CN111523034B

CN111523034B - Application processing method, device, equipment and medium

Info

Publication number: CN111523034B
Application number: CN202010331435.XA
Authority: CN
Inventors: 梁华盛; 何文
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-04-24
Filing date: 2020-04-24
Publication date: 2023-08-18
Anticipated expiration: 2040-04-24
Also published as: CN111523034A

Abstract

The embodiment of the application discloses a processing method of an application program. The method comprises the following steps: and acquiring a second application program from the reference program set according to the main body attribute of the first application program, wherein the main body attribute of the second application program is associated with the main body attribute of the first application program, predicting the quality characteristic of the first application program by adopting the quality characteristic of the second application program, and outputting the quality identification result of the first application program according to the quality characteristic of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained based on the quality characteristics of the first application program.

Description

Application processing method, device, equipment and medium

Technical Field

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

Background

With the development of computer technology, the application program without installation (i.e. the application program which can be used without downloading and installing) has the advantages of convenience, small memory occupation, and the like, and is developed rapidly. Such applications have the characteristics of multiple categories, complex functions, multiple carriers, multiple content polymorphisms, multiple structures, and the like, so that it is difficult to well identify the quality of the application by using a single type of technology. Currently, two quality recognition technologies for the application program are mainly adopted, one is manual auditing, and special operation and maintenance personnel audit the content, the function and the like of the application program to reject some low-quality application programs; the mode is low in efficiency, high in cost and low in practicality. The other is to adopt the abnormal matching of the model, in particular to adopt some low-quality data to carry out modeling, the content, the function and the like related to the application program are matched with the model, if the matching is successful, the content and the function of the application program are considered to be the low-quality data, so that the quality abnormality of the application program is identified; the method has the problems of low precision, difficulty in fusing complex information such as functions, pages and interactions in the application program, low efficiency and high cost.

Disclosure of Invention

The embodiment of the application discloses a processing method, a device, equipment and a medium of an application program, which can accurately identify the quality of the application program by a simple and efficient quality identification technology.

In one aspect, an embodiment of the present application provides a method for processing an application, where the method includes:

acquiring a main body attribute and a reference program set of a first application program, wherein the reference program set comprises main body attributes and quality features of a plurality of reference application programs;

obtaining a second application from the reference set, the second application being at least one reference application in the reference set, and the subject attribute of the second application being associated with the subject attribute of the first application;

predicting the quality characteristics of the first application program by adopting the quality characteristics of the second application program;

and outputting a quality identification result of the first application program according to the quality characteristics of the first application program.

In the embodiment of the application, a second application program is acquired from a reference program set according to the main body attribute of the first application program, the main body attribute of the second application program is associated with the main body attribute of the first application program, then the quality characteristics of the first application program are predicted by adopting the quality characteristics of the second application program, and the quality recognition result of the first application program is output according to the quality characteristics of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained based on the quality characteristics of the first application program.

displaying a search page of a first client, wherein the first client comprises a first application program, and the first application program refers to any sub-application program parasitic in the first client;

acquiring a quality identification result of a first application program, wherein the quality identification result of the first application program is obtained by adopting the processing method of the application program;

if the quality identification result of the first application program meets the recommendation condition, acquiring information of the first application program;

information of the first application program is displayed in a search page of the first client.

In the embodiment of the application, a search page of a first client is displayed, a quality identification result of a first application program is obtained, the quality identification result of the first application program is obtained by adopting the processing method of the application program, if the quality identification result of the first application program meets the recommended condition, the information of the first application program is obtained, and the information of the first application program is displayed in the search page of the first client. Therefore, the embodiment of the application recommends the application program meeting the recommendation condition, improves the probability of selecting and using the high-quality application program by the user, and further improves the user experience.

In one aspect, the present application provides an apparatus for processing an application, the apparatus for processing an application including:

the acquisition unit is used for acquiring the main body attribute of the first application program and a reference program set, wherein the reference program set comprises the main body attribute and quality characteristics of a plurality of reference application programs;

the processing unit is used for acquiring a second application program from the reference program set, wherein the second application program is at least one reference application program in the reference program set, the main body attribute of the second application program is associated with the main body attribute of the first application program, the quality characteristic of the first application program is predicted by adopting the quality characteristic of the second application program, and the quality recognition result of the first application program is output according to the quality characteristic of the first application program.

In an embodiment, the processing unit is further configured to obtain the second application program from the reference program set, specifically configured to:

constructing a main body relation graph, wherein the main body relation graph comprises attribute nodes and program nodes, the attribute nodes comprise main body attribute nodes of a first application program and main body attribute nodes of a reference application program, the program nodes comprise first application program nodes and reference application program nodes, and one attribute node is connected with a plurality of program nodes;

Splitting the main body relation graph according to the attribute nodes to obtain a plurality of sub-relation graphs;

obtaining a target sub-relationship diagram from the plurality of sub-relationship diagrams, and determining a second application program from the target sub-relationship diagram; the attribute node of the target sub-relationship graph is a main body attribute node of the first application program, and the program node of the target sub-relationship graph comprises a first application program node and a second application program node.

In an embodiment, the processing unit is further configured to predict the quality feature of the first application using the quality feature of the second application, specifically configured to:

constructing a feature map of the first application program according to the quality features and the target sub-relationship map of the second application program;

and calling a graph network model to predict the feature graph to obtain the quality features of the first application program.

In an embodiment, the processing unit is further configured to construct a feature map of the first application according to the quality feature and the target sub-relationship map of the second application, specifically configured to:

connecting each program node in the target sub-relationship graph, deleting attribute nodes in the target sub-relationship graph, and obtaining a feature graph of the first application program;

the program nodes in the feature map comprise a first application program node and a second application program node, the second application program node carries quality features of the second application program, and an edge formed by connection between any two program nodes carries main body attributes of the first application program.

In one embodiment, the graph network model includes a graph roll-up network;

the processing unit is further configured to invoke a graph network model to predict the feature graph to obtain a quality feature of the first application program, and specifically configured to:

invoking a graph rolling network to extract quality features of the first application program from the feature graph.

In one embodiment, the number of the body attributes of the first application program is N, the number of the feature graphs of the first application program is N, the graph network model includes a graph convolution network, the graph convolution network includes N layers of graph convolution layers, and N is an integer greater than 1;

calling an N-layer graph convolution layer to extract N quality feature components of the first application program from the N feature graphs respectively; the method comprises the steps that a graph convolution layer is used for carrying out feature extraction processing on a feature graph to obtain a quality feature component of the first application program;

performing aggregation processing on the N quality characteristic components to obtain the quality characteristics of the first application program;

the aggregation processing mode comprises a weighted aggregation mode or a serial connection mode.

In one embodiment, the graph network model includes a graph roll-up network;

invoking a graph convolution network to carry out convolution processing on the feature graph to obtain intermediate quality features;

and stacking the intermediate quality features to obtain the quality features of the first application program.

In one embodiment, the graph network model includes a classifier;

the processing unit is further configured to output a quality recognition result of the first application according to the quality feature of the first application, and specifically configured to:

and calling a classifier to classify the quality characteristics of the first application program to obtain a quality identification result of the first application program.

In an embodiment, the processing unit is further configured to:

adding the body attribute and the quality feature of the first application program to a reference program set;

wherein the body attributes include at least one of: the identity of the developer, the identity of the operator and the service address;

the first application program and the reference application program are both installation-free application programs; the first application program refers to any sub-application program parasitic in the first client; the reference application program refers to a sub-application program which is parasitic in the first client side and is except the first application program; alternatively, the reference application refers to a sub-application that is hosted within the second client.

the display unit is used for displaying a search page of a first client, wherein the first client comprises a first application program, and the first application program refers to any sub-application program parasitic in the first client;

the processing unit is used for acquiring a quality identification result of the first application program, wherein the quality identification result of the first application program is obtained by adopting the processing method of the application program; if the quality identification result of the first application program meets the recommendation condition, acquiring information of the first application program;

and the display unit is also used for displaying the information of the first application program in the search page of the first client.

In one embodiment, the quality recognition result includes a quality score; the quality recognition result of the first application program meeting the recommendation condition means that the quality score of the first application program is larger than the quality threshold; the information of the first application program includes at least one of: the identity, the subject attribute, the category, and the function profile of the first application;

the processing unit is further configured to display information of the first application program in a search page of the first client, specifically configured to:

Adding information of a first application program into a recommendation list of a first client, wherein the recommendation list comprises a plurality of pieces of information to be recommended, and the pieces of information are ordered according to the quality scores of the corresponding application programs from high to low;

displaying information in a recommendation list of a first client in a search page of the first client;

the processing unit is further configured to:

if the quality score of the first application program is smaller than the punishment threshold value, punishment processing is carried out on the first application program, wherein the punishment threshold value is smaller than the quality threshold value, and the punishment processing comprises at least one of the following steps: and shielding, deleting and sending correction prompt information to an operator of the first application program.

In one aspect, the present application provides an apparatus for processing an application program, including:

a memory storing computer readable instructions;

and a processor coupled to the memory for executing computer readable instructions to implement the method of processing an application as described above.

In one aspect, the present application provides a computer readable storage medium storing one or more instructions adapted to be loaded by a processor and to perform the method of processing an application as described above.

In the embodiment of the application, a second application program is acquired from a reference program set according to the main body attribute of the first application program, the main body attribute of the second application program is associated with the main body attribute of the first application program, then the quality characteristics of the first application program are predicted by adopting the quality characteristics of the second application program, and the quality recognition result of the first application program is output according to the quality characteristics of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained through the quality characteristics of the first application program, so that the quality recognition technology is simple, efficient, low in cost and high in practicability; when the first client searches the application program, if the quality recognition result of the first application program indicates that the quality of the first application program is better, the first application program can be recommended in the searching process, the probability that the high-quality application program is selected and used by a user is improved, and further the user experience is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 illustrates an architecture diagram of a processing system for an application provided in one exemplary embodiment of the present application;

FIG. 2 illustrates a flowchart of a method for processing an application provided in an exemplary embodiment of the present application;

FIG. 3 illustrates a flowchart of a method for processing an application provided by an exemplary embodiment of the present application;

FIG. 4a illustrates a principal relational diagram provided by an exemplary embodiment of the application;

FIG. 4b illustrates a sub-relationship diagram provided by an exemplary embodiment of the present application;

FIG. 4c illustrates a feature diagram provided by an exemplary embodiment of the present application;

FIG. 4d illustrates a flowchart for extracting quality features of an application in a feature map according to an exemplary embodiment of the present application;

FIG. 4e illustrates a flowchart for extracting quality features of an application in a feature map, in accordance with yet another exemplary embodiment of the present application;

FIG. 4f illustrates a flowchart for extracting quality features of an application in a feature map according to yet another exemplary embodiment of the present application;

FIG. 4g illustrates yet another subject matter relationship diagram provided by an exemplary embodiment of the present application;

FIG. 4h illustrates a flowchart for extracting quality features of an application in a feature map according to yet another exemplary embodiment of the present application;

FIG. 4i illustrates a flowchart for extracting quality features of an application in a feature map according to yet another exemplary embodiment of the present application;

FIG. 5 illustrates a flowchart of a method for processing yet another application provided by an exemplary embodiment of the present application;

FIG. 6a illustrates a search page diagram of a first client provided in accordance with an exemplary embodiment of the present application;

FIG. 6b illustrates a search results page view of a first client provided in accordance with an exemplary embodiment of the present application;

FIG. 7 illustrates a flowchart of a method for processing yet another application provided by an exemplary embodiment of the present application;

FIG. 8 is a schematic diagram of an application processing device according to an exemplary embodiment of the present application;

FIG. 9 is a schematic diagram of a processing device for yet another application according to an exemplary embodiment of the present application;

fig. 10 is a schematic diagram of a processing device for an application according to an exemplary embodiment of the present application.

Detailed Description

The technical scheme in the embodiment of the application will be described below with reference to the accompanying drawings.

The embodiment of the application relates to artificial intelligence (Artificial Intelligence, AI), natural language processing (Nature Language processing, NLP) and Machine Learning (ML), and can mine information hidden in data and having potential value by combining the AI, the NLP and the ML, so that equipment can predict and identify an application program more accurately. The AI is a theory, a method, a technology and an application system which simulate, extend and extend human intelligence by using a digital computer or a machine controlled by the digital computer, sense environment, acquire knowledge and acquire an optimal result by using the knowledge. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.

AI technology is a comprehensive discipline, and relates to a wide range of technologies, both hardware and software. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, processing technology for large applications, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

NLP is an important direction in the computer science and AI fields. It is studying various theories and methods that enable effective communication between a person and a computer in natural language. NLP is a science that integrates linguistics, computer science, and mathematics. Thus, the research in this field will involve natural language, i.e. language that people use daily, so it has a close relationship with the research in linguistics. NLP techniques typically include text processing, semantic understanding, machine translation, robotic questions and answers, knowledge graph techniques, and the like.

ML is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. ML is the core of artificial intelligence, the fundamental way for computers to have intelligence, which is applied throughout the various fields of artificial intelligence. ML and deep learning typically includes techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.

Embodiments of the present application relate to processing of applications. An application may be referred to herein as an installation-free application, i.e., an application that can be used without downloading an installation, such an application is also commonly referred to as an applet, which is typically hosted in a client as a sub-application. A client (also referred to as an application client, APP client) refers to a program that is downloaded and installed in a terminal and runs in the terminal. Terminals may include various types of clients, including but not limited to: IM (instant messaging) clients (e.g., micro-messaging clients, QQ clients, etc.), SNS (Social Networking Services ) clients (e.g., micro-blog clients, social-enabled map clients, etc.), content service clients (e.g., news clients), image processing clients, search clients, etc. The application programs mentioned in the subsequent embodiments of the present application will be described by taking, as examples, sub-application programs (i.e., applets) that are parasitic in various types of clients of the terminal, unless specifically described otherwise.

Because of the variety and number of applications, the quality of applications is also irregular, and the applications can be generally classified into high-quality applications and poor-quality applications. Wherein the quality of an application can be assessed from multiple dimensions of traffic, services, content, etc. From the traffic dimension, by premium application may be meant an application where traffic (e.g., cumulative points or interests) exceeds a threshold, such as: a user may consider an application to be a premium if the user's cumulative endorsement number for that application exceeds 100 tens of thousands. Conversely, an inferior application is an application whose traffic does not reach a threshold value, or an application whose traffic is reported to be improved by improper means, for example: applications that increase traffic by imposhing information such as names, trademarks, etc. of others, or by inducing sharing, purchasing false attention numbers, etc. are considered inferior applications. From the service dimension, a good-quality application may refer to an application that can smoothly provide a service to a user, and a bad-quality application may refer to an application that cannot normally provide a service to a user, for example: applications that provide unavailable services due to expiration or abnormality of certificates, or that have malicious services such as induced download, plug-in, etc., are considered to be poor quality applications. From the content dimension, the priority application program refers to an application program which does not contain advertisement information, has positive energy and does not contain illegal or false information, and the content is positive upwards; the inferior application program refers to an application program of which the content has a large amount of advertisement information or illegal website jump links. Therefore, recognizing the quality of the application program and screening out the high-quality application program to provide high-quality service for the user is an important means for improving the competitiveness of the client. However, quality identification of applications presents a significant challenge because: (1) different application functions are different; for example: the news applet provides news searching and browsing functions, and the shopping applet provides electronic commerce functions; (2) the content of the application program is rich and varied, for example, the news applet contains various news; the shopping applet contains various commodity information; (3) the carriers of the application programs are various; for example, a shopping applet hosts an IM client, while a gaming applet hosts a news client; (4) the application programs have different structures, and each application program is developed by using different program frameworks; for the reasons mentioned above, quality recognition of applications presents a great technical challenge.

The embodiment of the application provides a scheme for identifying the quality of an application program, which is used for acquiring a second application program from a reference program set according to the main body attribute of a first application program, wherein the main body attribute of the second application program is associated with the main body attribute of the first application program, predicting the quality characteristic of the first application program by adopting the quality characteristic of the second application program, and outputting the quality identification result of the first application program according to the quality characteristic of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained through the quality characteristics of the first application program, so that the quality recognition technology is simple, efficient, low in cost and high in practicability; when the first client searches the application program, if the quality recognition result of the first application program indicates that the quality of the first application program is better, the first application program can be recommended in the searching process, the probability that the high-quality application program is selected and used by a user is improved, and further the user experience is improved.

FIG. 1 illustrates an architecture diagram of a processing system for an application provided in one exemplary embodiment of the present application. As shown in fig. 1, the processing system of the application may include one or more terminal devices 101 and one or more servers 102. The number of terminal devices and servers in the processing system of the application program shown in fig. 1 is merely an example, and for example, the number of terminal devices and servers may be plural, and the present application is not limited to the number of terminal devices and servers.

The terminal device 101 is a device used by a user, and includes at least one client including at least one sub-application (applet). For example, the client is a WeChat client, which includes a news applet, a shopping applet, a game applet, and the like. The terminal device 101 may include an AI model and a reference program set. Terminal device 101 may include, but is not limited to: smart phones (such as Android phones, iOS phones, etc.), tablet computers, portable personal computers, mobile internet devices (MID for short), etc., the embodiments of the present application are not limited. The server 102 refers to a background device for managing an application program and providing services to the terminal device 101. The server 102 may include an AI model and a reference program set. Server 102 may include, but is not limited to, a clustered server.

In one embodiment, the process flow of the application may be performed by the server 102. Specifically: the server 102 performs quality recognition on the application deployed on the server by using the processing method of the application provided by the embodiment of the application, so as to obtain a quality recognition result of the application. When the terminal device 101 requests the server 102 to acquire an application program, the server 102 provides the corresponding application program to the terminal device 101 according to the quality identification result of the application program; for example, the terminal device 101 requests to acquire the applet related to shopping, and the server 102 includes 10 applet related to shopping, but according to the quality recognition result of these applet, it is known that only 3 applet related to shopping have quality meeting the push condition, and the server 102 transmits the 3 applet related to shopping meeting the push condition to the terminal device 101.

In another embodiment, the processing flow of the application program may be executed by the terminal 101. According to the user behavior sequence stored in the terminal equipment or the sequence set provided by the server 102, the terminal equipment 101 identifies the quality of the application program in the terminal equipment by the processing method of the application program provided by the embodiment of the application, so as to obtain the quality identification result of the application program, and further recommends the corresponding application program for the user according to the quality identification result of the application program; for example: the terminal 101 includes 10 shopping-related applets, but as can be seen from the quality recognition results of the applets, only 3 shopping-related applets have quality meeting the recommendation condition, and when a search instruction for the shopping-related applets sent by the user is received, the terminal 101 displays the 3 shopping-related applets meeting the recommendation condition to the user.

In the processing system of the application program shown in fig. 1, the processing flow of the application program mainly includes: (1) the terminal device 101 obtains the subject attribute of the first application (such as the identifier of the developer of the small program 1 in the chat platform, the identifier of the operator, and the service address) and the reference program set (such as obtained by the server 102), where the reference program set includes the subject attribute and the quality feature of the plurality of reference applications (such as whether advertisement pushing is included, the number of people concerned, the average number of times used per day, etc.) (2) obtains the second application from the reference program set, which may be one or more, and the second application is associated with the subject attribute of the first application (such as that the first application and the second application are developed by the same development company, or that the development company of the first application is a subsidiary of the development company of the second application, etc.). (3) The terminal device 101 predicts the quality characteristics of the first application using the quality characteristics of the second application (e.g., the first application and the plurality of second applications are all developed by company a and each of the second applications includes advertisement push, then the first application is predicted to also include advertisement push) (4) the terminal device 101 outputs the quality recognition result of the first application based on the quality characteristics of the first application (e.g., the quality score of the application that does not include advertisement is set to 90 and the quality score of the application that includes advertisement is set to 70).

In the embodiment of the application, a terminal device or a server acquires a second application program from a reference program set according to the main body attribute of a first application program, the main body attribute of the second application program is associated with the main body attribute of the first application program, then the quality characteristic of the first application program is predicted by adopting the quality characteristic of the second application program, and the quality recognition result of the first application program is output according to the quality characteristic of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained based on the quality characteristics of the first application program.

In the processing system of the application program shown in fig. 1, the processing flow of another application program mainly includes: (5) and (3) displaying a search page of the first client in the terminal equipment 101, wherein the first client comprises a first application program (for example, the first application program is an applet 1, the first client is a social platform, the social platform comprises the applet 1) (6) the terminal equipment 101 obtains the quality identification result of the first application program obtained in the steps (1) to (4). (7) If the quality recognition result of the first application program meets the recommendation condition (for example, the quality score is higher than 80 points), the information (for example, the identification, the main body attribute, the category, the function profile, and the like of the first application program) of the first application program is obtained through the memory of the terminal device or the server 102 (8), and the information of the first application program is displayed in the search page of the first client.

In the embodiment of the application, the terminal equipment displays a search page of the first client to acquire a quality identification result of the first application program, wherein the quality identification result of the first application program is acquired by adopting the processing method of the application program, and if the quality identification result of the first application program meets the recommended condition, the information of the first application program is acquired and displayed in the search page of the first client. Therefore, the embodiment of the application recommends the application program meeting the recommendation condition, improves the probability of selecting and using the high-quality application program by the user, and further improves the user experience.

Fig. 2 is a flowchart illustrating a processing method of an application according to an exemplary embodiment of the present application. The processing method of the application program may be executed by the terminal device 101 or the server 102 shown in fig. 1; as shown in fig. 2, the processing method of the application program includes, but is not limited to, the following steps 201 to 204. The following describes in detail a processing method of an application program provided by an embodiment of the present application by taking a terminal device as an example:

201. the terminal equipment acquires the main body attribute of the first application program and the reference program set.

Wherein, the first application program refers to any installation-free application program in the terminal equipment, and concretely can refer to a sub-application program (applet) in a client of the terminal equipment; for example, the first application is an applet in the IM client.

The principal attributes of the first application include, but are not limited to, the identity of the developer of the first application, the identity of the operator, and the service address, among other attributes that can distinguish applications. For example, the body attributes of the application 1 include: the developer is a software development company A, and the service address is 165.28.3.16; the body attributes of the application 2 include: the developer is a software development company B, the operator is an XX operation company, and the service address is 172.25.37.86.

The reference set includes body attributes and quality features of a plurality of reference applications, i.e., the quality features of the reference applications in the reference set are known. The reference application program refers to a sub-application program which is parasitic in the first client side and is except the first application program; alternatively, the reference application refers to a sub-application that is hosted within the second client. For example, the first application is applet 1 in the social platform, and the reference application is applet 2 to applet 10 in the social platform; alternatively, the reference application is applet 1 through applet 10 in the shopping platform. Quality characteristics refer to parameters that are referenced when measuring the quality of an application. For example, quality characteristics include whether or not advertisement pushing is included, the number of people in focus, the average number of times used per day, and the like.

In one embodiment, the reference program set is stored in a local storage space of the terminal device (e.g., a storage space built in or extended by the terminal device, or a memory of a client running in the terminal device). In another embodiment, the terminal device obtains the reference program set through the server (e.g., the terminal device sends an obtaining request to the server, where the obtaining request is used to request to obtain the reference program set).

202. The terminal device obtains a second application program from the reference program set.

Wherein the second application refers to an application associated with a subject property of the first application. Specifically, the first application and the second application have at least one identical subject attribute, or the at least one subject attribute of the first application is related to the at least one subject attribute of the second application, for example, development company a of application 1 is a subsidiary of development company B of application 2.

In one embodiment, the terminal device determines the subject attribute associated with the first application according to the subject attribute of the first application, screens out the reference application with the subject attribute associated with the subject attribute of the first application from the reference program set based on the subject attribute, and determines the reference application as the second application.

For example, assuming that the principal attribute of the first application is "a company development", and B company is a subsidiary of a company, the terminal device determines that the associated principal attribute is "a company development" and "B company development" from "a company development". Then, the terminal device screens out the reference application programs 1 to 5 whose main attribute is "company a development" or "company B development" from the reference program set, and determines the reference application programs 1 to 5 as the second application program.

203. The terminal device predicts the quality characteristics of the first application program by adopting the quality characteristics of the second application program.

Wherein the quality features refer to parameters referenced when measuring the quality of the application program; for example, quality characteristics include whether or not advertisement pushing is included, the number of people in focus, the average number of times used per day, and the like. Specifically, the terminal device obtains the quality features of the second application program through the reference program set, and predicts the quality features of the first application program through the quality features of the second application program in the modes of data mining, deep learning, natural language processing and the like. For example, assuming that each second application carries a feature for displaying advertisements in use, the terminal device predicts that the quality feature of the first application includes a feature for displaying advertisements in use.

204. And the terminal equipment outputs a quality identification result of the first application program according to the quality characteristics of the first application program.

The quality recognition result is used for indicating the quality of the first application program; for example, the first application is indicated as a premium application or a poor quality application. In one embodiment, the terminal device judges the quality of the first application program according to the quality characteristics of the first application program to obtain a quality identification result of the first application program; for example, the quality characteristic of the application 1 is "including false information", and the terminal device determines that the application 1 is a poor quality application (quality recognition result) based on the "including false information".

Fig. 3 is a flowchart illustrating a processing method of an application according to an exemplary embodiment of the present application. The processing method of the application program may be executed by the terminal device 101 or the server 102 shown in fig. 1; as shown in fig. 3, the processing method of the application program includes, but is not limited to, the following steps 301 to 306. The following describes in detail a processing method of an application program provided by an embodiment of the present application by taking a terminal device as an example:

301. the terminal equipment acquires the main body attribute of the first application program and the reference program set.

The specific embodiment of step 301 may refer to the embodiment of step 201 in fig. 2, and will not be described herein.

302. The terminal equipment constructs a main body relation graph.

The main body relation graph is used for establishing connection between the first application program node and the reference application program node through main body attributes. The principal relation graph comprises attribute nodes and program nodes, wherein the attribute nodes comprise principal attribute nodes of a first application program and principal attribute nodes of a reference application program, the program nodes comprise first application program nodes and reference application program nodes, and one attribute node is connected with a plurality of program nodes.

In one embodiment, the terminal device constructs a subject relationship graph from the first application and the reference program set. Specifically, taking a first application as an example: (1) a program node of the first application is generated. (2) Generating attribute nodes of the first application program, and establishing connection between the program nodes of the first application program and the attribute nodes of the first application program, wherein each attribute node corresponds to a main body attribute of the first application program. (3) Generating a program node of a second application program associated with the attribute node of the first application program, and establishing a connection between the attribute node of the first application program and the program node of the second application program. (4) Continuing to establish the connection of other reference application nodes in the reference program set in the reference step (2) and the reference step (3) until the program nodes in the main body relation graph contain all the reference application nodes in the reference program set.

Fig. 4a shows a principal relational diagram provided by an exemplary embodiment of the application. As shown in fig. 4a, the principal relation graph includes attribute nodes a to C and program nodes D to K. Assuming that the first application program corresponds to the program node G in fig. 4a, the first application program has the main attribute 1 to the main attribute 3, and corresponds to the attribute node a to the attribute node C, respectively, the program node D connected to the attribute node a, and the program node F and the program node I are program nodes of the second application program having the association relationship with the main attribute 1 corresponding to the attribute node a. Similarly, the program node E and the program node H are program nodes of the second application program having an association relationship with the subject attribute 2 corresponding to the attribute node B, and the program node J and the program node K are program nodes of the second application program having an association relationship with the subject attribute 3 corresponding to the attribute node C. It should be noted that, the attribute nodes, the number of program nodes and the structure of the principal relational graph in fig. 4a are only used as examples, and do not constitute a practical limitation of the present application. For example, connected to the attribute node a is also the program node L, and connected to the program node D is also the attribute node M.

303. The terminal equipment splits the main body relation diagram according to the attribute nodes to obtain a plurality of sub-relation diagrams, acquires a target sub-relation diagram from the plurality of sub-relation diagrams, and determines a second application program from the target sub-relation diagram.

The sub-relationship diagram is used for determining a plurality of application programs which have an association relationship with the main body attribute (for example, the development company a of the application program 1 is a sub-company of the development company B of the application program 2, and the development companies of the application program 3 and the application program 4 are all development companies a). Each sub-relationship graph includes an attribute node and a plurality of program nodes. For example, assuming that the sub-relationship diagram 1 includes the attribute node 1 and the program nodes 1 to 3, it indicates that all of the 3 application programs corresponding to the program nodes 1 to 3 have an association relationship with the main attribute corresponding to the attribute node 1. The target sub-relationship graph refers to a sub-relationship graph containing a first application node in the program nodes. It can be understood that, in the target sub-relationship diagram, the application programs corresponding to the other program nodes except the first application program node are the second application programs.

In one embodiment, the terminal device splits the main relationship graph according to the attribute nodes to obtain a plurality of sub-relationship graphs (if the main relationship graph includes N attribute nodes, N is a positive integer, N sub-relationship graphs may be obtained by splitting), determines a target sub-relationship graph, and determines the second application program from the target sub-relationship graph. Specifically, each attribute node is taken as a center point, the program nodes connected with the attribute node are reserved, a sub-relationship diagram including a first application program node (namely, the attribute node of the target sub-relationship diagram is a main attribute node of the first application program) in the program nodes is determined to be the target sub-relationship diagram, and a reference application program corresponding to the program nodes except the first application program node in the target sub-relationship diagram is determined to be the second application program. It can be understood that by splitting the main body relation diagram and referring to the manner of obtaining the second application program associated with the first application program, the associated application program of each reference application program in the reference program set can be obtained, and the quality characteristics of each reference application program in the reference program set can be updated by the method provided by the embodiment of the application.

FIG. 4b illustrates a sub-relationship diagram provided by an exemplary embodiment of the present application. The sub-relationship diagram shown in fig. 4B is split centering on the attribute node B in fig. 4a, and includes a program node G, a program node H, and a program node E, each having an association relationship between the attribute node B and the main attribute 2 corresponding to the attribute node B. Since the program node G in the sub-relationship graph is the first application node, the sub-relationship graph is the target sub-relationship graph. The reference application programs corresponding to the program node H and the program node E are second application programs of which the subject attributes are associated with the subject attributes of the first application program.

Alternatively, if only the second application associated with the first application needs to be acquired, the target sub-relationship graph may be directly constructed through the body attribute of the first application and the reference program set, so as to determine the second application associated with the first application. Or screening the reference program set through the main body attribute of the first application program to obtain a second application program.

304. And the terminal equipment constructs a feature map of the first application program according to the quality features and the target sub-relation map of the second application program.

The feature graph is obtained by adding main body attributes corresponding to attribute nodes in the target sub-relationship graph to the connecting edges among the program nodes. The program nodes in the feature map comprise a first application program node and a second application program node, the second application program node carries the quality features of the second application program (the quality features of the second application program can be extracted through the history of the use record of the second application program), and the edge formed by connection between any two program nodes carries the main body attribute of the first application program.

In one embodiment, the terminal device connects each program node in the target sub-relationship graph with each other, and deletes the attribute node in the target sub-relationship graph to obtain the feature graph of the first application program. Fig. 4c shows a feature diagram provided by an exemplary embodiment of the present application. The feature diagram shown in fig. 4c is constructed according to fig. 4B, where the program node E and the program node H carry quality features of the second application program, and the connecting edges between the program nodes carry the body attribute 2 corresponding to the attribute node B.

305. And the terminal equipment calls a graph network model to predict the feature graph so as to obtain the quality features of the first application program.

The graph network model is used for predicting quality features of the first application program according to the feature graph. The graph network model includes, but is not limited to, a graph convolutional neural network (Graph Convolutional Network, GCN).

In one embodiment, the number of the main body attributes of the first application program is 1, that is, the number of the feature graphs is 1, and the terminal device adopts the graph network model to fuse the features in the feature graphs. FIG. 4d illustrates a flowchart for extracting quality features of an application in a feature map according to an exemplary embodiment of the present application. As shown in fig. 4d, the program node G is a first application node, and the terminal device invokes the GCN to perform feature extraction on the feature map of the first application, so as to obtain quality features of the first application. For example, assuming that the program node E and the program node H both carry features for displaying advertisements in use and the main attribute 2 is XX operation company, the quality features of the first application program obtained by extracting features from the feature map through GCN include features for displaying advertisements in use.

In another embodiment, the number of body attributes of the first application is N, i.e. the number of feature maps is N, N being a positive integer greater than 1. FIG. 4e illustrates a flowchart for extracting quality features of an application in a feature map according to yet another exemplary embodiment of the present application. As shown in fig. 4e, the program node G is a first application node, the terminal device invokes the GCN to perform feature extraction on each feature map to obtain feature components of N first applications, and then performs weighted aggregation on the feature components of N first applications through an Attention (Attention) mechanism, or performs aggregation on the feature components of N first applications through a concatenation (Concat) manner to obtain quality features of the first applications.

The attention mechanism is used for increasing the weight of key feature components in the feature components of the first application program, so that the influence of the key feature components of the first application program on the quality features of the first application program is increased. For example, for a news applet, the key feature components are the number of users browsed, the number of endorsements, the authenticity of the article, etc.; for shopping applets, key feature components are the number of users purchased, quality assessment of the merchandise, and the like. Assuming that in fig. 4e, the weight of the feature component 1 (f 1) of the first application program extracted by the GCN is 0.3, the weight of the feature component 2 (f 2) is 0.7, and the weight of the feature component 3 (f 3) is 0.4, the quality features of the first application program obtained by the weighted aggregation are: f1×0.3+f2×0.7+f3×0.4; the quality characteristics of the first application program obtained by aggregation in a serial connection mode are as follows: f1+f2+f3.

In yet another embodiment, the quality feature of the first application obtained by the terminal device is used as an intermediate quality feature, and the intermediate quality feature is stacked to obtain the quality feature of the first application. FIG. 4f illustrates a flowchart for extracting quality features of an application in a feature map according to yet another exemplary embodiment of the present application. As shown in fig. 4f, the terminal device refers to the implementation manner of obtaining the quality features of the first application program, obtains the quality features of each reference application program in the reference program set, constructs a new main body relationship diagram according to the intermediate quality features and the quality features of each reference application, further obtains an updated target sub-relationship diagram, obtains an updated feature diagram through the updated target sub-relationship diagram, invokes the GCN to respectively extract the features of the updated feature diagram, obtains the quality feature components of the first application program, and aggregates the quality feature components of the first application program to obtain the intermediate quality features. And repeating the steps for M times of stacking the intermediate quality features to obtain the quality features of the first application program, wherein M is a positive integer. The neighborhood of the first application program can be expanded through stacking processing, and the more comprehensive and complete quality characteristics of the first application program are obtained, so that the accuracy of the quality identification result of the first application program is improved.

306. And the terminal equipment outputs a quality identification result of the first application program according to the quality characteristics of the first application program.

In one embodiment, the terminal device updates the reference program set by adding the body attributes and quality features of the first application to the reference program set. And then, adopting a classifier included in the graph network model to identify the quality characteristics of the first application program, and obtaining a quality identification result of the first application program. Specifically, the classifier scores the quality of the first application program according to the score rule of each quality characteristic, and counts the scores of each quality characteristic of the first application program to obtain the quality identification result of the first application program. For example, the quality score of the applet with the user's endorsement lower than 1 ten thousand is set to 30, the quality score of the applet with the user's endorsement between 1 ten thousand and 10 ten thousand is set to 50, the quality score of the applet with the user's endorsement between 10 ten thousand and 50 ten thousand is set to 70, and the quality score of the applet with the user's endorsement exceeding 50 ten thousand is set to 90. For another example, the quality score of an applet that carries advertisement information is set to 60 and the quality score of an applet that does not carry advertisement information is set to 80. Assuming that the applet 1 has 3 quality features and the quality scores of the 3 quality features are 65, 90, 70, respectively, the quality score in the quality recognition result of the applet 1 is 75.

Optionally, the quality features of the first application program are obtained by manual labeling or by performing feature extraction on the historical behavior record of the application program, and compared with the quality of the first application program obtained in the step 203, and the graph network model is trained (for example, parameters in the graph network model are adjusted) by using a gradient backhaul method, so as to obtain the optimized graph network model.

The following describes in detail, by way of an example, a processing method of an application program according to an embodiment of the present application: the terminal equipment is set to identify the quality of an application program of bus taking and scanning codes, wherein the bus taking and scanning codes are developed by a development company XX and operated by an operation company YY; the service address of the bus taking code is a network address xxxxx; therefore, the main body attributes of the bus taking code include: development company XX, operation company YY, and network address xxxxx. The processing method of the application program in this example includes the following steps:

firstly, constructing a main body relation diagram of a bus taking code. As shown in fig. 4g, the "bus riding code" is first used as a program node, and the 3 main attributes thereof are respectively used as attribute nodes; and the connection between the program node 'bus taking and scanning code' and the attribute node 'development company XX', 'network address' and 'operation company YY' is established respectively. And sequentially acquiring the reference application program from the reference program set as program nodes, acquiring the main body attribute of the reference application program as attribute nodes, and respectively connecting the program nodes with the attribute nodes according to attribute relevance. As shown in fig. 4g, the attribute node "development company XX" is further connected with the program nodes "bus card two-dimension code riding" and "bus e-way riding code", which means that the program nodes of the reference application programs and the "bus riding code scanning" have the same or related main attribute (i.e. development company XX), and further means that the reference application programs and the application programs of the "bus riding code scanning" are all developed by the development company XX or related companies thereof (such as sub-companies of the development company XX). Similarly, the attribute node 'network address xxx' is also connected with a program node 'bus e-way bus taking code', 'AA subway bus trip' and 'bus taking and code scanning assistant'; it is indicated that these reference applications and the "bus riding code" application are served by a server having a network address xxxxx or its associated server (like other servers in a local area network). Similarly, the attribute node 'operating company YY' is also connected with a program node 'taking bus' and 'bus code scanning taking bus'; the application program of the reference application program and the application program of the bus taking and scanning code are operated by an operation company YY or related companies (such as a subsidiary of the operation company YY).

And secondly, splitting the main body relation graph to obtain a plurality of sub relation graphs. In this example, referring to fig. 4g, each circular dashed box includes an attribute node and a plurality of program nodes connected to the attribute node; then, the main body relation diagram can be split according to a circular dotted line frame, and three target sub-relation diagrams related to the application program of bus taking and scanning can be obtained. And further, other reference application programs in the sub-relation diagram, such as ' AA subway bus travel ', ' bus e-way riding code ', ' riding and the like, can be determined to be the second application program.

And then, generating a feature map according to the second application program and the target sub-relation map. Referring to fig. 4h, the feature map includes a development company sub-map, a network address sub-map, and an operation company sub-map. The meaning of each node in fig. 4h is shown in table 1 below:

table 1: node meaning table

/>

Deleting the connection between the attribute node A and the program nodes H and the connection between the attribute node A and the program nodes E, establishing the connection between each program node (such as establishing the connection between the program node G and the program node E and establishing the connection between the program node E and the program node H), and adding development company XX into the connection edges between each program node, so that the connection edges between each program node carry the information of the development company XX, thereby obtaining the feature map corresponding to the development company subgraph. And similarly, respectively generating a network address subgraph and a characteristic graph of an operation company subgraph according to the second application program and the target subgraph.

And then, carrying out feature extraction on each feature map to obtain quality feature components, and carrying out aggregation processing on the quality feature components to obtain the quality features of the first application program. As shown in fig. 4h, invoking a graph convolution neural network model (such as GCN) to perform feature extraction processing on the three feature graphs to obtain three quality feature components of a bus riding scanning code; and carrying out weighted aggregation treatment on the three quality characteristic components by adopting an attention mechanism to obtain the middle quality characteristic of the bus taking code.

And finally, superposing the middle quality characteristics of the bus taking and scanning codes to obtain the quality characteristics of the bus taking and scanning codes. As shown in fig. 4i, the quality features of the second application programs such as "AA subway bus travel", "bus e-way bus code", "bus has been taken" and the like can be obtained in the above manner, and one or more times of calculation is performed in the above manner to obtain the quality features (i.e. stacking processing) of the "bus code-sweeping" and the final result after stacking is classified by the classifier to obtain the quality recognition result of the "bus code-sweeping".

In the embodiment of the application, a main body relation diagram is constructed according to the main body attribute of a first application program, namely a reference program set, the main body relation diagram is split to obtain a plurality of sub-relation diagrams, a target sub-relation diagram is determined, a second application program is further determined, a characteristic diagram of the first application program is constructed according to the second application program and the target sub-relation diagram, the characteristic diagram is predicted through a graph network model to obtain the quality characteristics of the first application program, and the quality recognition result of the first application program is output according to the quality characteristics of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained based on the quality characteristics of the first application program.

Fig. 5 is a flowchart illustrating a processing method of yet another application program according to an exemplary embodiment of the present application. The processing method of the application program may be executed by the terminal apparatus 101 shown in fig. 1; as shown in fig. 5, the processing method of the application program includes, but is not limited to, the following steps 501 to 504. Wherein:

501. the terminal device displays a search page of the first client.

The first client is any client on the terminal equipment, the first client comprises a first application program, and the first application program refers to any sub-application program parasitized in the first client. For example, the mobile phone includes a social APP (i.e., client), and the social APP includes applet 1 to applet 10. For another example, the computer includes shopping software 1, and the shopping software 1 includes sub-applications 1 to 3.

502. The terminal equipment obtains a quality identification result of the first application program.

In one embodiment, the terminal device obtains a quality recognition result of the first application program from the local storage space, where the recognition result of the first application program is obtained by the terminal device using the embodiment described in the embodiment of fig. 2, and the quality recognition result of the first application program includes a quality score of the first application program. For example, the quality recognition result of the application 1 is a shopping program, and the quality score is 80.

In another embodiment, the terminal device obtains a quality identification result of the first application program from the server, where the identification result of the first application program is obtained by the server using the embodiment described in the embodiment of fig. 2.

503. And if the quality identification result of the first application program meets the recommendation condition, the terminal equipment acquires the information of the first application program.

Wherein, meeting the recommendation condition means that the quality score of the first application program is greater than the quality threshold. For example, if the quality threshold is 70 points, the quality score of the application 1 is 80 points, and the quality score of the application 2 is 60 points, it is determined that the application 1 satisfies the recommendation condition, and the application 2 does not satisfy the recommendation condition. The information of the first application program includes at least one of: identification of the first application, subject attributes, category, functionality profile.

In one embodiment, if the terminal device determines that the first application program meets the recommendation condition, the terminal device obtains information of the first application program from the server or the local storage space, and adds the information of the first application program to the recommendation list. And the terminal equipment sorts the application programs in the recommendation list according to the order of the quality from high to low.

In another embodiment, if the quality score of the first application is less than the penalty threshold, the terminal device performs penalty processing on the first application. The punishment threshold is smaller than the quality threshold, namely, the application program meeting the recommendation condition is not punished, and the punished application program is not recommended. The penalty process includes: the terminal equipment performs shielding processing on the first application program (i.e. the terminal equipment does not display when the user searches the first application program), deleting processing (i.e. the terminal equipment deletes the first application program), sending modification prompt information to an operator of the first application program, wherein the modification prompt information comprises possible problems of the first application program (such as false information in articles issued by the prompt operator), and the modification prompt information is used for prompting the operator to perform self-checking and improvement aiming at the possible problems.

504. The terminal device displays information of the first application program in a search page of the first client.

In one embodiment, the search page of the first client includes a recommendation column, and the terminal device displays information in the recommendation list in the recommendation column of the first client. Fig. 6a shows a search page diagram of a first client according to an exemplary embodiment of the present application. As shown in fig. 6a, 601 is a search bar of the first client, and when a user needs to search for an applet, the user clicks the search bar, inputs keywords of the applet to be searched for, and clicks a "search-for-one" button to perform a search. And 602 is a recommendation column of the first client, the terminal equipment displays information in a recommendation list in 602, and the applet in the recommendation list is an applet with quality identification results meeting recommendation conditions. The arrangement sequence of the applets in the recommendation list is obtained by sequencing the quality scores of the applets from high to low; or the method is obtained by sequencing the comprehensive scores of a plurality of factors such as the relevance of the applet and the historical search record of the user and the quality scores of the applet according to the order of the comprehensive scores from high to low.

In another embodiment, after receiving a search instruction of a user, the terminal performs comprehensive scoring according to a plurality of factors such as quality scores of the applied programs and the like of the related degree of the keywords, sorts the applied programs corresponding to the search instruction according to the order of the comprehensive scores from high to low, and displays the sorted results in a search result column. Specific implementation may refer to implementation of displaying information in a recommendation list in a recommendation column, and will not be described herein. FIG. 6b illustrates a search results page diagram of a first client provided in accordance with an exemplary embodiment of the present application. As shown in fig. 6b, the user enters "shopping" at 601 and clicks the "search for one" button. 603 is a search result column of the first client, the terminal device determines a corresponding application program according to "shopping", sorts information of the searched application program, and displays the sorted result in the search result column 603.

Fig. 7 is a flowchart illustrating a processing method of yet another application program according to an exemplary embodiment of the present application. The processing method of the application program may be executed by the terminal device 101 shown in fig. 1 or interactively executed by the terminal device 101 and the server 102 shown in fig. 1; as shown in fig. 7, the processing method of the application program includes, but is not limited to, the following steps 701 to 708. Wherein:

701. the terminal equipment acquires the main body attribute of the first application program and the reference program set.

702. The terminal device obtains a second application program from the reference program set.

703. The terminal device predicts the quality characteristics of the first application program by adopting the quality characteristics of the second application program.

704. And the terminal equipment outputs a quality identification result of the first application program according to the quality characteristics of the first application program.

The specific embodiments of steps 701 to 704 may refer to the embodiments of steps 201 to 204 in fig. 2, and are not described herein. The steps 701 to 704 may be performed by a server.

705. The terminal device displays a search page of the first client.

706. The terminal equipment obtains a quality identification result of the first application program.

707. And if the quality identification result of the first application program meets the recommendation condition, the terminal equipment acquires the information of the first application program.

708. The terminal device displays information of the first application program in a search page of the first client.

The specific embodiments of steps 705 to 708 may refer to the embodiments of steps 501 to 504 in fig. 5, and are not described herein.

The following describes in detail, by way of a complete example, the processing method of the application program provided in the embodiment of the present application: let 1 be the newly developed applet for company a and is responsible for operation maintenance by company B, and applet 1 is the applet in APP 1. The terminal device obtains the main body attribute of the applet 1, and the main body attribute of the applet 1 includes a development company (i.e., company a) and an operation company (i.e., company B). The terminal device acquires a reference program set including the main body attributes and quality features of the applets 2 to 100. The terminal device screens out the applet 13 and applet 24 developed by company a, the applet 5 and applet 31 developed by company C (company C is a subsidiary of company a), and the applet 12, applet 63 and applet 78 responsible for operation maintenance by company B from the reference program set. Assuming that applet 5, applet 13, applet 24 and applet 31 are shopping applets and that neither applet 12, applet 63 nor applet 78 contain advertisement information, the terminal device predicts that applet 1 is a shopping applet without advertisement information based on the characteristics of these 7 applets. The terminal device then obtains the quality recognition result of applet 1 from applet 1 for the shopping applet (quality feature) that does not contain advertisement information.

When a user opens a search page of the APP1, the terminal equipment acquires the quality recognition results of the applet 1 and other applets in the APP 1. Firstly, based on quality scores contained in quality recognition results, selecting applets with quality scores higher than a quality threshold and applets with quality scores lower than a penalty threshold from the applets. Then, the applets with the quality scores higher than the quality threshold value are ranked in the order from high to low, and the information of the applets is sequentially displayed in the recommendation column of the search page according to the ranking result. As shown in fig. 6a, the terminal device displays the 3 applets with the highest quality scores among the applets in the recommendation field 602. Wherein, the quality score of XX applet is the quality score of YY applet is the quality score of ZZ applet is the quality threshold. Similarly, as shown in fig. 6b, after the search keyword is acquired, the terminal device sorts the applets with the quality scores higher than the quality threshold according to the corresponding applets matched with the search keyword in the order of the quality scores from high to low, and sequentially displays the information of the applets in the search result column 603 of the search page according to the sorting result. Then, the applet whose quality score is lower than the penalty threshold is subjected to penalty processing such as masking, deleting or sending a modification prompt message to the operator of the applet.

In the embodiment of the application, the terminal equipment acquires the second application program from the reference program set according to the main body attribute of the first application program, the main body attribute of the second application program is associated with the main body attribute of the first application program, then the quality characteristic of the first application program is predicted by adopting the quality characteristic of the second application program, and the quality identification result of the first application program is output according to the quality characteristic of the first application program. Based on the clustering idea, a second application program which is the same with the first application program is dug through the relevance among the main body attributes, the quality characteristics of the first application program which are obtained through the quality characteristic prediction of the second application program can reflect the whole quality of the first application program more accurately, and the accurate quality recognition result of the first application program is obtained through the quality characteristics of the first application program, so that the quality recognition technology is simple, efficient, low in cost and high in practicability; when the first client searches the application program, if the quality recognition result of the first application program indicates that the quality of the first application program is better, the first application program can be recommended in the searching process, the probability that the high-quality application program is selected and used by a user is improved, and further the user experience is improved.

The foregoing details of the method of embodiments of the present application are provided for the purpose of better implementing the foregoing aspects of embodiments of the present application, and accordingly, the following provides an apparatus of embodiments of the present application.

Referring to fig. 8, fig. 8 is a schematic structural diagram of an application processing apparatus according to an exemplary embodiment of the present application, where the application processing apparatus may be installed on a terminal device or a server in the foregoing method embodiment. The processing means of the application program shown in fig. 8 may be used to perform some or all of the functions of the method embodiments described above with respect to fig. 2, 3 and 7. Wherein, the detailed description of each unit is as follows:

an obtaining unit 801, configured to obtain a main body attribute of a first application program and a reference program set, where the reference program set includes main body attributes and quality features of a plurality of reference application programs; and means for obtaining a second application from the reference set, the second application being at least one reference application in the reference set, and a subject attribute of the second application being associated with a subject attribute of the first application;

the processing unit 802 is configured to predict a quality feature of the first application by using the quality feature of the second application, and output a quality recognition result of the first application according to the quality feature of the first application.

In an embodiment, the processing unit 802 is further configured to obtain a second application from the reference program set, specifically configured to:

constructing a main body relation graph, wherein the main body relation graph comprises attribute nodes and program nodes, the attribute nodes comprise main body attribute nodes of the first application program and main body attribute nodes of the reference application program, the program nodes comprise first application program nodes and reference application program nodes, and one attribute node is connected with a plurality of program nodes;

splitting the main body relation graph according to attribute nodes to obtain a plurality of sub relation graphs;

acquiring a target sub-relationship diagram from the plurality of sub-relationship diagrams, and determining a second application program from the target sub-relationship diagram; the attribute node of the target sub-relationship graph is a main body attribute node of the first application program, and the program node of the target sub-relationship graph comprises the first application program node and the second application program node.

In an embodiment, the processing unit 802 is further configured to predict the quality feature of the first application using the quality feature of the second application, specifically configured to:

constructing a feature map of the first application program according to the quality features of the second application program and the target sub-relationship map;

In an embodiment, the processing unit 802 is further configured to construct a feature map of the first application according to the quality feature of the second application and the target sub-relationship map, specifically configured to:

connecting all program nodes in the target sub-relationship graph with each other, deleting attribute nodes in the target sub-relationship graph, and obtaining a feature graph of the first application program;

the program nodes in the feature map comprise the first application program node and the second application program node, the second application program node carries quality features of the second application program, and an edge formed by connection between any two program nodes carries main body attributes of the first application program.

In one embodiment, the graph network model includes a graph roll-up network;

the processing unit 802 is further configured to invoke a graph network model to predict the feature graph to obtain a quality feature of the first application, and specifically is configured to:

and calling the graph rolling network to extract the quality characteristics of the first application program from the characteristic graph.

In an embodiment, the number of the main body attributes of the first application program is N, the number of the feature graphs of the first application program is N, the graph network model includes a graph convolution network, the graph convolution network includes N layers of graph convolution layers, and N is an integer greater than 1;

invoking an N-layer graph convolution layer to respectively extract N quality characteristic components of the first application program from N characteristic graphs; the first application program comprises a first image convolution layer, a second image convolution layer and a third image convolution layer, wherein the first image convolution layer is used for carrying out feature extraction processing on a feature image to obtain a quality feature component of the first application program;

In one embodiment, the graph network model includes a graph roll-up network;

Calling the graph convolution network to carry out convolution processing on the feature graph to obtain intermediate quality features;

In one embodiment, the graph network model includes a classifier;

the processing unit 802 is further configured to output a quality recognition result of the first application according to the quality feature of the first application, specifically configured to:

and calling the classifier to classify the quality features of the first application program to obtain a quality identification result of the first application program.

In an embodiment, the processing unit 802 is further configured to:

adding body attributes and quality features of the first application to the reference set;

the first application program and the reference application program are both installation-free application programs; the first application program refers to any sub-application program parasitized in the first client; the reference application program refers to a sub-application program which is registered in the first client and is except the first application program; alternatively, the reference application refers to a sub-application that is hosted within the second client.

According to one embodiment of the present application, part of the steps involved in the processing method of the application program shown in fig. 2, 3 and 7 may be performed by respective units in the processing apparatus of the application program shown in fig. 8. For example, steps 201 and 202 shown in fig. 2 may be performed by the acquisition unit 801 shown in fig. 8, and steps 203 and 204 may be performed by the processing unit 802 shown in fig. 8. Step 301 shown in fig. 3 may be performed by the acquisition unit 801 shown in fig. 8, and steps 302 to 306 may be performed by the processing unit 802 shown in fig. 8. Steps 701 and 702 shown in fig. 7 may be performed by the acquisition unit 801 shown in fig. 8, and steps 703 and 704 may be performed by the processing unit 802 shown in fig. 8. The respective units in the processing apparatus of the application program shown in fig. 8 may be individually or all combined into one or several additional units, or some (some) of the units may be further split into a plurality of units with smaller functions, which may achieve the same operation without affecting the achievement of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the processing device of the application program may also include other units, and in practical applications, these functions may also be implemented with assistance by other units, and may be implemented by cooperation of a plurality of units.

According to another embodiment of the present application, a processing apparatus of an application program as shown in fig. 8 may be constructed by running a computer program (including program code) capable of executing steps involved in the respective methods as shown in fig. 2, 3 and 7 on a general-purpose computing apparatus such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and a processing method of an application program implementing the embodiment of the present application. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and run in the above-described computing device through the computer-readable recording medium.

Based on the same inventive concept, the principle and beneficial effects of the application program processing device provided in the embodiment of the present application for solving the problem are similar to those of the application program processing method in the method embodiment of the present application, and may refer to the principle and beneficial effects of the implementation of the method, which are not described herein for brevity.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a processing apparatus for an application according to another exemplary embodiment of the present application, where the processing apparatus for an application may be installed on a terminal device in the foregoing method embodiment. The processing means of the application program shown in fig. 9 may be used to perform some or all of the functions of the method embodiments described above with respect to fig. 5 and 7. Wherein, the detailed description of each unit is as follows:

The display unit 901 is configured to display a search page of a first client, where the first client includes a first application, and the first application refers to any sub-application that is hosted in the first client;

a processing unit 902, configured to obtain a quality recognition result of the first application, where the quality recognition result of the first application is obtained by using the method in the method embodiment described in fig. 2; if the quality identification result of the first application program meets the recommendation condition, acquiring information of the first application program;

the display unit 901 is further configured to display information of the first application program in a search page of the first client.

In one embodiment, the quality recognition result includes a quality score; the quality recognition result of the first application program meeting the recommendation condition means that the quality score of the first application program is larger than a quality threshold; the information of the first application program comprises at least one of the following: the identification, the main body attribute, the category and the function profile of the first application program;

the processing unit 902 is further configured to display information of the first application program in a search page of the first client, specifically configured to:

Adding the information of the first application program into a recommendation list of the first client, wherein the recommendation list comprises a plurality of pieces of information to be recommended, and the pieces of information are ordered according to the quality scores of the corresponding application programs from high to low;

displaying information in a recommendation list of the first client in a search page of the first client;

the processing unit 902 is further configured to:

if the quality score of the first application program is smaller than a punishment threshold value, punishment processing is carried out on the first application program, wherein the punishment threshold value is smaller than the quality threshold value, and the punishment processing comprises at least one of the following steps: and shielding, deleting and sending correction prompt information to an operator of the first application program.

According to one embodiment of the present application, part of the steps involved in the processing method of the application program shown in fig. 5 and 7 may be performed by respective units in the processing apparatus of the application program shown in fig. 9. For example, steps 501 and 504 shown in fig. 5 may be performed by the display unit 901 shown in fig. 9, and steps 502 and 503 may be performed by the processing unit 902 shown in fig. 9. Step 705 and step 708 shown in fig. 7 may be performed by the display unit 901 shown in fig. 9, and step 706 and step 707 may be performed by the processing unit 902 shown in fig. 9. The respective units in the processing apparatus of the application program shown in fig. 9 may be individually or all combined into one or several additional units, or some (some) of the units may be further split into a plurality of units with smaller functions, which may achieve the same operation without affecting the achievement of the technical effects of the embodiments of the present application. The above units are divided based on logic functions, and in practical applications, the functions of one unit may be implemented by a plurality of units, or the functions of a plurality of units may be implemented by one unit. In other embodiments of the present application, the processing device of the application program may also include other units, and in practical applications, these functions may also be implemented with assistance by other units, and may be implemented by cooperation of a plurality of units.

According to another embodiment of the present application, a processing apparatus of an application program as shown in fig. 9 may be constructed by running a computer program (including program code) capable of executing the steps involved in the respective methods as shown in fig. 5 and 7 on a general-purpose computing apparatus such as a computer including a processing element such as a Central Processing Unit (CPU), a random access storage medium (RAM), a read only storage medium (ROM), and the like, and a storage element, and a processing method of an application program implementing the embodiment of the present application. The computer program may be recorded on, for example, a computer-readable recording medium, and loaded into and run in the above-described computing device through the computer-readable recording medium.

Referring to fig. 10, fig. 10 is a schematic structural diagram of an application processing device according to an exemplary embodiment of the present application, where the application processing device includes at least a processor 1001, a communication interface 1002, and a memory 1003. Wherein the processor 1001, the communication interface 1002 and the memory 1003 may be connected by a bus or other means, the embodiments of the present application are exemplified by a bus connection. The processor 1001 (or called central processing unit (Central Processing Unit, CPU)) is a computing core and a control core of the processing device of the application program, and can parse various instructions in the terminal device and process various data of the terminal device, for example: the CPU can be used for analyzing a startup and shutdown instruction sent by a user to the terminal equipment and controlling the terminal equipment to perform startup and shutdown operation; and the following steps: the CPU can transmit various kinds of interaction data between the internal structures of the terminal device, and so on. Communication interface 1002 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI, mobile communication interface, etc.), and may be controlled by processor 1001 to receive and transmit data; the communication interface 1002 may also be used for transmission and interaction of data inside the terminal device. A Memory 1003 (Memory) is a Memory device in the terminal device for storing programs and data. It will be appreciated that the memory 1003 here may include a built-in memory of the terminal device, or may include an extended memory supported by the terminal device. Memory 1003 provides storage space that stores the operating system of the terminal device, which may include, but is not limited to: android systems, iOS systems, windows Phone systems, etc., the application is not limited in this regard.

In one embodiment, the processing device of the application may refer to a terminal device or a server, such as terminal device 101 or server 102 shown in fig. 1. In this case, the processor 1001 performs the following operations by executing the executable program code in the memory 1003:

acquiring a main body attribute and a reference program set of a first application program through a communication interface 1002, wherein the reference program set comprises main body attributes and quality features of a plurality of reference application programs;

obtaining a second application from the reference set, the second application being at least one reference application in the reference set, and a subject attribute of the second application being associated with a subject attribute of the first application;

predicting the quality features of the first application program by adopting the quality features of the second application program;

As an alternative embodiment, the specific embodiment of the processor 1001 obtaining the second application program from the reference program set is:

As an alternative embodiment, the specific embodiment of predicting the quality feature of the first application by using the quality feature of the second application by the processor 1001 is:

As an alternative embodiment, the specific embodiment of constructing the feature map of the first application by the processor 1001 according to the quality feature of the second application and the target sub-relationship map is:

As an alternative embodiment, the graph network model includes a graph roll-up network;

the specific implementation manner of the processor 1001 invoking the graph network model to predict the feature graph to obtain the quality feature of the first application program is as follows:

As an optional implementation manner, the number of the main body attributes of the first application program is N, the number of the feature graphs of the first application program is N, the graph network model includes a graph convolution network, the graph convolution network includes N layers of graph convolution layers, and N is an integer greater than 1;

As an alternative embodiment, the graph network model includes a classifier;

the specific implementation manner of the processor 1001 outputting the quality recognition result of the first application program according to the quality feature of the first application program is:

As an alternative embodiment, the processor 1001 further performs the following operations by executing the executable program code in the memory 1003:

In another embodiment, the processing device of the application may refer to a terminal device, such as terminal device 101 shown in fig. 1. In this case, the processor 1101 performs the following operations by executing executable program code in the memory 1103:

displaying a search page of a first client, wherein the first client comprises a first application program, and the first application program refers to any sub-application program which is hosted in the first client;

the quality recognition result of the first application program is obtained through the communication interface 1102, wherein the quality recognition result of the first application program is obtained through recognition by adopting the method in the method embodiment described in the above-mentioned fig. 2;

and displaying the information of the first application program in the search page of the first client.

As an alternative embodiment, the quality recognition result includes a quality score; the quality recognition result of the first application program meeting the recommendation condition means that the quality score of the first application program is larger than a quality threshold; the information of the first application program comprises at least one of the following: the identification, the main body attribute, the category and the function profile of the first application program;

the specific implementation manner of displaying the information of the first application program in the search page of the first client by the processor 1101 is:

the processor 1101 also performs the following operations by executing executable program code in the memory 1103:

The embodiment of the application also provides a computer readable storage medium, wherein one or more instructions are stored in the computer readable storage medium, and the one or more instructions are suitable for being loaded by a processor and executing the processing method of the application program in the embodiment of the method.

The embodiment of the application also provides a computer program product containing instructions, which when run on a computer, cause the computer to execute the processing method of the application program in the embodiment of the method.

It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of action described, as some steps may be performed in other order or simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.

The modules in the device of the embodiment of the application can be combined, divided and deleted according to actual needs.

Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the readable storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

The above disclosure is only a preferred embodiment of the present application, and it should be understood that the scope of the application is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present application.

Claims

1. A method for processing an application program, the method comprising:

acquiring main body attributes and a reference program set of a first application program, wherein the reference program set comprises the main body attributes and quality characteristics of a plurality of reference application programs;

2. The method of claim 1, wherein the obtaining a second application from the reference set of programs comprises:

3. The method of claim 2, wherein predicting the quality feature of the first application using the quality feature of the second application comprises:

4. A method according to claim 3, wherein said constructing a feature map of said first application from quality features of said second application and said target sub-relationship map comprises:

5. A method according to claim 3, wherein the graph network model comprises a graph convolution network; the call graph network model predicts the feature graph to obtain the quality feature of the first application program, and the method comprises the following steps:

6. A method according to claim 3, wherein the number of body attributes of the first application is N, the number of feature graphs of the first application is N, the graph network model comprises a graph convolution network comprising N layers of graph convolution layers, N being an integer greater than 1;

The call graph network model predicts the feature graph to obtain the quality feature of the first application program, and the method comprises the following steps:

7. A method according to claim 3, wherein the graph network model comprises a graph convolution network; the call graph network model predicts the feature graph to obtain the quality feature of the first application program, and further comprises:

8. A method according to claim 3, wherein the graph network model comprises a classifier; the outputting the quality recognition result of the first application program according to the quality characteristics of the first application program includes:

9. The method according to claim 1, wherein the method further comprises:

10. A method for processing an application program, the method comprising:

Acquiring a quality recognition result of the first application program, wherein the quality recognition result of the first application program is obtained by recognizing the quality recognition result by the method according to any one of claims 1 to 9;

11. The method of claim 10, wherein the quality recognition result comprises a quality score; the quality recognition result of the first application program meeting the recommendation condition means that the quality score of the first application program is larger than a quality threshold; the information of the first application program comprises at least one of the following: the identification, the main body attribute, the category and the function profile of the first application program;

the displaying the information of the first application program in the search page of the first client side comprises the following steps:

the method further comprises the steps of:

12. An apparatus for processing an application program, comprising:

the device comprises an acquisition unit, a reference program set and a processing unit, wherein the acquisition unit is used for acquiring the main body attribute and the reference program set of the first application program, and the reference program set comprises the main body attributes and the quality characteristics of a plurality of reference application programs; and means for obtaining a second application from the reference set, the second application being at least one reference application in the reference set, and a subject attribute of the second application being associated with a subject attribute of the first application;

the processing unit is used for predicting the quality characteristics of the first application program by adopting the quality characteristics of the second application program and outputting the quality recognition result of the first application program according to the quality characteristics of the first application program.

13. An apparatus for processing an application program, comprising:

the display unit is used for displaying a search page of a first client, wherein the first client comprises a first application program, and the first application program refers to any sub-application program which is registered in the first client;

the processing unit is configured to obtain a quality recognition result of the first application program, where the quality recognition result of the first application program is obtained by recognizing the quality recognition result by using the method according to any one of claims 1 to 9; if the quality identification result of the first application program meets the recommendation condition, acquiring information of the first application program;

the display unit is further configured to display information of the first application program in a search page of the first client.

14. An application processing apparatus, comprising:

a memory storing computer readable instructions;

a processor coupled to the memory for executing the computer readable instructions to implement the method of processing an application as claimed in any one of claims 1 to 9 or to implement the method of processing an application as claimed in claim 10 or 11.

15. A computer readable storage medium, characterized in that it stores one or more instructions adapted to be loaded and executed by a processor for the processing method of an application according to any of claims 1-9 or for implementing the processing method of an application according to claim 10 or 11.