CN111553748A - Android micro-service recommendation method and system based on user scene - Google Patents

Android micro-service recommendation method and system based on user scene Download PDF

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CN111553748A
CN111553748A CN202010387831.4A CN202010387831A CN111553748A CN 111553748 A CN111553748 A CN 111553748A CN 202010387831 A CN202010387831 A CN 202010387831A CN 111553748 A CN111553748 A CN 111553748A
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陈家瑞
李政浩
陈星�
王毅
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Fuzhou University
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Abstract

The invention relates to a user scene-based Android micro-service recommendation method and system, which comprises the following steps: extracting user scene information; and searching the service related to the extracted user scene information in the service library and recommending the service to the user. The method and the device acquire the content currently browsed by the user and recommend the related application service to the user according to the content.

Description

Android micro-service recommendation method and system based on user scene
Technical Field
The invention relates to the field of Android service development and design, in particular to an Android micro-service recommendation method and system based on a user scene.
Background
In recent years, with the rapid development of mobile devices, Android applications are emerging in large quantities. These applications, which cover all aspects of functions, including life, health, travel, etc., have become the main avenue for people to use the internet on the mobile side. The functions of a single application are numerous and can meet the requirements of the user of the application, but it is more desirable to combine some functions of multiple applications to meet the requirements of personalized services, for example, when the user uses a movie APP to browse certain movie information, he may want to purchase movie tickets by purchasing the movie APP, or wants to go to the movie theater to watch the movie, and needs a taxi-taking service.
Currently, there are some methods to implement service recommendation through application usage prediction models of shared aggregation patterns of application preferences and historical behaviors of users, data in cell phone sensors, and application behaviors in different user groups. However, these methods have a problem that they do not well represent the current usage environment of the user in terms of the data used, because the correlation between the data used in their proposed methods and the data used in the current scene of the user is not high.
Disclosure of Invention
In view of this, the present invention provides a method and a system for recommending Android micro-services based on a user scenario, to obtain a content currently browsed by a user, and then recommend a relevant application service to the user according to the content.
The invention is realized by adopting the following scheme: an Android micro-service recommendation method based on user scenes comprises the following steps:
extracting user scene information;
and searching the service related to the extracted user scene information in the service library and recommending the service to the user. Further, the extracting the user context information specifically includes: and acquiring a client screen text, marking to obtain a formal description of the user scene, and taking the formal description as user scene information.
Wherein, the formal description of the user scene is represented as pageC:
pageC＝{＜tag1,value1＞,...,＜tagi,valuei＞,...,＜tagn,valuen＞}；
in the formula, valueiAnd tagiRespectively representing the ith text and the corresponding text type in the user scene information, wherein n is the number of texts in the user page information, namely the number of information data in the user page。
Further, the related services include similar services and complementary services; wherein, the similar service is a service whose output is similar to the current scene information, and the complementary service is a service whose input is similar to the current scene information.
Formalizing the service as:
service＝＜Is，Os＞
wherein the content of the first and second substances,
Is＝{＜tag1,value1＞,...,＜tagj,valuej＞,...,＜tagm,valuem＞}
Os＝{＜tag1,value1＞,...,＜tagk,valuek＞,...,＜tagl,valuel＞}
in the formula IsAnd OsRespectively representing the input and output of a service, valuejAnd tagjRespectively representing the jth text in the service input and its corresponding text type, valuekAnd tagkRespectively representing the kth text and the corresponding text type in the service input, wherein m is the number of texts in the service input, and l is the number of texts in the service output.
Further, the searching for the similar service specifically includes: calculating the similarity simOC (O) between the service output in the service library and the current scene of the user by adopting the following formulaspageC), and selecting a service with a high similarity to the current scene of the user by N before as a similar service, wherein N is a natural number greater than or equal to 1:
Figure BDA0002484441930000031
wherein the content of the first and second substances,
Figure BDA0002484441930000032
Figure BDA0002484441930000033
Figure BDA0002484441930000034
Figure BDA0002484441930000035
Figure BDA0002484441930000036
wherein n represents the number of information data in a page, l represents the number of information data outputted by a service, and OsRepresenting the output of the service, pageC is a formal description of the user's scenario, tagiAnd tagkRepresenting labels in user scenarios and labels, value, in service outputs, respectivelyiAnd valuekRespectively representing text in a user scene and text in a service output; v. ofiAnd vkRespectively represent tags tagiAnd tagkWord vector of eiAnd ekRespectively representing text valuesiAnd valuekThe word vector of (2).
Further, the searching for the complementary service specifically includes: calculating the similarity simI C (I) of the service input in the service library and the current scene of the user by adopting the following formulaspageC), and selecting a service with a high K before the similarity of the current scene of the user as a complementary service, wherein K is a natural number greater than or equal to 1:
Figure BDA0002484441930000041
wherein the content of the first and second substances,
Figure BDA0002484441930000042
Figure BDA0002484441930000043
Figure BDA0002484441930000044
Figure BDA0002484441930000045
in the formula IsRepresenting the input of the service, pageC is the formal description of the user scene; n denotes the number of information data in a page, m denotes the number of information data in a service input, tagiAnd tagjRepresenting labels in user scenarios and labels in service input, value, respectivelyiAnd valuejRespectively representing text in a user scene and text in a service input; v. ofiAnd vjRespectively represent tags tagiAnd tagjWord vector of eiAnd ejRespectively representing text valuesiAnd valuejThe word vector of (2).
The invention also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a client and a server;
when a client receives a request from a user, extracting user scene information and sending the user scene information to a server;
when the server receives the user scene information transmitted from the client, relevant services are searched in the service library and are transmitted back to the client.
The invention also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a client;
the client receives a request from a user, extracts user scene information when receiving the request, sends the user scene information to the server, and waits for the server to search related services in a service library;
and the client receives the related service returned by the server and feeds back the related service to the user when receiving the related service returned by the server.
The invention also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a server;
and the server receives the user scene information sent by the client, searches the service related to the user scene information in the service library when receiving the user scene information sent by the client, and returns the result to the client.
Compared with the prior art, the invention has the following beneficial effects: according to the Android micro-service recommendation method based on the user scene, the service related to the page content can be provided for the user according to the page content browsed by the user, and firstly, the information in the current scene of the user is extracted by using a method based on a knowledge base; then, calculating the similarity between the service in the service library and the current user scene information; and finally, recommending the Android micro-service with the highest scene similarity with the current user to the user, thereby helping the user to complete the target of the user more conveniently and quickly. Meanwhile, the method simultaneously considers similar services and complementary services, and the effectiveness and accuracy of recommendation can be met.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Fig. 2 is a system framework schematic diagram of an embodiment of the invention.
Fig. 3 is a schematic diagram of a verification result according to an embodiment of the present invention.
FIG. 4 is a diagram of a thousand-and thousand-seek movie input interface according to an embodiment of the present invention.
FIG. 5 is a diagram illustrating an example of a service interface for a thousand and thousand seek movies.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the embodiment provides a user-scenario-based Android micro-service recommendation method, which includes the following steps:
extracting user scene information;
and searching the service related to the extracted user scene information in the service library and recommending the service to the user.
Through the steps, the Android microservice recommendation method and device achieve Android microservice recommendation.
Preferably, the embodiment uses a method of adadin [1] (Ma Yun, Hu Ziniu, Liu Yunxin, Xie Tao, Liu xuanzhe. adadin: automatic Release of Deep-Link APIs on Android [ J ]. the World Wide Web Conference,2018,27) in the prior art to generate the micro-service of each application, the adadin may generate a Deep Link for each page in the application, and a specific page in the application may be opened through the Deep Link. The present embodiment regards each deep link as a micro-service and uses the adadin to generate the service library of the present method.
In this embodiment, the extracting the user scene information specifically includes: and acquiring a client screen text, marking to obtain a formal description of the user scene, and taking the formal description as user scene information.
Wherein, the formal description of the user scene is represented as pageC:
pageC＝{＜tag1,value1＞,...,＜tagi,valuei＞,...,＜tagn,valuen＞}；
in the formula, valueiAnd tagiRespectively representing the ith text and the corresponding text type in the user scene information, wherein n is the number of the texts in the user page information.
In the embodiment, the user scene information is extracted mainly according to the characters in the mobile phone of the user. Firstly, obtaining mobile phone screen texts through an API provided by an Android system, and then labeling the texts according to a method provided by ApPract [2] (Fernandes E, RivaO, Nath S.Apacton-the-fly app content indexes with beta privacy [ J ]. the 22nd Annual International Conference,2016,22.), so that a formal description of a user scene is obtained. And then calculating the similarity between the current scene of the user and the service in the service library, and finally recommending the relevant service for the user.
In this embodiment, the related services include similar services and complementary services; wherein, the similar service is a service whose output is similar to the current scene information, and the complementary service is a service whose input is similar to the current scene information. Formalizing the service as:
service＝＜Is，Os＞；
wherein the content of the first and second substances,
Is＝{＜tag1,value1＞,...,＜tagj,valuej＞,...,＜tagm,valuem＞}；
Os＝{＜tag1,value1＞,...,＜tagk,valuek＞,...,＜tagl,valuel＞}；
in the formula IsAnd OsRespectively representing the input and output of a service, valuejAnd tagjRespectively representing the jth text in the service input and its corresponding text type, valuekAnd tagkRespectively representing the kth text and the corresponding text type in the service input, wherein m is the number of texts in the service input, and l is the number of texts in the service output.
Preferably, the present embodiment defines the similar service as: the service outputs a service similar to the user's scene, and such a service can provide the user with content similar to the current scene. Therefore, the present embodiment needs to calculate the similarity between the current user scenario and the service output, and recommend a service with higher similarity to the user. In calculating the similarity, the present embodimentThe similarity between the current user scene and the service output is evaluated by the similarity between words, which is expressed by the cosine of the corresponding word vector. In this embodiment, the searching for the similar service specifically includes: calculating the similarity simOC (O) between the service output in the service library and the current scene of the user by adopting the following formulaspageC), and selecting a service with a high similarity to the current scene of the user by N before as a similar service, wherein N is a natural number greater than or equal to 1:
Figure BDA0002484441930000081
wherein the content of the first and second substances,
Figure BDA0002484441930000082
Figure BDA0002484441930000091
Figure BDA0002484441930000092
Figure BDA0002484441930000093
wherein n represents the number of information data in a page, l represents the number of information data outputted by a service, and OsRepresenting the output of the service, pageC is a formal description of the user's scenario, tagiAnd tagkRepresenting labels in user scenarios and labels, value, in service outputs, respectivelyiAnd valuekRespectively representing text in a user scene and text in a service output; v. ofiAnd vkRespectively represent tags tagiAnd tagkWord vector of eiAnd ekRespectively representing text valuesiAnd valuekThe word vector of (2).
In this way, the present embodiment can obtain the similarity between the user scenario and the service output, and recommend the service with higher similarity to the user.
Preferably, if the input data of the service is similar to some data in the user scenario, the present embodiment refers to the service as a complementary service of the current scenario. In order to recommend the complementary service to the user, the present embodiment needs to calculate the similarity between the user scenario and the service input. In this embodiment, the searching for the complementary service specifically includes: calculating the similarity simIC (I) of the service input in the service library and the current scene of the user by adopting the following formulaspageC), and selecting a service with a high K before the similarity of the current scene of the user as a complementary service, wherein K is a natural number greater than or equal to 1:
Figure BDA0002484441930000094
wherein the content of the first and second substances,
Figure BDA0002484441930000101
Figure BDA0002484441930000102
Figure BDA0002484441930000103
Figure BDA0002484441930000104
in the formula IsRepresenting the input of the service, pageC is the formal description of the user scene; n denotes the number of information data in a page, m denotes the number of information data in a service input, tagiAnd tagjRepresenting labels in user scenarios and labels in service input, value, respectivelyiAnd valuejRespectively representing text in a user scene and text in a service input; v. ofiAnd vjRespectively represent tags tagiAnd tagjWord vector of eiAnd ejRespectively representing text valuesiAnd valuejThe word vector of (2).
After the calculation by the method, the complementary service is recommended to the user.
In summary, the present embodiment recommends two services to the user, one is a similar service, and this service can help the user explore an application page similar to the current scenario. The other is a complementary service, which can be seen as a prediction of user behavior, helping users to achieve their goals faster.
The embodiment also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a client and a server;
when a client receives a request from a user, extracting user scene information and sending the user scene information to a server;
when the server receives the user scene information transmitted from the client, relevant services are searched in the service library and are transmitted back to the client.
The embodiment also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a client;
the client receives a request from a user, extracts user scene information when receiving the request, sends the user scene information to the server, and waits for the server to search related services in a service library;
and the client receives the related service returned by the server and feeds back the related service to the user when receiving the related service returned by the server.
The embodiment also provides a system of the Android micro-service recommendation method based on the user scene, which comprises a server;
and the server receives the user scene information sent by the client, searches the service related to the user scene information in the service library when receiving the user scene information sent by the client, and returns the result to the client.
Preferably, the system framework of the embodiment is mainly composed of 5 parts, namely a listener, an executor, a scene analyzer, a service recommender and a user interface, as shown in fig. 2. The embodiment places the listener and the executor on the client, that is, they run on the user's handset. When the listener receives a request from a user, it will automatically capture the text content on the current phone screen and send it to the executor, which then sends the user context information to the server. The present embodiment runs a scene analyzer and a service recommender on a server. The scene analyzer will process the scene information sent by the client, i.e. assign tags to the texts in the scene. The service recommender then searches for services in the service repository and calculates the similarity to the user's context and sends the similar and complementary services with higher similarity to the client.
In particular, the embodiment uses a development tool Android Studio, and is developed based on java language. Before the method is used, firstly, method codes are led into an Android Studio working interval, corresponding modules are generated and activated on an Android platform provided with an xposed, then any application interface is opened, and a set area is clicked, so that recommendation of similar and complementary services with high similarity to the current user scene can be obtained.
Next, the present embodiment selects 10 applications from the e-book, news, shopping, food, movie, ticket, music, and traffic categories on the pea pod, respectively, and then generates a service database for each application using the adadin tool. This example invites five students to use the recommendation tool using the method of this example and evaluate the recommendation results. The present embodiment defines the service recommendation accuracy as:
Figure BDA0002484441930000121
wherein P is the number of services that the user considers to be related to the current page content, and F is the number of services that the user considers to be unrelated to the current page content, the accuracy of the first 3, 6, and 9 recommended services are respectively calculated, and the result is shown in fig. 3, which shows that the recommendation method based on the user scenario has high accuracy.
According to the method and the device, the similarity between the current user scene and the input and output of the micro service is calculated, so that the similar and complementary service with higher similarity to the current user scene can be obtained. For the details interface of the thousand and thousand found movies in the bean movie (as shown in fig. 4, the recommended service can be obtained by clicking the circular button at the bottom), the similar and complementary microservices recommended by the present embodiment are shown in fig. 5. Through the verification results, it can be seen that the similar and complementary services recommended by the embodiment are very relevant to the current user scene, and meet the requirements of people. Meanwhile, in most cases, the embodiment can recommend correct similar services and complementary services by calculating the similarity of the current user scene and the input and output of the services, which shows that the effectiveness and accuracy of the method of the embodiment can be satisfied.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.

Claims (9)

1. An Android micro-service recommendation method based on user scenes is characterized by comprising the following steps:
extracting user scene information;
and searching the service related to the extracted user scene information in the service library and recommending the service to the user.
2. The Android micro-service recommendation method and system based on user context according to claim 1, wherein the extracting of user context information specifically comprises: and acquiring a client screen text, marking to obtain a formal description of the user scene, and taking the formal description as user scene information.
3. The Android micro-service recommendation method and system based on user scenarios as claimed in claim 2, wherein the formal description of the user scenario is represented as pageC:
pageC＝{<tag1，value1>，...，<tagi，valuei>，...，<tagn，valuen>}；
in the formula, valueiAnd tagiRespectively representing the ith text and the corresponding text type in the user scene information, wherein n is the number of the texts in the user page information.
4. The user scenario-based Android micro-service recommendation method according to claim 1, wherein the related services include similar services and complementary services; the similar service is a service with output similar to the current scene information, and the complementary service is a service with input similar to the current scene information; formalizing the service as:
service＝<Is，Os>；
wherein the content of the first and second substances,
Is＝{<tag1，value1>，...，<tagj，valuej>，...，<tagm，valuem>}；
Os＝{<tag1，value1>，...，<tagk，valuek>，...，<tagl，valuel>}；
in the formula IsAnd OsRespectively representing the input and output of a service, valuejAnd tagjRespectively representing the jth text in the service input and its corresponding text type, valuekAnd tagkRespectively representing the kth text and the corresponding text type in the service input, wherein m is the number of texts in the service input, and l is the number of texts in the service output.
5. The user context-based Android micro-service recommendation method according to claim 4, characterized in that the similar service search specifically comprises: computing service library using the formulaSimilarity simOC (O) of service output and user current scene in (1)spageC), and selecting a service with a high similarity to the current scene of the user by N before as a similar service, wherein N is a natural number greater than or equal to 1:
Figure FDA0002484441920000021
wherein the content of the first and second substances,
Figure FDA0002484441920000022
Figure FDA0002484441920000023
Figure FDA0002484441920000024
Figure FDA0002484441920000025
wherein n represents the number of information data in a page, l represents the number of information data outputted by a service, and OsRepresenting the output of the service, pageC is a formal description of the user's scenario, tagiAnd tagkRepresenting labels in user scenarios and labels, value, in service outputs, respectivelyiAnd valuekRespectively representing text in a user scene and text in a service output; v. ofiAnd vkRespectively represent tags tagiAnd tagkWord vector of eiAnd ekRespectively representing text valuesiAnd valuekThe word vector of (2).
6. The user context-based Android micro-service recommendation method according to claim 4, characterized in that the searching of the complementary service specifically comprises: calculating the similarity simI between the service input in the service library and the current scene of the user by adopting the following formulaC(IspageC), and selecting a service with a high K before the similarity of the current scene of the user as a complementary service, wherein K is a natural number greater than or equal to 1:
Figure FDA0002484441920000031
wherein the content of the first and second substances,
Figure FDA0002484441920000032
Figure FDA0002484441920000033
Figure FDA0002484441920000034
Figure FDA0002484441920000035
in the formula IsRepresenting the input of the service, pageC is the formal description of the user scene; n denotes the number of information data in a page, m denotes the number of information data in a service input, tagiAnd tagjRespectively representing the tag in the user's scene and the tag, Value, in the service inputiAnd valuejRespectively representing text in a user scene and text in a service input; v. ofiAnd vjRespectively represent tags tagiAnd tagjWord vector of eiAnd ejRespectively representing text valuesiAnd valuejThe word vector of (2).
7. A system of the Android micro service recommendation method based on the user scenario of any one of claims 1 to 6,
comprises a client and a server;
when a client receives a request from a user, extracting user scene information and sending the user scene information to a server;
when the server receives the user scene information transmitted from the client, relevant services are searched in the service library and are transmitted back to the client.
8. A system of the Android micro service recommendation method based on the user scenario of any one of claims 1 to 6,
comprises a client;
the client receives a request from a user, extracts user scene information when receiving the request, sends the user scene information to the server, and waits for the server to search related services in a service library;
and the client receives the related service returned by the server and feeds back the related service to the user when receiving the related service returned by the server.
9. A system of the Android micro service recommendation method based on the user scenario of any one of claims 1 to 6,
comprises a server;
and the server receives the user scene information sent by the client, searches the service related to the user scene information in the service library when receiving the user scene information sent by the client, and returns the result to the client.
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