CN116739704A - E-commerce platform interest analysis type commodity recommendation method and system based on artificial intelligence - Google Patents
E-commerce platform interest analysis type commodity recommendation method and system based on artificial intelligence Download PDFInfo
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Abstract
The application discloses an electronic commerce platform interest analysis type commodity recommendation method and system based on artificial intelligence, and relates to the technical field of data processing. The method comprises the following steps: extracting a user voice signal and a video signal containing a user face; intercepting any section of video signal as a video to be detected, carrying out recognition and verification on the user identity, and if the verification result is that the user identity is consistent with the user account, carrying out recognition on the user voice signal to obtain a voice recognition result; performing concentration detection on each frame of image in the video signal to obtain a plurality of concentration detection results; and determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the E-commerce platform system, forming a commodity recommendation set, and recommending the commodity recommendation set to the corresponding user. The application combines multiple models to realize accurate user identity recognition verification, voice recognition and concentration detection, thereby realizing accurate and effective commodity recommendation.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an electronic commerce platform interest analysis type commodity recommendation method and system based on artificial intelligence.
Background
With the development of internet technology and mobile payment, an e-commerce platform becomes one of the mainstream modes of modern social shopping. For users, the electronic commerce platform is provided with richer commodity types and specifications, so that the increasingly-increased personalized requirements of the users can be met; for merchants, the e-commerce platform not only can reduce the operation cost, but also can increase the exposure rate and sales volume.
However, the electronic commerce platform has a plurality of commodity types, so that the user cannot efficiently select the target commodity, and the time cost of the user in the commodity selection process is greatly increased. With the development of technology in the artificial intelligence field, the interest and hobbies of the user can be fully analyzed, and direct support is provided for targeted recommendation of commodities. Therefore, the method and the system for recommending the electronic commerce platform interest analysis type commodity based on the artificial intelligence have very important value and significance.
Disclosure of Invention
In order to overcome the problems or at least partially solve the problems, the application provides an electronic commerce platform interest analysis type commodity recommendation method and system based on artificial intelligence, which are combined with a multi-dimensional image coding matching type identity verification model based on video frame optimization, an end-to-end voice recognition model based on voice denoising and multi-recognition module combined application and an adaptive consumption type concentration detection model based on classification confidence analysis to realize accurate user identity recognition verification, voice recognition and concentration detection, thereby realizing accurate and effective commodity recommendation.
In order to solve the technical problems, the application adopts the following technical scheme:
in a first aspect, the application provides an interest analysis type commodity recommendation method of an e-commerce platform based on artificial intelligence, which comprises the following steps:
after a user logs in an E-commerce platform system by using a corresponding user account number and a password, extracting a user voice signal and a video signal containing a user face based on audio and video extraction equipment;
intercepting any section of video signal as a video to be detected, and utilizing a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user so as to generate a verification result;
if the verification result is that the user identity is consistent with the user account, recognizing the user voice signal by using an end-to-end voice recognition model based on voice denoising and multi-recognition module joint application so as to obtain a voice recognition result;
performing concentration detection on each frame of image in the video signal by using an adaptive consumption concentration detection model based on classification confidence analysis to obtain a plurality of concentration detection results;
and determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the E-commerce platform system, forming a commodity recommendation set, and recommending the commodity recommendation set to the corresponding user.
Firstly, the application provides a multi-dimensional image coding matching type identity verification model based on video frame optimization, and the identity of a user is identified and verified; the model selects the high-quality frame image of the video, and carries out multidimensional coding matching on the high-quality frame image of the video and the template face image on the basis, so that more accurate identification verification is realized. Secondly, the application provides an end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition module, which is used for recognizing the voice signal; the model can jointly apply the voice denoising network module, the plurality of voice recognition network modules and the voice recognition result comparison module, thereby realizing end-to-end accurate voice recognition. Finally, the application also provides a self-adaptive consumption type concentration detection model based on classification confidence analysis, which carries out concentration detection on each frame of image in the video signal; when a simple concentration detection model is used, if a concentration detection result with higher classification confidence can be obtained, a more complex concentration detection model is not needed to be utilized; otherwise, the complex concentration detection model is utilized to complete concentration detection. The concentration detection mode can ensure the precision of concentration detection and reduce the consumption of computing resources.
Based on the first aspect, the method for identifying and checking the identity of the user by using the multi-dimensional image coding matching type identity checking model based on video frame preference further comprises the following steps:
detecting each frame of image in the video to be detected by using a peak signal-to-noise ratio detection model so as to select a plurality of high-quality frame images;
respectively carrying out high-dimension coding on each high-quality frame image and a preset template face image by using a high-dimension self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of high-dimension similarity results;
respectively carrying out high-dimensional coding on each high-quality frame image and a preset template face image by using a low-dimensional self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of low-dimensional similarity results;
and if all the high-dimensional similarity results and the low-dimensional similarity results are higher than the preset similarity threshold, the identity of the user is confirmed to be consistent with the user account.
Based on the first aspect, the method for recognizing the user voice signal by using the end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition module further comprises the following steps:
connecting a plurality of voice recognition network modules adopting different voice recognition algorithms in parallel, connecting a voice denoising network module at the front ends of the voice recognition network modules together, and connecting a voice recognition result comparison module at the rear ends of the voice recognition network modules together to form an end-to-end voice recognition model;
and recognizing the voice signal of the user through the end-to-end voice recognition model.
Based on the first aspect, the method for recognizing the user voice signal through the end-to-end voice recognition model further comprises the following steps:
inputting the user voice signal into a voice denoising network module for denoising processing so as to obtain a denoising voice signal;
the denoising voice signals are respectively input into a plurality of voice recognition network modules to carry out voice recognition so as to obtain a plurality of recognition results;
and inputting the multiple recognition results into a voice recognition result comparison module for comparison and analysis, and outputting a final voice recognition result if the multiple recognition results are consistent.
Based on the first aspect, the method for performing concentration detection on each frame of image in the video signal by using the self-adaptive consumption concentration detection model based on classification confidence analysis further comprises the following steps:
selecting a plurality of face images with high concentration as positive samples, and selecting a plurality of person images with low concentration as negative samples;
selecting a part of positive samples and a part of negative samples to train the SVM model so as to obtain a concentration detection model based on the SVM model;
training the convolutional neural network by using all positive samples and all negative samples to obtain a concentration detection model based on the convolutional neural network;
performing multi-scale reconstruction on any frame of image to obtain images with multiple scales;
detecting the images of a plurality of scales by using a concentration degree detection model based on an SVM model to obtain a plurality of detection results;
if the detection results are consistent, outputting a final concentration detection result; otherwise, detecting the image by using a concentration detection model based on a convolutional neural network to obtain a final concentration detection result.
In a second aspect, the application provides an electronic commerce platform interest analysis type commodity recommendation system based on artificial intelligence, which comprises a signal extraction module, an identity verification module, a voice recognition module, a concentration detection module and a commodity recommendation module, wherein:
the signal extraction module is used for extracting a user voice signal and a video signal containing a user face based on audio and video extraction equipment after a user logs in an electronic commerce platform system by using a corresponding user account number and a corresponding password;
the identity verification module is used for intercepting any section of video signal as a video to be detected, and utilizing a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user so as to generate a verification result;
the voice recognition module is used for recognizing the voice signal of the user by utilizing an end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition module if the verification result is that the user identity is consistent with the user account number, so as to obtain a voice recognition result;
the concentration detection module is used for carrying out concentration detection on each frame of image in the video signal by utilizing an adaptive consumption concentration detection model based on classification confidence analysis so as to obtain a plurality of concentration detection results;
and the commodity recommendation module is used for determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the electronic commerce platform system, forming a commodity recommendation set and recommending the commodity recommendation set to the corresponding user.
The system realizes accurate user identity recognition verification, voice recognition and concentration detection through the combination of a plurality of modules such as a signal extraction module, an identity verification module, a voice recognition module, a concentration detection module, a commodity recommendation module and the like, and further realizes accurate and effective commodity recommendation. Firstly, the system utilizes a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user; the model selects the high-quality frame image of the video, and carries out multidimensional coding matching on the high-quality frame image of the video and the template face image on the basis, so that more accurate identification verification is realized. Secondly, the system utilizes an end-to-end voice recognition model based on the joint application of voice denoising and multiple recognition modules to recognize voice signals; the model can jointly apply the voice denoising network module, the plurality of voice recognition network modules and the voice recognition result comparison module, thereby realizing end-to-end accurate voice recognition. Finally, the system also utilizes a self-adaptive consumption type concentration detection model based on classification confidence analysis to detect the concentration of each frame of image in the video signal; when a simple concentration detection model is used, if a concentration detection result with higher classification confidence can be obtained, a more complex concentration detection model is not needed to be utilized; otherwise, the complex concentration detection model is utilized to complete concentration detection. The concentration detection mode can ensure the precision of concentration detection and reduce the consumption of computing resources.
In a third aspect, the present application provides an electronic device comprising a memory for storing one or more programs; a processor; the method of any of the first aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the first aspects described above.
The application has at least the following advantages or beneficial effects:
the application provides an interest analysis type commodity recommendation method and system based on an e-commerce platform of artificial intelligence, which are combined with a multidimensional image coding matching type identity verification model based on video frame optimization, an end-to-end voice recognition model based on voice denoising and multi-recognition module joint application and a self-adaptive consumption type concentration detection model based on classification confidence analysis to realize accurate user identity recognition verification, voice recognition and concentration detection, thereby realizing accurate and effective commodity recommendation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an interest analysis type commodity recommendation method of an e-commerce platform based on artificial intelligence according to an embodiment of the application;
FIG. 2 is a flowchart of user identification verification in an E-commerce platform interest analysis type commodity recommendation method based on artificial intelligence according to an embodiment of the present application;
FIG. 3 is a flowchart of voice recognition in an interest analysis type commodity recommendation method of an e-commerce platform based on artificial intelligence according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of an interest analysis type commodity recommendation system of an e-commerce platform based on artificial intelligence according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 100. a signal extraction module; 200. an identity verification module; 300. a voice recognition module; 400. a concentration detection module; 500. a commodity recommendation module; 101. a memory; 102. a processor; 103. a communication interface.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the embodiments of the present application, "plurality" means at least 2.
Examples:
1-3, in a first aspect, the present application provides an interest analysis type commodity recommendation method for an e-commerce platform based on artificial intelligence, which includes the following steps:
s1, after a user logs in an electronic commerce platform system by using a corresponding user account number and a corresponding password, extracting a user voice signal and a video signal containing a user face based on audio and video extraction equipment;
s2, intercepting any section of video signal as a video to be detected, and utilizing a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user so as to generate a verification result;
further, as shown in fig. 2, includes:
s21, detecting each frame of image in the video to be detected by using a peak signal-to-noise ratio detection model so as to select a plurality of high-quality frame images;
s22, respectively carrying out high-dimensional coding on each high-quality frame image and a preset template face image by using a high-dimensional self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of high-dimensional similarity results;
s23, respectively carrying out high-dimensional coding on each high-quality frame image and a preset template face image by using a low-dimensional self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of low-dimensional similarity results;
and S24, if all the high-dimensional similarity results and the low-dimensional similarity results are higher than a preset similarity threshold, the user identity is determined to be consistent with the user account.
In some embodiments of the present application, a portion of the video signal (3-5 seconds may be taken) is intercepted as the video to be detected, and the user identity is identified and checked by using a multi-dimensional image encoding matching type identity check model based on video frame preference. If the user identity is consistent with the corresponding identity of the account, continuing to the next step.
The video frame-based preferred multi-dimensional image coding matching type identity verification model specifically comprises the following steps: and detecting each frame image in the video to be detected by using a peak signal-to-noise ratio detection model, and selecting a plurality of (usually 10) frame images with high peak signal-to-noise ratio as high-quality frame images. For the 1 st high-quality frame image and the template face image (the template face image corresponding to the account number is stored in the system), respectively carrying out high-dimensional coding on the 1 st high-quality frame image and the template face image by using a high-dimensional self-encoder, and calculating the similarity of the high-dimensional frame image and the template face image by using the Euclidean distance to obtain high-dimensional coding similarity; and respectively carrying out low-dimensional coding on the two codes by using a low-dimensional self-coder, and calculating the similarity of the two codes by using the Euclidean distance to obtain the low-dimensional coding similarity. And if the coding similarity of different dimensions is higher, the similarity between the 1 st high-quality frame image and the template face image is higher. According to the method, the similarity between the rest high-quality frame image and the template face image is calculated. And if the similarity between all the high-quality frame images and the template face images is higher, the identity of the user is identified to be consistent with the corresponding identity of the account. And (3) injection: the high-dimension self-encoder can obtain a coding result with higher dimension; the low-dimension self-encoder can obtain the encoding result with lower dimension.
S3, if the verification result shows that the user identity is consistent with the user account, recognizing the user voice signal by using an end-to-end voice recognition model based on voice denoising and multi-recognition module combined application so as to obtain a voice recognition result;
further, the method comprises the steps of: connecting a plurality of voice recognition network modules adopting different voice recognition algorithms in parallel, connecting a voice denoising network module at the front ends of the voice recognition network modules together, and connecting a voice recognition result comparison module at the rear ends of the voice recognition network modules together to form an end-to-end voice recognition model; and recognizing the voice signal of the user through the end-to-end voice recognition model.
Further, as shown in fig. 3, includes:
s31, inputting a user voice signal into a voice denoising network module for denoising processing so as to obtain a denoising voice signal;
s32, respectively inputting the denoising voice signals into a plurality of voice recognition network modules to perform voice recognition so as to obtain a plurality of recognition results;
s33, inputting the plurality of recognition results into a voice recognition result comparison module for comparison and analysis, and outputting a final voice recognition result if the plurality of recognition results are consistent.
In some embodiments of the present application, speech signals are identified using an end-to-end speech recognition model based on a joint application of speech denoising and multiple recognition modules. If the user is identified to say words expressing like, satisfaction or approval, such as 'too excellent', 'me like', the user is identified to have a higher interest in the merchandise being viewed at that time.
The end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition modules specifically comprises the steps of connecting a voice recognition network module A, a voice recognition network module B and a voice recognition network module C in parallel (the voice recognition algorithms used by the network modules are different), connecting a voice denoising network module together at the front ends of the voice recognition network module A, connecting a voice recognition result comparison module together at the rear ends of the voice recognition network module A, the voice recognition network module B and the voice recognition network module C, and forming the end-to-end voice recognition model together. Firstly, an input voice signal is subjected to voice denoising network module to obtain a denoised voice signal. Secondly, the denoised voice signal passes through a voice recognition network module A to obtain a voice recognition result A; the denoised voice signal passes through a voice recognition network module B to obtain a voice recognition result B; the denoised voice signal passes through a voice recognition network module C to obtain a voice recognition result C. Finally, the voice recognition result A, the voice recognition result B and the voice recognition result C are subjected to a voice recognition result comparison module together, and if the three voice recognition results are consistent, the final voice recognition result is output.
S4, performing concentration detection on each frame of image in the video signal by using an adaptive consumption concentration detection model based on classification confidence analysis to obtain a plurality of concentration detection results;
further, the method comprises the steps of: selecting a plurality of face images with high concentration as positive samples, and selecting a plurality of person images with low concentration as negative samples; selecting a part of positive samples and a part of negative samples to train the SVM model so as to obtain a concentration detection model based on the SVM model; training the convolutional neural network by using all positive samples and all negative samples to obtain a concentration detection model based on the convolutional neural network; performing multi-scale reconstruction on any frame of image to obtain images with multiple scales; detecting the images of a plurality of scales by using a concentration degree detection model based on an SVM model to obtain a plurality of detection results; if the detection results are consistent, outputting a final concentration detection result; otherwise, detecting the image by using a concentration detection model based on a convolutional neural network to obtain a final concentration detection result.
In some embodiments of the present application, for most of all frame images within a period of time (about 5 seconds), it is possible to detect that the concentration of the user is high, and identify that the interest of the user in the merchandise being viewed is high.
The self-adaptive consumption type concentration detection model based on classification confidence analysis specifically comprises the following steps: and selecting a sufficient amount of face images with higher concentration as a positive sample, and selecting a sufficient amount of face images with lower concentration as a negative sample. And selecting partial positive samples and partial negative samples, and training the SVM model to obtain the concentration detection model based on the SVM. And training the convolutional neural network by using all positive samples and all negative samples to obtain a concentration detection model based on the convolutional neural network. For a certain frame of image, constructing the image into images with multiple scales, and respectively detecting the images with the multiple scales by using an SVM-based concentration detection model. If the image detection results of the multiple scales are consistent (concentration degree is higher or lower), directly obtaining concentration degree detection results; if the image detection results of the multiple scales are inconsistent, detecting the frame image by using a concentration detection model based on a convolutional neural network (the detection is carried out under the original scale), and obtaining a final concentration detection result.
And S5, determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the E-commerce platform system, forming a commodity recommendation set, and recommending the commodity recommendation set to the corresponding user.
In the steps S3 and S4, if any one of the steps obtains a result that the interest degree of the user in the commodity watched at the moment is high, the commodity watched by the user is directly recorded, and the commodity in the same category and other highly relevant categories is recommended to the user for watching. Any step obtains a result that the interest degree of the user on the commodity watched at the moment is high, directly records the commodity watched by the user, and recommends the commodity in the same category and other highly relevant categories to the user for watching.
Firstly, the application provides a multi-dimensional image coding matching type identity verification model based on video frame optimization, and the identity of a user is identified and verified; the model selects the high-quality frame image of the video, and carries out multidimensional coding matching on the high-quality frame image of the video and the template face image on the basis, so that more accurate identification verification is realized. Secondly, the application provides an end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition module, which is used for recognizing the voice signal; the model can jointly apply the voice denoising network module, the plurality of voice recognition network modules and the voice recognition result comparison module, thereby realizing end-to-end accurate voice recognition. Finally, the application also provides a self-adaptive consumption type concentration detection model based on classification confidence analysis, which carries out concentration detection on each frame of image in the video signal; when a simple concentration detection model is used, if a concentration detection result with higher classification confidence can be obtained, a more complex concentration detection model is not needed to be utilized; otherwise, the complex concentration detection model is utilized to complete concentration detection. The concentration detection mode can ensure the precision of concentration detection and reduce the consumption of computing resources.
As shown in fig. 4, in a second aspect, an embodiment of the present application provides an electronic commerce platform interest analysis type commodity recommendation system based on artificial intelligence, which includes a signal extraction module 100, an identity verification module 200, a voice recognition module 300, an concentration detection module 400, and a commodity recommendation module 500, wherein:
the signal extraction module 100 is configured to extract a user voice signal and a video signal including a user face based on an audio/video extraction device after a user logs in an e-commerce platform system by using a corresponding user account and password;
the identity verification module 200 is used for intercepting any section of video signal as a video to be detected, and performing identification verification on the identity of a user by using a multi-dimensional image coding matching type identity verification model based on video frame optimization to generate a verification result;
the voice recognition module 300 is configured to recognize a user voice signal by using an end-to-end voice recognition model based on joint application of the voice denoising and multiple recognition modules if the verification result indicates that the user identity is consistent with the user account, so as to obtain a voice recognition result;
the concentration detection module 400 is configured to perform concentration detection on each frame of image in the video signal by using an adaptive consumption concentration detection model based on classification confidence analysis, so as to obtain a plurality of concentration detection results;
the commodity recommendation module 500 is configured to determine and record a commodity with high user interest according to the voice recognition result and the multiple concentration detection results, extract related commodities in the e-commerce platform system, form a commodity recommendation set, and recommend the commodity recommendation set to a corresponding user.
The system realizes accurate user identity recognition verification, voice recognition and concentration detection through the combination of a plurality of modules such as the signal extraction module 100, the identity verification module 200, the voice recognition module 300, the concentration detection module 400, the commodity recommendation module 500 and the like, and further realizes accurate and effective commodity recommendation. Firstly, the system utilizes a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user; the model selects the high-quality frame image of the video, and carries out multidimensional coding matching on the high-quality frame image of the video and the template face image on the basis, so that more accurate identification verification is realized. Secondly, the system utilizes an end-to-end voice recognition model based on the joint application of voice denoising and multiple recognition modules to recognize voice signals; the model can jointly apply the voice denoising network module, the plurality of voice recognition network modules and the voice recognition result comparison module, thereby realizing end-to-end accurate voice recognition. Finally, the system also utilizes a self-adaptive consumption type concentration detection model based on classification confidence analysis to detect the concentration of each frame of image in the video signal; when a simple concentration detection model is used, if a concentration detection result with higher classification confidence can be obtained, a more complex concentration detection model is not needed to be utilized; otherwise, the complex concentration detection model is utilized to complete concentration detection. The concentration detection mode can ensure the precision of concentration detection and reduce the consumption of computing resources.
As shown in fig. 5, in a third aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The method of any of the first aspects described above is implemented when one or more programs are executed by the processor 102.
And a communication interface 103, where the memory 101, the processor 102 and the communication interface 103 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules that are stored within the memory 101 for execution by the processor 102 to perform various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other manners. The above-described method and system embodiments are merely illustrative, for example, flow charts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by the processor 102, implements a method as in any of the first aspects described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. The interest analysis type commodity recommendation method for the e-commerce platform based on the artificial intelligence is characterized by comprising the following steps of:
after a user logs in an E-commerce platform system by using a corresponding user account number and a password, extracting a user voice signal and a video signal containing a user face based on audio and video extraction equipment;
intercepting any section of video signal as a video to be detected, and utilizing a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user so as to generate a verification result;
if the verification result is that the user identity is consistent with the user account, recognizing the user voice signal by using an end-to-end voice recognition model based on voice denoising and multi-recognition module joint application so as to obtain a voice recognition result;
performing concentration detection on each frame of image in the video signal by using an adaptive consumption concentration detection model based on classification confidence analysis to obtain a plurality of concentration detection results;
and determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the E-commerce platform system, forming a commodity recommendation set, and recommending the commodity recommendation set to the corresponding user.
2. The method for recommending commodities according to an interest analysis type of an e-commerce platform based on artificial intelligence according to claim 1, wherein the method for identifying and verifying the identity of the user by using a multi-dimensional image coding matching type identity verification model based on video frame optimization comprises the following steps:
detecting each frame of image in the video to be detected by using a peak signal-to-noise ratio detection model so as to select a plurality of high-quality frame images;
respectively carrying out high-dimension coding on each high-quality frame image and a preset template face image by using a high-dimension self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of high-dimension similarity results;
respectively carrying out high-dimensional coding on each high-quality frame image and a preset template face image by using a low-dimensional self-coder, and calculating the similarity between each high-quality frame image and the template face image to obtain a plurality of low-dimensional similarity results;
and if all the high-dimensional similarity results and the low-dimensional similarity results are higher than the preset similarity threshold, the identity of the user is confirmed to be consistent with the user account.
3. The method for recommending commodities according to an interest analysis type of an e-commerce platform based on artificial intelligence according to claim 1, wherein the method for recognizing the user voice signal by using an end-to-end voice recognition model based on the joint application of voice denoising and multiple recognition modules comprises the following steps:
connecting a plurality of voice recognition network modules adopting different voice recognition algorithms in parallel, connecting a voice denoising network module at the front ends of the voice recognition network modules together, and connecting a voice recognition result comparison module at the rear ends of the voice recognition network modules together to form an end-to-end voice recognition model;
and recognizing the voice signal of the user through the end-to-end voice recognition model.
4. The method for recommending commodities according to an interest analysis type of an e-commerce platform based on artificial intelligence according to claim 3, wherein the method for recognizing the user's voice signal through the end-to-end voice recognition model comprises the steps of:
inputting the user voice signal into a voice denoising network module for denoising processing so as to obtain a denoising voice signal;
the denoising voice signals are respectively input into a plurality of voice recognition network modules to carry out voice recognition so as to obtain a plurality of recognition results;
and inputting the multiple recognition results into a voice recognition result comparison module for comparison and analysis, and outputting a final voice recognition result if the multiple recognition results are consistent.
5. The method for recommending commodities according to an interest analysis type of an e-commerce platform based on artificial intelligence according to claim 1, wherein the method for performing concentration detection on each frame of image in a video signal by using an adaptive consumption concentration detection model based on classification confidence analysis comprises the following steps:
selecting a plurality of face images with high concentration as positive samples, and selecting a plurality of person images with low concentration as negative samples;
selecting a part of positive samples and a part of negative samples to train the SVM model so as to obtain a concentration detection model based on the SVM model;
training the convolutional neural network by using all positive samples and all negative samples to obtain a concentration detection model based on the convolutional neural network;
performing multi-scale reconstruction on any frame of image to obtain images with multiple scales;
detecting the images of a plurality of scales by using a concentration degree detection model based on an SVM model to obtain a plurality of detection results;
if the detection results are consistent, outputting a final concentration detection result; otherwise, detecting the image by using a concentration detection model based on a convolutional neural network to obtain a final concentration detection result.
6. The utility model provides an electronic commerce platform interest analysis formula commodity recommendation system based on artificial intelligence which characterized in that includes signal extraction module, identity verification module, speech recognition module, concentration detection module and commodity recommendation module, wherein:
the signal extraction module is used for extracting a user voice signal and a video signal containing a user face based on audio and video extraction equipment after a user logs in an electronic commerce platform system by using a corresponding user account number and a corresponding password;
the identity verification module is used for intercepting any section of video signal as a video to be detected, and utilizing a multi-dimensional image coding matching type identity verification model based on video frame optimization to carry out identification verification on the identity of a user so as to generate a verification result;
the voice recognition module is used for recognizing the voice signal of the user by utilizing an end-to-end voice recognition model based on the joint application of the voice denoising and multi-recognition module if the verification result is that the user identity is consistent with the user account number, so as to obtain a voice recognition result;
the concentration detection module is used for carrying out concentration detection on each frame of image in the video signal by utilizing an adaptive consumption concentration detection model based on classification confidence analysis so as to obtain a plurality of concentration detection results;
and the commodity recommendation module is used for determining and recording commodities with high user interest according to the voice recognition result and the concentration detection results, extracting related commodities in the electronic commerce platform system, forming a commodity recommendation set and recommending the commodity recommendation set to the corresponding user.
7. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method of any of claims 1-5 is implemented when the one or more programs are executed by the processor.
8. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-5.
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