CN117390292B - Application program information recommendation method, system and equipment based on machine learning - Google Patents
Application program information recommendation method, system and equipment based on machine learning Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004590 computer program Methods 0.000 claims description 13
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides an application program information recommending method, system and equipment based on machine learning, wherein the method comprises the steps of obtaining a first application program type, a second application program type and an N application program type with priority and a first application characteristic data set, a second application characteristic data set and an N application characteristic data set corresponding to each type through obtained application program use information and use frequency information of a target user; the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set are input into the machine learning model for learning to obtain contribution degrees, then the using interestingness of the target user for the application program is determined according to the contribution degrees, and then the application program information matched with the using interestingness is recommended to the target user, so that the application program information recommendation is more accurate and in place, the interest of the user for the recommended application program is improved, the downloading efficiency of the recommended application program is improved, and unnecessary popularization cost is saved.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to an application program information recommendation method, system and equipment based on machine learning.
Background
The rapid growth of internet size and information resources presents information overload problems, and it is increasingly difficult to obtain the required information. An information recommendation system using 'information push' as a service mode is a main means for solving the problem of information overload at present. Information recommendation refers to the system recommending useful information to a user that the user may be interested in but not known, and is implemented mainly by means of a recommendation system. In the existing information recommendation field, widely-advertised information popularization forms are generally adopted to recommend information to users, whether the information is actually needed or even the information is disliked by the users is not considered, in this case, no specific target user exists, the recommendation target is not targeted, the purpose of recommending proper information for different target users is difficult to achieve, when a certain product needs to be targeted personalized information recommendation for users with different ages and different use requirements, the recommendation difficulty is large, the recommendation range is quite wide, for example, application program information recommendation is quite wide, the popularization cost is higher due to the fact that the widely-recommended forms are often adopted, but the recommendation precision is not high due to the fact that the actual demands of the target user groups are not considered, the positioning is inaccurate, and the user downloading efficiency is difficult to achieve the expectations.
Disclosure of Invention
The invention aims to provide a machine learning-based application information recommendation method, system and equipment, and aims to solve the problems that the prior art is difficult to achieve the purpose of recommending proper information for different target users, the recommendation difficulty is large, the recommendation range is very wide, the recommendation precision is low, the positioning is inaccurate, and the user downloading efficiency is difficult to achieve expectations.
In one aspect, the present invention provides a machine learning-based application information recommendation method, the method comprising the steps of:
acquiring application program use information of a target user and use frequency information thereof, and classifying application programs used by the target user into a first application program category, a second application program category and an Nth application program category according to the application program use information;
dividing the use priority of the first application program category, the second application program category and the N application program category according to the use frequency information, and extracting the application characteristics of each application program in the corresponding first application program category, second application program category and N application program category according to the use priority;
respectively integrating application characteristics of each application program in the first application program category, the second application program category and the Nth application program category into a first application characteristic data set, a second application characteristic data set and an Nth application characteristic data set;
inputting the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set into a machine learning model for learning, carrying out contribution analysis on the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set, determining the using interestingness of a target user on an application program according to the contribution, and recommending application program information matched with the using interestingness to the target user according to the using interestingness.
In some embodiments, prioritizing usage of the first application class, the second application class, and the nth application class according to the usage frequency information includes:
respectively counting a first total use frequency of the first application program category, a second total use frequency of the second application program category and an Nth total use frequency of the Nth application program category;
judging the first total use frequency, the second total use frequency and the N total use frequency: if the first total use frequency is greater than the second total use frequency and the second total use frequency is greater than the Nth total use frequency, dividing the first application program category corresponding to the first total use frequency into a first priority, dividing the second application program category corresponding to the second total use frequency into a second priority, and dividing the Nth application program category corresponding to the Nth total use frequency into an Nth priority.
In some embodiments, the extracting the application features of each application in the corresponding first application class, second application class, and nth application class according to the usage priority includes:
and sequentially acquiring each application program in the first application program category of the first priority, the second application program category of the second priority and the Nth application program category of the Nth priority according to the first priority, the second priority and the Nth priority, and extracting the application characteristics of each application program.
In some embodiments, the integrating the application features of each application in the first application class, the second application class, and the nth application class into a first application feature dataset, a second application feature dataset, and an nth application feature dataset, respectively, includes:
combining a plurality of application feature sets corresponding to the first priority into a first application feature data set;
combining a plurality of application feature sets corresponding to the second priority into a second application feature data set;
and integrating a plurality of application feature sets corresponding to the Nth priority into an Nth application feature data set.
In some embodiments, the inputting the first, second, and nth application feature data sets into a machine learning model for learning includes:
performing machine learning on the first application characteristic data set to obtain a first machine learning model;
inputting the second application characteristic data set into a first machine learning model for machine learning to obtain a second machine learning model;
and inputting the Nth application characteristic data set into a second machine learning model for machine learning to obtain an Nth machine learning model.
In some embodiments, the contribution degree analysis is performed on the first application feature data set, the second application feature data set and the nth application feature data set, the usage interest degree of the target user for the application program is determined according to the contribution degree, and the application program information matched with the usage interest degree is recommended to the target user according to the usage interest degree, and the method includes:
performing contribution degree analysis based on the first machine learning model, the second machine learning model and the Nth machine learning model as well as the first application feature data set, the second application feature data set and the Nth application feature data set to obtain contribution degrees of the first application feature data set, the second application feature data set and the Nth application feature data set to the machine learning model;
and determining the using interestingness of the target user on the application program according to the contribution degrees of the first application characteristic data set, the second application characteristic data set and the N application characteristic data set, and recommending the application program information matched with the interestingness to the target user according to the using interestingness.
In another aspect, the present invention also provides an application information recommendation system based on machine learning, where the system includes:
an application information obtaining unit, configured to obtain application usage information of a target user and usage frequency information thereof, and classify an application used by the target user into a first application class, a second application class, and an nth application class according to the application usage information;
the priority dividing unit is used for dividing the use priority of the first application program category, the second application program category and the Nth application program category according to the use frequency information, and extracting the application characteristics of each application program in the corresponding first application program category, second application program category and Nth application program category according to the use priority;
an application feature set unit, configured to set application features of each application program in the first application program category, the second application program category, and the nth application program category into a first application feature data set, a second application feature data set, and an nth application feature data set, respectively;
the application program information recommending unit is used for inputting the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set into the machine learning model for learning, carrying out contribution degree analysis on the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set, determining the using interestingness of the target user to the application program according to the contribution degree, and recommending the application program information matched with the using interestingness to the target user according to the using interestingness.
In another aspect, the present invention further provides a machine learning-based application information recommendation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the methods described above when the processor executes the computer program.
In another aspect, the invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of any of the methods described above.
The invention has the beneficial effects that: different from the prior art, the application program information recommendation method based on machine learning provided by the invention is characterized in that the application program used by the target user is classified into a first application program category, a second application program category and an Nth application program category according to the application program use information by acquiring the application program use information of the target user and the use frequency information of the application program; dividing the use priority of the first application program category, the second application program category and the N application program category according to the use frequency information, and extracting the application characteristics of each application program in the corresponding first application program category, second application program category and N application program category according to the use priority; respectively integrating application characteristics of each application program in the first application program category, the second application program category and the Nth application program category into a first application characteristic data set, a second application characteristic data set and an Nth application characteristic data set; inputting the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set into a machine learning model for learning, carrying out contribution degree analysis on the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set, determining the using interestingness of a target user on an application program according to the contribution degree, recommending application program information matched with the using interestingness to the target user according to the using interestingness, and recommending the application program information in place more accurately without recommending the application program to the user in a sea throwing mode, thereby reducing the anti-interestingness of the user on the application program without interests, improving the interest of the user on the recommended application program, facilitating the downloading of the application program with interests, improving the downloading efficiency of the recommended application program and saving unnecessary popularization cost.
Drawings
FIG. 1 is a flowchart of an implementation of a machine learning based application information recommendation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a machine learning based application information recommendation system according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an application information recommendation device based on machine learning in the third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The following describes in detail the implementation of the present invention in connection with specific embodiments:
example 1
Fig. 1 shows an implementation flow of an application information recommendation method based on machine learning according to an embodiment of the present invention. For convenience of explanation, only the parts related to the embodiments of the present invention are shown, and the details are as follows, specifically including the following steps, please refer to fig. 1:
s1: acquiring application program use information of a target user and use frequency information thereof, and classifying the application programs used by the target user into a first application program category, a second application program category and an Nth application program category according to the application program use information;
in step S1, user attribute information of the target user is further obtained, and the method is used for customizing application program category characteristics according to the user attribute information, and after inputting the custom application program category characteristics into machine learning, the machine learning depth can be corrected conveniently, so that application program information which is determined according to the custom application program category characteristics and can be recommended to the target user can be recommended to the user according to the user attribute information.
S2: dividing the first application program category, the second application program category and the N application program category into use priorities according to the use frequency information, and extracting application characteristics of each application program in the corresponding first application program category, second application program category and N application program category according to the use priorities;
in step S2, the specific method includes:
s201: respectively counting a first total use frequency of the first application program category, a second total use frequency of the second application program category and an Nth total use frequency of the Nth application program category;
s202: judging the first total use frequency, the second total use frequency and the N total use frequency: if the first total use frequency is greater than the second total use frequency and the second total use frequency is greater than the Nth total use frequency, dividing a first application program category corresponding to the first total use frequency into a first priority, dividing a second application program category corresponding to the second total use frequency into a second priority, and dividing an Nth application program category corresponding to the Nth total use frequency into an Nth priority;
s203: and sequentially acquiring each application program in the first application program category of the first priority, the second application program category of the second priority and the Nth application program category of the Nth priority according to the first priority, the second priority and the Nth priority, and extracting the application characteristics of each application program.
S3: respectively integrating application characteristics of each application program in the first application program category, the second application program category and the Nth application program category into a first application characteristic data set, a second application characteristic data set and an Nth application characteristic data set;
in step S3, the specific method includes:
s301: combining a plurality of application feature sets corresponding to the first priority into a first application feature data set;
s302: combining a plurality of application feature sets corresponding to the second priority into a second application feature data set;
s303: and integrating the plurality of application feature sets corresponding to the Nth priority into an Nth application feature data set.
S304: performing machine learning on the first application characteristic data set to obtain a first machine learning model;
s305: inputting the second application characteristic data set into the first machine learning model for machine learning to obtain a second machine learning model;
s306: and inputting the Nth application characteristic data set into a second machine learning model for machine learning to obtain an Nth machine learning model.
S4: inputting the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set into a machine learning model for learning, carrying out contribution analysis on the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set, determining the using interestingness of a target user on an application program according to the contribution, and recommending application program information matched with the using interestingness to the target user according to the using interestingness.
In step S4, the specific method includes:
s401: performing contribution degree analysis based on the first machine learning model, the second machine learning model, the Nth machine learning model, the first application feature data set, the second application feature data set and the Nth application feature data set to obtain contribution degrees of the first application feature data set, the second application feature data set and the Nth application feature data set to the machine learning model;
s402: and determining the using interest degree of the target user on the application program according to the contribution degrees of the first application feature data set, the second application feature data set and the N application feature data set, and recommending the application program information matched with the interest degree to the target user according to the using interest degree.
In this embodiment, N is a positive integer greater than 2.
Example two
Fig. 2 shows a structure of an application information recommendation system based on machine learning according to a second embodiment of the present invention. For convenience of explanation, only the portions related to the embodiments of the present invention are shown, referring to fig. 2, a machine learning-based application information recommendation system provided in a second embodiment of the present invention includes:
an application information obtaining unit 501, configured to obtain application usage information of a target user and usage frequency information thereof, and classify applications used by the target user into a first application class, a second application class, and an nth application class according to the application usage information;
a priority dividing unit 502, configured to divide a usage priority for a first application class, a second application class, and an nth application class according to the usage frequency information, and extract application features of each application in the first application class, the second application class, and the nth application class according to the usage priority;
an application feature collection unit 503, configured to collect application features of each application program in the first application program category, the second application program category, and the nth application program category into a first application feature data set, a second application feature data set, and an nth application feature data set, respectively;
the application information recommending unit 504 is configured to input the first application feature data set, the second application feature data set, and the nth application feature data set into the machine learning model for learning, perform contribution analysis on the first application feature data set, the second application feature data set, and the nth application feature data set, determine a usage interest level of the target user in the application according to the contribution level, and recommend application information matching the usage interest level to the target user according to the usage interest level.
Obtaining a first application program type, a second application program type and an N application program type with priority and a first application characteristic data set, a second application characteristic data set and an N application characteristic data set corresponding to each type according to the obtained application program use information of the target user and the use frequency information of the target user; the first application characteristic data set, the second application characteristic data set and the Nth application characteristic data set are input into the machine learning model for learning to obtain contribution degrees, then the using interestingness of the target user for the application program is determined according to the contribution degrees, and then the application program information matched with the using interestingness is recommended to the target user, so that the application program information recommendation is more accurate and in place, the interest of the user for the recommended application program is improved, the downloading efficiency of the recommended application program is improved, and unnecessary popularization cost is saved.
In the embodiment of the invention, each unit of the application program information recommendation system based on machine learning can be realized by corresponding hardware or software units, and each unit can be an independent software and hardware unit or can be integrated into one software and hardware unit, so that the invention is not limited.
Example III
Fig. 3 shows a structure of an application information recommendation device based on machine learning according to a third embodiment of the present invention, and for convenience of explanation, only a portion related to the embodiment of the present invention is shown, please refer to fig. 3.
The machine learning-based application information recommendation device 2 provided in the third embodiment of the present invention includes a memory 201, a processor 202, and a computer program 203 stored in the memory 201 and executable on the processor 202, and the steps S1 to S4 of the machine learning-based application information recommendation method provided in the first embodiment are implemented when the processor 202 executes the computer program 203. Alternatively, the processor 202 implements the respective unit functions of the machine learning-based application information recommendation system provided in the above-described second embodiment, such as the functions of the units 501 to 504 shown in fig. 2, when executing the computer program 203.
Example IV
A fourth embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements steps S1 to S4 of the machine learning-based application information recommendation method provided in the first embodiment. Alternatively, the processor 202 implements the respective unit functions of the machine learning-based application information recommendation system provided in the above-described second embodiment, such as the functions of the units 501 to 504 shown in fig. 2, when executing the computer program 203.
The computer readable storage medium of embodiments of the present invention may include any entity or device capable of carrying computer program code, recording medium, such as ROM/RAM, magnetic disk, optical disk, flash memory, and so on.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (6)
1. A machine learning based application information recommendation method, the method comprising the steps of:
acquiring application program use information of a target user and use frequency information thereof, and classifying application programs used by the target user into a first application program category, a second application program category and an Nth application program category according to the application program use information;
dividing the use priority of the first application program category, the second application program category and the N application program category according to the use frequency information, and extracting the application characteristics of each application program in the corresponding first application program category, second application program category and N application program category according to the use priority;
combining a plurality of application feature sets corresponding to the first priority into a first application feature data set; combining a plurality of application feature sets corresponding to the second priority into a second application feature data set; combining a plurality of application feature sets corresponding to the nth priority into an nth application feature data set;
performing machine learning on the first application characteristic data set to obtain a first machine learning model; inputting the second application characteristic data set into a first machine learning model for machine learning to obtain a second machine learning model; inputting the Nth application characteristic data set into a second machine learning model for machine learning to obtain an Nth machine learning model; performing contribution degree analysis based on the first machine learning model, the second machine learning model and the Nth machine learning model as well as the first application feature data set, the second application feature data set and the Nth application feature data set to obtain contribution degrees of the first application feature data set, the second application feature data set and the Nth application feature data set to the machine learning model; and determining the using interestingness of the target user on the application program according to the contribution degrees of the first application characteristic data set, the second application characteristic data set and the N application characteristic data set, and recommending the application program information matched with the interestingness to the target user according to the using interestingness.
2. The machine learning-based application information recommendation method of claim 1, wherein prioritizing usage of a first application class, a second application class, and an nth application class according to the usage frequency information comprises:
respectively counting a first total use frequency of the first application program category, a second total use frequency of the second application program category and an Nth total use frequency of the Nth application program category;
judging the first total use frequency, the second total use frequency and the N total use frequency: if the first total use frequency is greater than the second total use frequency and the second total use frequency is greater than the Nth total use frequency, dividing the first application program category corresponding to the first total use frequency into a first priority, dividing the second application program category corresponding to the second total use frequency into a second priority, and dividing the Nth application program category corresponding to the Nth total use frequency into an Nth priority.
3. The machine learning based application information recommendation method of claim 2, wherein extracting application features of each application in the corresponding first, second and nth application categories according to the usage priority comprises:
and sequentially acquiring each application program in the first application program category of the first priority, the second application program category of the second priority and the Nth application program category of the Nth priority according to the first priority, the second priority and the Nth priority, and extracting the application characteristics of each application program.
4. A machine learning based application information recommendation system, the system comprising:
an application information obtaining unit, configured to obtain application usage information of a target user and usage frequency information thereof, and classify an application used by the target user into a first application class, a second application class, and an nth application class according to the application usage information;
the priority dividing unit is used for dividing the use priority of the first application program category, the second application program category and the Nth application program category according to the use frequency information, and extracting the application characteristics of each application program in the corresponding first application program category, second application program category and Nth application program category according to the use priority;
an application feature set unit, configured to combine a plurality of application feature sets corresponding to the first priority into a first application feature data set; combining a plurality of application feature sets corresponding to the second priority into a second application feature data set; combining a plurality of application feature sets corresponding to the nth priority into an nth application feature data set;
the application program information recommending unit is used for performing machine learning on the first application characteristic data set to obtain a first machine learning model; inputting the second application characteristic data set into a first machine learning model for machine learning to obtain a second machine learning model; inputting the Nth application characteristic data set into a second machine learning model for machine learning to obtain an Nth machine learning model; performing contribution degree analysis based on the first machine learning model, the second machine learning model and the Nth machine learning model as well as the first application feature data set, the second application feature data set and the Nth application feature data set to obtain contribution degrees of the first application feature data set, the second application feature data set and the Nth application feature data set to the machine learning model; and determining the using interestingness of the target user on the application program according to the contribution degrees of the first application characteristic data set, the second application characteristic data set and the N application characteristic data set, and recommending the application program information matched with the interestingness to the target user according to the using interestingness.
5. A machine learning based application information recommendation device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 3 when executing the computer program.
6. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 3.
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