O subsystem 609 receives electrical signals from touch screen 612 or sends electrical signals to touch screen 612 . The touch screen 612 detects the contact on the touch screen, and the display controller 6091 converts the detected contact into interaction with the user interface object displayed on the touch screen 612, that is, to realize human-computer interaction, and the user interface object displayed on the touch screen 612 can be run. Icons for games, icons for connecting to the corresponding network, etc. It is worth noting that the device may also include a light mouse, which is a touch-sensitive surface that does not display visual output, or an extension of the touch-sensitive surface formed by a touch screen.

The RF circuit 605 is mainly used to establish the communication between the smart speaker and the wireless network (ie, the network side), so as to realize the data reception and transmission between the smart speaker and the wireless network. Such as sending and receiving text messages, e-mails, etc.

The audio circuit 606 is mainly used for receiving audio data from the peripheral interface 603 , converting the audio data into an electrical signal, and sending the electrical signal to the speaker 611 .

The speaker 611 is used to restore the voice signal received by the smart speaker from the wireless network through the RF circuit 605 into sound and play the sound to the user.

The power management chip 608 is used for power supply and power management for the hardware connected to the CPU 602, the I/O subsystem and the peripheral interface.

In this embodiment, the central processing unit 602 is used for:

Obtain the target application list according to the target keyword;

Obtain the push parameters of the first application in the target application list;

Determine the target push application according to the push parameters;

Push the target push application to the user.

Further, the obtaining the recommended parameters of the first application in the target application list includes:

Calculate the word frequency of the first application, where the word frequency is the ratio of the number of historical downloads of the first application to the number of historical downloads of the applications used in the application library;

Calculate the reverse file frequency of the first application;

The push parameter of the first application is calculated according to the word frequency and the reverse file frequency.

Further, the calculation of the reverse file frequency of the first application includes:

Calculate the reverse file frequency according to the total number of applications in the application library and the number of times the first application appears in other application lists, where the other application lists are application lists corresponding to other keywords except the target keyword;

Correspondingly, calculating the push parameter of the first application according to the word frequency and the reverse file frequency includes:

The push parameter of the first application is obtained by multiplying the word frequency and the reverse file frequency.

Further, determining the target push application according to the push parameter includes:

Determine the push sequence of the first application according to the push parameter;

A preset number of first applications ahead of the push sequence are determined as the target push application.

Further, determining the target push application according to the push parameter includes:

calculating the average value of the push parameters of the first application in the target application list;

Obtain the first application whose push parameter is greater than the average value, and determine it as the second application;

The second application is determined as a target push application.

Further, determining the second application as the target push application includes:

If the number of the second applications is greater than the preset number, the preset number of second applications is determined as the target push application.

Further, after obtaining the target application list according to the target keyword, it also includes:

Obtain the user's current geographic location information;

Correspondingly, determining the target push application according to the push parameters includes:

A target push application is determined according to the geographic location information and the push parameter.

Embodiments of the present application further provide a storage medium containing executable instructions of a terminal device, where the executable instructions of the terminal device are used to execute an application recommendation method when executed by a processor of the terminal device.

The computer storage medium of the embodiments of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples (a non-exhaustive list) of computer readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable Programmable Read Only Memory (EPROM or Flash), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .

Program code embodied on a computer readable medium may be transmitted using any suitable medium, including - but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out the operations of the present application may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional procedural languages, or a combination thereof. Programming Language - such as "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).

Of course, a storage medium containing computer-executable instructions provided by the embodiments of the present application is not limited to the above-mentioned application recommendation operation, and the computer-executable instructions of the storage medium can also be implemented in the application recommendation method provided by any embodiment of the present application. related operations.

The above-mentioned apparatus can execute the methods provided by all the foregoing embodiments of the present application, and has corresponding functional modules and beneficial effects for executing the above-mentioned methods. For technical details not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of this application.

Note that the above are only preferred embodiments of the present application and applied technical principles. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can also include more other equivalent embodiments without departing from the concept of the present application. The scope is determined by the scope of the appended claims.

Claims (9)

1. an application recommendation method, is characterized in that, comprises: Obtain the target application list according to the target keyword; Obtain the push parameters of the first application in the target application list, where the push parameters are calculated by a word frequency-reverse file frequency algorithm; Determine the target push application according to the push parameters; Pushing the target push application to the user; Wherein, the push parameters of the first application in the target application list are obtained, and the push parameters are calculated by a word frequency-reverse file frequency algorithm, including: Calculate the word frequency of the first application, and the word frequency is the ratio of the historical download times of the first application to the historical download times of all applications in the application library; Calculate the reverse file frequency of the first application; The push parameter of the first application is calculated according to the word frequency and the reverse file frequency. 2. The application recommendation method according to claim 1, wherein the calculating the reverse file frequency of the first application comprises: Calculate the reverse file frequency according to the total number of applications in the application library and the number of times the first application appears in other application lists, where the other application lists are application lists corresponding to other keywords except the target keyword; Correspondingly, calculating the push parameter of the first application according to the word frequency and the reverse file frequency includes: The push parameter of the first application is obtained by multiplying the word frequency and the reverse file frequency. 3. The application recommendation method according to claim 1, wherein the determining of the target push application according to the push parameter comprises: Determine the push sequence of the first application according to the push parameter; A preset number of first applications ahead of the push sequence are determined as the target push application. 4. The application recommendation method according to claim 1, wherein the determining of the target push application according to the push parameter comprises: calculating the average value of the push parameters of the first application in the target application list; Obtain the first application whose push parameter is greater than the average value, and determine it as the second application; The second application is determined as a target push application. 5. The application recommendation method according to claim 4, wherein the determining the second application as a target push application comprises: If the number of the second applications is greater than the preset number, the preset number of second applications is determined as the target push application. 6. The application recommendation method according to claim 1, wherein after acquiring the target application list according to the target keyword, the method further comprises: Obtain the user's current geographic location information; Correspondingly, determining the target push application according to the push parameters includes: A target push application is determined according to the geographic location information and the push parameter. 7. An application recommendation device, characterized in that, comprising: The target application list acquisition module is used to acquire the target application list according to the target keyword; A push parameter acquisition module, configured to obtain the push parameters of the first application in the target application list, where the push parameters are calculated by a word frequency-reverse file frequency algorithm; a target push application determination module, configured to determine a target push application according to the push parameters; a target push application push module, configured to push the target push application to the user; The push parameter acquisition module is further configured to calculate the word frequency of the first application, where the word frequency is the ratio of the historical download times of the first application to the historical download times of all applications in the application library; calculate the reverse file of the first application frequency; the push parameter of the first application is calculated according to the word frequency and the reverse file frequency. 8. A terminal device, characterized in that it comprises: a processor, a memory, and a computer program stored in the memory and running on the processor, the processor implementing the computer program as claimed in claims 1-6 when the processor executes the computer program. The application recommendation method described in any one of. 9. A storage medium on which a computer program is stored, characterized in that, when the program is executed by a processor, the application recommendation method according to any one of claims 1-6 is implemented.

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