CN113254804B - Social relationship recommendation method and system based on user attributes and behavior characteristics - Google Patents
Social relationship recommendation method and system based on user attributes and behavior characteristics Download PDFInfo
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Abstract
The invention discloses a social relationship recommendation method and a social relationship recommendation system based on user attributes and behavior characteristics, wherein the method comprises the following steps: performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user; generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user; constructing a social relationship recommendation interaction database; inputting the preset matching portrait information into the social relationship recommendation interaction database, and training based on a recommendation algorithm to obtain a first matching object of the first user; and feeding back the first matching object to the first user based on the social relationship recommendation system. The method solves the technical problems that in the prior art, social relationship recommendation does not combine personal characteristics of users, so that recommendation results are not specific and accuracy is low.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a social relationship recommendation method and system based on user attributes and behavior characteristics.
Background
The social relationship recommendation network covers all network service forms taking human social as a core, and is an interactive platform capable of mutual communication, intercommunication and mutual participation, so that the Internet is expanded into a tool for human social from research departments, schools, governments and commercial application platforms, and is also a tool for enriching network social.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the social relationship recommendation in the prior art does not combine personal characteristics of users, so that the recommendation result has no specificity and is not high in accuracy.
Disclosure of Invention
The embodiment of the application provides a social relationship recommendation method and system based on user attributes and behavior characteristics, solves the technical problems that in the prior art, social relationship recommendation does not combine personal characteristics of users, so that recommendation results are not specific and low in accuracy, and achieves the technical effect that social relationship recommendation is more accurate based on the user attributes and the behavior characteristics in an artificial intelligence mode so as to help the users to perform personalized recommendation by using user figures.
In view of the above, the present invention has been developed to provide a solution to, or at least partially solve, the above problems.
In a first aspect, an embodiment of the present application provides a social relationship recommendation method based on user attributes and behavior characteristics, where the method includes: acquiring personal audit data information of a first user and generating a personal basic tag of the first user; obtaining an application use record of the first user according to the electronic communication equipment of the first user; constructing a social space group of the first user according to the application use record; obtaining social expression information and social behavior information of the first user based on the social space group; performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user; generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user; constructing a social relationship recommendation interaction database, wherein the social relationship recommendation interaction database is contained in the social relationship recommendation system; inputting the preset matching portrait information into the social relationship recommendation interaction database, training based on a recommendation algorithm, and obtaining a first matching object of the first user, wherein the first user and the first matching object have a first mapping relationship; and feeding back the first matching object to the first user based on the social relationship recommendation system.
In another aspect, the present application further provides a social relationship recommendation system based on user attributes and behavior characteristics, the system including: the first obtaining unit is used for obtaining personal audit data information of a first user and generating a personal basic label of the first user; a second obtaining unit, configured to obtain an application usage record of the first user according to the electronic communication device of the first user; the first construction unit is used for constructing the social space group of the first user according to the application use record; a third obtaining unit, configured to obtain social expression information and social behavior information of the first user based on the social space group; the first generating unit is used for performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user; a fourth obtaining unit, configured to generate a first portrait of the first user according to the personal basic tag and the behavior feature tag, and obtain preset matching portrait information of the first user; the second construction unit is used for constructing a social relationship recommendation interaction database, wherein the social relationship recommendation interaction database is contained in the social relationship recommendation system; a fifth obtaining unit, configured to input the preset matching portrait information into the social relationship recommendation interaction database, train based on a recommendation algorithm, and obtain a first matching object of the first user, where the first user and the first matching object have a first mapping relationship; the first feedback unit is used for feeding back the first matching object to the first user based on the social relationship recommendation system.
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the method for controlling output data includes any one of the steps described above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for controlling output data according to any one of the above.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining personal audit data information of a first user and generating a personal basic label of the first user; obtaining an application use record of the first user according to the electronic communication equipment of the first user; performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user; generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user; inputting the preset matching portrait information into the social relationship recommendation interaction database, training based on a recommendation algorithm, and obtaining a first matching object of the first user, wherein the first user and the first matching object have a first mapping relationship; and feeding back the first matching object to the first user based on the social relationship recommendation system. And further, the social relationship recommendation is more accurate based on the user attributes and the behavior characteristics in an artificial intelligence mode, so that the user portrait is utilized to help the user to perform personalized recommendation.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of a social relationship recommendation method based on user attributes and behavior characteristics according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a social relationship recommendation system based on user attributes and behavior characteristics according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device for executing a method of controlling output data according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a first generating unit 15, a fourth obtaining unit 16, a second constructing unit 17, a fifth obtaining unit 18, a first feedback unit 19, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150 and a user interface 1160.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
The first embodiment is as follows: as shown in fig. 1, an embodiment of the present application provides a social relationship recommendation method based on user attributes and behavior characteristics, where the method includes:
step S100: acquiring personal audit data information of a first user and generating a personal basic tag of the first user;
specifically, the personal audit data information is the personal data information submitted by the user, such as name, age, gender, location area, birthday, height, working and economic conditions, personal preference, character, quality and appearance, concept and concept, and the like. And generating personal basic labels of the user according to the audit data information, wherein the personal basic labels comprise labels of personal basic conditions, special hobbies, working conditions and the like.
Step S200: obtaining an application use record of the first user according to the electronic communication equipment of the first user;
specifically, the application use record of the user is obtained through the electronic communication equipment of the user, including equipment such as a mobile phone, a computer and the like, wherein the application use record comprises the use application categories such as media playing software, shopping software, professional software, education and entertainment software, graphic image software and the like and the application use time.
Step S300: constructing a social space group of the first user according to the application use record;
further, in an embodiment of the present invention, the constructing a social space group of the first user according to the application usage record, step S300 further includes:
step S310: obtaining a first social application set according to the application usage records, wherein the application usage records in the first social application set reach a preset usage frequency and a preset usage duration;
step S320: and constructing a social space group of the first user according to the first social application set.
Specifically, a social application set of the user is obtained through the application usage record, and social applications in the social application set are applications with preset usage frequency and preset usage duration, which indicate that the social applications are frequently used by the user and have referential property. And constructing a social space group of the user according to the social application set, wherein the social space group is a social space network of the user and comprises social characteristic information of the user. The technical effect of analyzing the social characteristics of the user through the application use condition of the user is achieved, and the social analysis of the user is more accurate.
Step S400: obtaining social expression information and social behavior information of the first user based on the social space group;
specifically, according to the social space group of the user, the social expression information of the user is obtained, wherein the social expression information comprises daily social expression , scene society social expression and the like, and social behavior information, namely the actual social behavior of the user, comprising social interaction capacity, social interaction means and the like.
Step S500: performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user;
further, in step S500 of generating the behavior feature tag of the first user in the embodiment of the present application, the method further includes:
step S510: according to the social expression information, obtaining the appearance behavior characteristics of the first user;
step S520: according to the social behavior information, acquiring the avatar behavior characteristics of the first user;
step S530: judging whether the representation behavior characteristics and the representation behavior characteristics are consistent or not;
step S540: if the representation behavior characteristics and the representation behavior characteristics are not consistent, generating first additional label information;
step S550: and correcting the behavior characteristic label according to the first additional label information to obtain the actual behavior characteristic label of the first user.
Specifically, the behavior characteristics are acquired and analyzed to some extent by permission of the electronic device, such as recording/photographing. The first user's apparent behavior characteristics are the user's social expression information, and the first user's apparent behavior characteristics are the user's specific social behavior information. And judging whether the representation behavior characteristics and the representation behavior characteristics are consistent or not, namely judging whether the language rows of the user are consistent or not, and if the representation behavior characteristics and the representation behavior characteristics are not consistent or not, namely the language rows of the user are inconsistent, generating first additional tag information, namely the actual social behavior tag of the user. And correcting the behavior characteristic label according to the first additional label information to obtain the actual behavior characteristic label of the first user. The technical effect of ensuring the consistency of the social lines of the users is achieved, so that the generated behavior characteristics are the actual behavior characteristics of the users.
Step S600: generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user;
specifically, a user portrait is a user portrait generated by connecting attributes and behaviors of a user with expected data conversion, and is widely used in various fields as an effective tool for delineating a target user and associating user appeal with a design direction. And generating a user portrait of the user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the user for portrait matching with a social relationship recommendation database.
Step S700: constructing a social relationship recommendation interaction database, wherein the social relationship recommendation interaction database is contained in the social relationship recommendation system;
further, in the building of the social relationship recommendation interaction database, step S700 in the embodiment of the present application further includes:
step S710: collecting a social account set of a user based on big data;
step S720: generating a first access instruction based on the social relationship recommendation system;
step S730: obtaining a user individual image set corresponding to the social account set according to the first access instruction;
step S740: performing cluster analysis on the user individual image set to obtain a first analysis result;
step S750: and constructing the social relationship recommendation interaction database according to the first analysis result.
Specifically, each social account set of the user is collected based on a big data mode, and big data collection has high decision power, insight discovery power and flow optimization capacity, and is a huge amount of information assets with high growth rate and diversification. The social relationship recommendation system carries out social relationship recommendation based on user data information, and obtains a user individual image set corresponding to the social account set according to an access instruction of the system. And carrying out cluster analysis on the user individual image set, wherein clustering is an unsupervised learning method, and a cluster label is inferred based on the structure of data without any label. And obtaining a first clustering result through the steps of clustering analysis, feature engineering and modeling parameter adjustment, wherein the first clustering result is the number of clusters which are divided according to relevant features after clustering, obtaining the association among the figures of the user through clustering analysis, and constructing the social relationship recommendation interaction database for social relationship recommendation. The technical effect that the association between the portrait of the user and the social relationship is more accurate so as to realize accurate recommendation of the social relationship is achieved.
Step S800: inputting the preset matching portrait information into the social relationship recommendation interaction database, training based on a recommendation algorithm, and obtaining a first matching object of the first user, wherein the first user and the first matching object have a first mapping relationship;
specifically, the preset matching portrait information is input into the social relationship recommendation interaction database, training is performed based on a recommendation algorithm, the recommendation algorithm is one of computer specialties, and things which a user may like are presumed through some mathematical algorithms. The system examines the matching degree of user data and items to be predicted based on the characteristics of user evaluation objects and the interest of learning users, and a data model of the user depends on the used learning method, and commonly used methods include a decision tree, a neural network, a vector-based representation method and the like. And obtaining an output result, namely a first matching object of the first user through a recommendation algorithm, wherein the first user and the first matching object have a first mapping relation, and if the user and the matching object have the same interest mapping relation.
Further, before obtaining the first matching object of the first user, step S800 in this embodiment of the present application further includes:
step S810: recommending an interactive database according to the social relationship to obtain a user image set;
step S820: obtaining a target convolution characteristic of the preset matching portrait information;
step S830: performing traversal convolution operation on the user image set according to the target convolution characteristic to obtain a first convolution result;
step S840: obtaining a first matching group according to the first convolution result, wherein the first matching group and the preset matching portrait information have a second mapping relation;
step S850: obtaining a first matching image set according to the first matching group;
step S860: and obtaining a first matching object of the first user according to the preset matching portrait information and the first matching portrait set.
Specifically, the user image set is obtained through the social relationship recommendation interactive database, and the convolutional neural network is a deep feedforward neural network with the characteristics of local connection, weight sharing and the like, has a remarkable effect in the field of image and video analysis, such as various visual tasks of image classification, target detection, image segmentation and the like, and is one of the most widely applied models at present. A convolutional neural network, literally comprising two parts: convolution + neural network. The convolution is a feature extractor, and the neural network can be regarded as a classifier. A convolutional neural network is trained, namely a feature extractor (convolution) and a subsequent classifier (neural network) are trained simultaneously. And performing traversal convolution operation on the user image set according to the target convolution characteristics of the preset matching image information to obtain a corresponding first convolution result, wherein the first convolution result is a result obtained after feature training is performed through a convolution neural network. And analyzing the first convolution result to obtain a corresponding matching group, wherein the first matching group has a mapping relation with the preset matching portrait information. And obtaining a corresponding matching image set according to the first matching group, and obtaining a recommended object matched with the user according to the preset matching image information and the characteristic analysis of the first matching image set. The technical effect of realizing accurate recommendation on the matching object of the user in a convolutional network characteristic classification mode is achieved.
Step S900: and feeding back the first matching object to the first user based on the social relationship recommendation system.
Specifically, a recommendation object matched with the user is obtained based on a recommendation result output by the social relationship recommendation system, and the first matching object is fed back to the first user for realizing the social relationship personalized recommendation conforming to the user characteristics.
Further, in an embodiment of the present invention, the obtaining a first matching object of the first user according to the preset matching image information and the first matching image set, step S860 further includes:
step S861: obtaining a target image set of the first matching group according to the first matching image set;
step S862: inputting the first image into the target image set for feature analysis to obtain a first fused image;
step S863: and obtaining a corresponding first matching object according to the first fused portrait, wherein the first fused portrait meets the target requirement of the first matching object and has a first relevance with the first portrait.
Specifically, a target image set of the first matching group is obtained according to the first matching image set, and a target image set matching the same interest is obtained according to the interest feature image set. And inputting the first image based on the personal characteristics of the user into the target image set for characteristic analysis to obtain a first fused image after characteristic fusion. The first fused portrait meets the target requirement of the first matching object and has a degree of association with the first portrait, and a corresponding matching object is obtained. The technical effect that the matched object is more accurate through portrait feature fusion and accurate social relationship recommendation is achieved.
Further, step S750 in the embodiment of the present application further includes:
step S751: generating a first characteristic data set according to the user individual image set;
step S752: performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
step S753: obtaining a first covariance matrix of the second feature data set;
step S754: calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
step S755: and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set after dimension reduction of the first feature data set.
Specifically, the feature data extracted from the user individual portrait set is subjected to digitization processing, a feature data set matrix is constructed, and the first feature data set is obtained. Then, centralization processing is carried out on each feature data in the first feature data set, namely mean value removing is carried out, firstly, the mean value of each feature in the first feature data set is solved, then, the mean value of each feature is subtracted from all samples, then, a new feature value is obtained, the second feature data set is formed by the new feature value, and the second feature data set is a data matrix. By the covariance formula:
and operating the second characteristic data set to obtain a first covariance matrix of the second characteristic data set. Wherein the content of the first and second substances,
characteristic data in the second characteristic data set;
is the average value of the characteristic data;
the total amount of sample data in the second feature data set. Then, through matrix operation, the eigenvalue and the eigenvector of the first covariance matrix are solved, and each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the corresponding characteristic vectors from the obtained first characteristic vectors, and projecting the original characteristics in the first characteristic data set onto the selected characteristic vectors to obtain the first characteristic data set after dimension reduction. The feature data in the database are subjected to dimensionality reduction processing through a principal component analysis method, and redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data in the database is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the operation speed of a training model on the data is accelerated.
To sum up, the social relationship recommendation method and system based on the user attributes and the behavior characteristics provided by the embodiment of the application have the following technical effects:
the method comprises the steps of obtaining personal audit data information of a first user and generating a personal basic label of the first user; obtaining an application use record of the first user according to the electronic communication equipment of the first user; performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user; generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user; inputting the preset matching portrait information into the social relationship recommendation interaction database, training based on a recommendation algorithm, and obtaining a first matching object of the first user, wherein the first user and the first matching object have a first mapping relationship; and feeding back the first matching object to the first user based on the social relationship recommendation system. And further, the social relationship recommendation is more accurate based on the user attributes and the behavior characteristics in an artificial intelligence mode, so that the user portrait is utilized to help the user to perform personalized recommendation.
Example two: based on the same inventive concept as the social relationship recommendation method based on the user attributes and the behavior characteristics in the foregoing embodiment, the present invention further provides a social relationship recommendation system based on the user attributes and the behavior characteristics, as shown in fig. 2, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain personal audit data information of a first user, and generate a personal basic tag of the first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain an application usage record of the first user according to the electronic communication device of the first user;
a first constructing unit 13, where the first constructing unit 13 is configured to construct a social space group of the first user according to the application usage record;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain social expression information and social behavior information of the first user based on the social space group;
a first generating unit 15, where the first generating unit 15 is configured to perform feature fusion analysis on the social expression information and the social behavior information, and generate a behavior feature tag of the first user;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to generate a first portrait of the first user according to the personal basic tag and the behavior feature tag, and obtain preset matching portrait information of the first user;
the second construction unit 17 is configured to construct a social relationship recommendation interaction database, where the social relationship recommendation interaction database is included in the social relationship recommendation system;
a fifth obtaining unit 18, where the fifth obtaining unit 18 is configured to input the preset matching portrait information into the social relationship recommendation interaction database, train based on a recommendation algorithm, and obtain a first matching object of the first user, where the first user and the first matching object have a first mapping relationship;
a first feedback unit 19, where the first feedback unit 19 is configured to feed back the first matching object to the first user based on the social relationship recommendation system.
Further, the system further comprises:
a sixth obtaining unit, configured to recommend an interaction database according to the social relationship, and obtain a user image set;
a seventh obtaining unit, configured to obtain a target convolution feature of the preset matching image information;
an eighth obtaining unit, configured to perform traversal convolution operation on the user image set according to the target convolution feature, so as to obtain a first convolution result;
a ninth obtaining unit, configured to obtain a first matching group according to the first convolution result, where the first matching group and the preset matching image information have a second mapping relationship;
a tenth obtaining unit, configured to obtain a first matching image set according to the first matching population;
an eleventh obtaining unit, configured to obtain a first matching object of the first user according to the preset matching image information and the first matching image set.
Further, the system further comprises:
a twelfth obtaining unit, configured to obtain, according to the first matching image set, a target image set of the first matching group;
a thirteenth obtaining unit, configured to input the first image into the target image set for feature analysis, so as to obtain a first fused image;
a fourteenth obtaining unit, configured to obtain a corresponding first matching object according to the first fused sketch, where the first fused sketch satisfies a target requirement of the first matching object and has a first association with the first sketch.
Further, the system further comprises:
a fifteenth obtaining unit, configured to obtain an appearance behavior feature of the first user according to the social expression information;
a sixteenth obtaining unit, configured to obtain an avatar behavior feature of the first user according to the social behavior information;
the first judging unit is used for judging whether the representation behavior characteristics and the representation behavior characteristics are consistent or not;
the second generation unit is used for generating first additional label information if the representation behavior characteristics and the representation behavior characteristics are not consistent;
a seventeenth obtaining unit, configured to correct the behavior feature tag according to the first additional tag information, and obtain an actual behavior feature tag of the first user.
Further, the system further comprises:
an eighteenth obtaining unit, configured to obtain a first social application set according to the application usage record, where the application usage record in the first social application set reaches a preset usage frequency and a preset usage duration;
a third constructing unit, configured to construct a social space group of the first user according to the first social application set.
Further, the system further comprises:
the first acquisition unit is used for acquiring a social account set of a user based on big data;
a third generating unit, configured to generate a first access instruction based on the social relationship recommendation system;
a nineteenth obtaining unit, configured to obtain, according to the first access instruction, a user individual image set corresponding to the social account set;
a twentieth obtaining unit, configured to perform cluster analysis on the user individual image set to obtain a first analysis result;
and the fourth construction unit is used for constructing the social relationship recommendation interaction database according to the first analysis result.
Further, the system further comprises:
a fourth generation unit, configured to generate a first feature data set according to the user individual image set;
a twenty-first obtaining unit, configured to perform centralized processing on the first feature data set to obtain a second feature data set;
a twenty-second obtaining unit for obtaining a first covariance matrix of the second feature data set;
a twenty-third obtaining unit, configured to perform operation on the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
a twenty-fourth obtaining unit, configured to project the first feature data set to the first feature vector, and obtain a first dimension-reduced data set, where the first dimension-reduced data set is a feature data set after dimension reduction of the first feature data set.
Various changes and specific examples of the social relationship recommendation method based on the user attributes and the behavior characteristics in the first embodiment of fig. 1 are also applicable to the social relationship recommendation system based on the user attributes and the behavior characteristics in the present embodiment, and through the foregoing detailed description of the social relationship recommendation method based on the user attributes and the behavior characteristics, those skilled in the art can clearly know an implementation method of the social relationship recommendation system based on the user attributes and the behavior characteristics in the present embodiment, so for the brevity of the description, detailed description is not repeated here.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Specifically, referring to fig. 3, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for mobile communications, code division multiple access, global microwave interconnect access, general packet radio service, wideband code division multiple access, long term evolution, LTE frequency division duplex, LTE time division duplex, long term evolution-advanced, universal mobile communications, enhanced mobile broadband, mass machine type communications, ultra-reliable low latency communications, etc.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the above method for controlling output data, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A social relationship recommendation method based on user attributes and behavior characteristics is applied to a social relationship recommendation system, and the method further comprises the following steps:
acquiring personal audit data information of a first user and generating a personal basic tag of the first user;
obtaining an application use record of the first user according to the electronic communication equipment of the first user;
constructing a social space group of the first user according to the application use record;
obtaining social expression information and social behavior information of the first user based on the social space group;
performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user;
generating a first portrait of the first user according to the personal basic label and the behavior feature label, and obtaining preset matching portrait information of the first user;
constructing a social relationship recommendation interaction database, wherein the social relationship recommendation interaction database is contained in the social relationship recommendation system;
inputting the preset matching portrait information into the social relationship recommendation interaction database, training based on a recommendation algorithm, and obtaining a first matching object of the first user, wherein the first user and the first matching object have a first mapping relationship;
feeding back the first matching object to the first user based on the social relationship recommendation system;
before the obtaining of the first matching object of the first user, the method further includes:
recommending an interactive database according to the social relationship to obtain a user image set;
obtaining a target convolution characteristic of the preset matching portrait information;
performing traversal convolution operation on the user image set according to the target convolution characteristic to obtain a first convolution result;
obtaining a first matching group according to the first convolution result, wherein the first matching group and the preset matching portrait information have a second mapping relation;
obtaining a first matching image set according to the first matching group;
obtaining a first matching object of the first user according to the preset matching portrait information and the first matching portrait set;
the obtaining of the first matching object of the first user according to the preset matching portrait information and the first matching portrait set further includes:
obtaining a target image set of the first matching group according to the first matching image set;
inputting the first image into the target image set for feature analysis to obtain a first fused image;
obtaining a corresponding first matching object according to the first fused portrait, wherein the first fused portrait meets the target requirement of the first matching object and has a first degree of association with the first portrait;
the generating the behavior feature tag of the first user further comprises:
according to the social expression information, obtaining the appearance behavior characteristics of the first user;
according to the social behavior information, acquiring the avatar behavior characteristics of the first user;
judging whether the representation behavior characteristics and the representation behavior characteristics are consistent or not;
if the representation behavior characteristics and the representation behavior characteristics are not consistent, generating first additional label information;
and correcting the behavior characteristic label according to the first additional label information to obtain the actual behavior characteristic label of the first user.
2. The method of claim 1, wherein said building a social space group for the first user from the application usage record further comprises:
obtaining a first social application set according to the application usage records, wherein the application usage records in the first social application set reach a preset usage frequency and a preset usage duration;
and constructing a social space group of the first user according to the first social application set.
3. The method of claim 1, wherein the building a social relationship recommendation interaction database further comprises:
collecting a social account set of a user based on big data;
generating a first access instruction based on the social relationship recommendation system;
obtaining a user individual image set corresponding to the social account set according to the first access instruction;
performing cluster analysis on the user individual image set to obtain a first analysis result;
and constructing the social relationship recommendation interaction database according to the first analysis result.
4. The method of claim 2, wherein the method further comprises:
generating a first characteristic data set according to the user individual image set;
performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
obtaining a first covariance matrix of the second feature data set;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set after dimension reduction of the first feature data set.
5. A social relationship recommendation system based on user attributes and behavioral characteristics, wherein the system comprises:
the first obtaining unit is used for obtaining personal audit data information of a first user and generating a personal basic label of the first user;
a second obtaining unit, configured to obtain an application usage record of the first user according to the electronic communication device of the first user;
the first construction unit is used for constructing the social space group of the first user according to the application use record;
a third obtaining unit, configured to obtain social expression information and social behavior information of the first user based on the social space group;
the first generating unit is used for performing feature fusion analysis on the social expression information and the social behavior information to generate a behavior feature tag of the first user;
a fourth obtaining unit, configured to generate a first portrait of the first user according to the personal basic tag and the behavior feature tag, and obtain preset matching portrait information of the first user;
the second construction unit is used for constructing a social relationship recommendation interaction database, wherein the social relationship recommendation interaction database is contained in the social relationship recommendation system;
a fifth obtaining unit, configured to input the preset matching portrait information into the social relationship recommendation interaction database, train based on a recommendation algorithm, and obtain a first matching object of the first user, where the first user and the first matching object have a first mapping relationship;
the first feedback unit is used for feeding back the first matching object to the first user based on the social relationship recommendation system;
the system further comprises:
a sixth obtaining unit, configured to recommend an interaction database according to the social relationship, and obtain a user image set;
a seventh obtaining unit, configured to obtain a target convolution feature of the preset matching image information;
an eighth obtaining unit, configured to perform traversal convolution operation on the user image set according to the target convolution feature, so as to obtain a first convolution result;
a ninth obtaining unit, configured to obtain a first matching group according to the first convolution result, where the first matching group and the preset matching image information have a second mapping relationship;
a tenth obtaining unit, configured to obtain a first matching image set according to the first matching population;
an eleventh obtaining unit, configured to obtain a first matching object of the first user according to the preset matching image information and the first matching image set;
a twelfth obtaining unit, configured to obtain, according to the first matching image set, a target image set of the first matching group;
a thirteenth obtaining unit, configured to input the first image into the target image set for feature analysis, so as to obtain a first fused image;
a fourteenth obtaining unit, configured to obtain a corresponding first matching object according to the first fused representation, where the first fused representation satisfies a target requirement of the first matching object and has a first association degree with the first representation;
a fifteenth obtaining unit, configured to obtain an appearance behavior feature of the first user according to the social expression information;
a sixteenth obtaining unit, configured to obtain an avatar behavior feature of the first user according to the social behavior information;
the first judging unit is used for judging whether the representation behavior characteristics and the representation behavior characteristics are consistent or not;
the second generation unit is used for generating first additional label information if the representation behavior characteristics and the representation behavior characteristics are not consistent;
a seventeenth obtaining unit, configured to correct the behavior feature tag according to the first additional tag information, and obtain an actual behavior feature tag of the first user.
6. An electronic device for social relationship recommendation based on user attributes and behavioural characteristics, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program realizes the steps of the method as claimed in any one of claims 1-4 when executed by the processor.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-4.
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| CN114155067B (en) * | 2022-02-10 | 2022-04-22 | 北京派瑞威行互联技术有限公司 | User portrait establishing method and system |
| CN115047924A (en) * | 2022-06-20 | 2022-09-13 | 深圳市朗大科技有限公司 | Interactive control method and system of portable medicine patch hot compress instrument |
| CN116361566A (en) * | 2023-03-29 | 2023-06-30 | 竞速信息技术(廊坊)有限公司 | User relationship recommendation method and device based on big data |
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