A consumer user classification method and system based on multi-view clustering

Abstract

The present invention discloses a consumer user classification method and system based on multi-view clustering, which is used to solve the technical problem that the existing consumer user classification technology based on multi-view clustering leads to poor reliability of classification results. The method includes obtaining multiple consumer behavior data, and using a preset similarity algorithm to perform similarity graph learning on the multiple consumer behavior data to generate multiple initial similarity graph matrices; separating each initial similarity graph matrix to determine the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; outputting independence criteria and values ​​and high-order information between views within a view based on the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; using the independence criteria and values ​​and consistency tensor to optimize each initial similarity graph matrix respectively, and determining the target similarity graph matrix corresponding to each initial similarity graph matrix; and generating consumer user classification results based on the multiple target similarity graph matrices.

Description

Technical Field

[0001] The present invention relates to the technical field of cluster analysis, and in particular to a consumer user classification method and system based on multi-view clustering. Background Art

[0002] With the widespread adoption of internet technology, consumers are demanding higher levels of experience, experiencing a greater diversity of products, and increasing their enthusiasm for consumption. To better meet these needs, consumer recommendation systems have become a key technology for major platforms to enhance user experience and boost sales. Recommendation systems analyze a variety of information, including historical user behavior data, product attributes, and social network data, using cluster analysis to segment target customers. This allows them to recommend appropriate products and encourage consumption.

[0003] Multi-view clustering has long been widely used in many fields, such as data mining and analysis, and is now attracting increasing attention. This is primarily due to the rapid development of information technology, which has led to an explosive growth in multi-view data. Clustering is a commonly used analytical technique when faced with massive amounts of data, and multi-view clustering is particularly important because a sample in multi-view data must be represented by features from multiple views. In real life, a sample often has different attributes and can be expressed in various ways. For example, online objects can be described by photos, text, video, or audio. For consumers, analysis should be conducted based on five aspects: click-to-read, favorite, add to cart, purchase, and after-sales service. The consistent features from multiple views are then merged, and consumers are grouped based on certain feature similarities.

[0004] Most existing consumer user classification technologies based on multi-view clustering use matrix-based methods or tensor-based methods for clustering. However, these methods simply treat each consumer behavior as an independent feature and cannot accurately capture the full picture of user behavior patterns, resulting in poor reliability of classification results. Summary of the Invention

[0005] The present invention provides a consumer user classification method and system based on multi-view clustering, which are used to solve the technical problem that the existing consumer user classification technology based on multi-view clustering leads to poor reliability of classification results.

[0006] A first aspect of the present invention provides a consumer user classification method based on multi-view clustering, comprising:

[0007] Acquire multiple consumer behavior data, and use a preset similarity algorithm to perform similarity graph learning on the multiple consumer behavior data to generate multiple initial similarity graph matrices;

[0008] Separating each of the initial similarity graph matrices to determine a consistency matrix and an inconsistency matrix corresponding to each of the initial similarity graph matrices;

[0009] Using a preset multi-view processing algorithm to output independence criteria and values ​​and intra-view and inter-view high-order information based on the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices;

[0010] Optimizing each of the initial similarity graph matrices using the independence criterion and value and the consistency tensor, and determining a target similarity graph matrix corresponding to each of the initial similarity graph matrices;

[0011] generating a fused similarity graph matrix according to the plurality of target similarity graph matrices;

[0012] Perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results.

[0013] Optionally, the preset multi-view processing algorithm includes an independence criterion and a double-enhanced tensor rank minimization algorithm; the intra-view and inter-view high-order information includes inter-view high-order information and intra-view high-order information; and the using of the preset multi-view processing algorithm to output the independence criterion and value and the intra-view and inter-view high-order information according to the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices includes:

[0014] Determining a plurality of independence criterion values ​​according to the inconsistency matrix corresponding to each of the initial similarity graph matrices using an independence criterion;

[0015] Summing the independence criterion values ​​to determine a sum of the independence criterion values;

[0016] stacking the consistency matrices to generate a consistency stack tensor;

[0017] Rotating the consistent stacked tensor to determine a consistent rotated tensor;

[0018] A double-augmented tensor rank minimization algorithm is used to determine, based on the consistent stacking tensor and the consistent rotation tensor, the intra-view high-order information corresponding to the consistent stacking tensor and the inter-view high-order information corresponding to the consistent rotation tensor.

[0019] Optionally, generating a fused similarity graph matrix according to the plurality of target similarity graph matrices includes:

[0020] Separating each of the target similarity graph matrices and determining a target consistency matrix corresponding to each of the target similarity graph matrices;

[0021] A plurality of target consistency matrices are fused to generate a fused similarity graph matrix.

[0022] Optionally, the construction process of the initial similarity graph matrix is ​​specifically as follows:

[0023] ;

[0024] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization.

[0025] Optionally, the separation process of the initial similarity graph matrix is ​​specifically as follows:

[0026] ;

[0027] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; is a trade-off parameter.

[0028] Optionally, the optimization process of the initial similarity graph matrix is ​​specifically as follows:

[0029] ;

[0030] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; For trade-off parameters; is an adjustable parameter; is the independence criterion and value; For the independence principle; It is high-level information in the view; It is the high-level information between views; For stacking; is a rotation; A is a consistent stacking tensor; Rotate the tensor for consistency.

[0031] A second aspect of the present invention provides a consumer user classification system based on multi-view clustering, comprising:

[0032] An acquisition module is used to acquire a plurality of consumer behavior data and perform similarity graph learning on the plurality of consumer behavior data using a preset similarity algorithm to generate a plurality of initial similarity graph matrices;

[0033] A separation module is used to separate the initial similarity graph matrices and determine the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices;

[0034] An output module, configured to output an inconsistency independence criterion and value and intra-view and inter-view high-order information based on a consistency matrix and an inconsistency matrix corresponding to each of the initial similarity graph matrices using a preset multi-view processing algorithm;

[0035] an optimization module, configured to optimize each of the initial similarity graph matrices using the inconsistency independence criterion and value and the consistency tensor, and determine a target similarity graph matrix corresponding to each of the initial similarity graph matrices;

[0036] A generating module, configured to generate a fused similarity graph matrix based on the plurality of target similarity graph matrices;

[0037] The spectral clustering module is used to perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results.

[0038] A third aspect of the present invention provides a computer device comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the consumer user classification method based on multi-view clustering as described in any one of the above items.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the consumer user classification method based on multi-view clustering as described in any one of the above.

[0040] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the consumer user classification method based on multi-view clustering as described in any one of the above items.

[0041] It can be seen from the above technical solutions that the present invention has the following advantages:

[0042] The above technical solution of the present invention provides a consumer user classification method based on multi-view clustering, which first obtains multiple consumer behavior data, and uses a preset similarity algorithm to perform similarity graph learning on the multiple consumer behavior data to generate multiple initial similarity graph matrices; then, separates each initial similarity graph matrix, and determines the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; uses a preset multi-view processing algorithm to output independence criteria and values ​​and high-order information between views within a view according to the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; uses the independence criteria and values ​​and consistency tensor to optimize each initial similarity graph matrix respectively, and determines the target similarity graph matrix corresponding to each initial similarity graph matrix; based on multiple target similarity graph matrices, Generate a fused similarity graph matrix; finally, perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results; based on the above scheme, combined with a preset multi-view processing algorithm and a preset similarity algorithm, process multiple consumer behavior data, output independence criteria and values ​​and high-order information between views within a view, and use the independence criteria and values ​​and high-order information between views within a view to optimize each initial similarity graph matrix, determine the target similarity graph matrix corresponding to each initial similarity graph matrix, and then perform spectral clustering on the fused similarity graph matrix generated according to multiple target similarity graph matrices to generate consumer user classification results. This process can better consider higher-order correlations between and within behaviors, thereby better grouping consumers and improving the reliability of the classification results. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A flowchart of a method for classifying consumer users based on multi-view clustering provided in the first embodiment of the present invention;

[0045] Figure 2 This is an overall framework diagram of the consumer user classification method based on multi-view clustering provided in the first embodiment of the present invention;

[0046] Figure 3 A flowchart of a consumer user classification method based on multi-view clustering provided in the second embodiment of the present invention;

[0047] Figure 4 This is a structural block diagram of a consumer user classification system based on multi-view clustering provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0048] The embodiments of the present invention provide a consumer user classification method and system based on multi-view clustering, which are used to solve the technical problem that the existing consumer user classification technology based on multi-view clustering leads to poor reliability of classification results.

[0049] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 , Figure 1 This is a flowchart of the steps of a consumer user classification method based on multi-view clustering provided in Example 1 of the present invention.

[0051] The present invention provides a consumer user classification method based on multi-view clustering, comprising:

[0052] Step 101: Acquire multiple consumer behavior data, and use a preset similarity algorithm to perform similarity graph learning on the multiple consumer behavior data to generate multiple initial similarity graph matrices.

[0053] Multiple consumer behavior data include multiple consumers' reading, collection, adding to shopping cart, purchase, after-sales and other behavior data.

[0054] It should be noted that the preset similarity algorithm uses Euclidean distance learning similarity to learn the similarity matrix, thereby obtaining the initial similarity graph matrix corresponding to each initial similarity graph. This is also a commonly used similarity calculation method in multi-view clustering. The construction process of the initial similarity graph matrix is ​​as follows:

[0055] ;

[0056] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization.

[0057] Step 102: Separate each initial similarity graph matrix and determine the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix.

[0058] It should be noted that the same consumer may have some unique records when performing different behaviors. These records will cause the similarity matrix S to contain incorrect sample similarities. Therefore, for the accuracy of the results, the present invention will separate these special records for processing. Therefore, the present invention separates the initial similarity graph matrix S into a consistent part A (consistency matrix) and an inconsistent part E (inconsistency matrix). Inconsistency here is a broader definition than noise. The separation process of the initial similarity graph matrix is ​​specifically as follows:

[0059] ;

[0060] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; is a trade-off parameter.

[0061] Step 103: Use a preset multi-view processing algorithm to output independence criteria and values ​​and intra-view and inter-view high-order information based on the consistency matrix and the inconsistency matrix corresponding to each initial similarity graph matrix.

[0062] The preset multi-view processing algorithms include the independence criterion and the doubly enhanced tensor rank minimization algorithm. The independence criterion is the Hilbert-Schmidt Independence Criterion (HSIC).

[0063] The intra-view and inter-view high-order information includes inter-view high-order information and intra-view high-order information.

[0064] Specifically, step 103 may include the following sub-steps S31-S35:

[0065] Step S31, using the independence criterion to determine multiple independence criterion values ​​according to the inconsistency matrix corresponding to each initial similarity graph matrix;

[0066] It should be noted that to explore the complementary information between behaviors, the Hilbert-Schmidt independence criterion is used to enhance the diversity of inconsistencies. The Hilbert-Schmidt independence criterion (HSIC) is a method for testing the independence of random variables. Based on the theory of kernel methods, it determines whether two random variables are independent by calculating the degree of dependence between them. If two variables are independent, their covariance should be zero.

[0067] Furthermore, the processing principle of the Hilbert-Schmidt independence criterion is as follows: given two random variables X and Y, choose an appropriate sum function k X and k Y , in order to map X and Y into a high-dimensional feature space and calculate the kernel matrix K of X and Y X and K Y, then each kernel matrix is ​​centered to eliminate the influence of the mean, and finally the HSIC value is calculated. The closer the HSIC value is to zero, the more independent X and Y are; the larger the HSIC value, the stronger the dependence between them.

[0068] Step S32: summing the independence criterion values ​​to determine the independence criterion sum value;

[0069] Step S33: stack the consistency matrices to generate a consistency stack tensor;

[0070] Step S34: rotating the consistent stacked tensor to determine a consistent rotated tensor;

[0071] Step S35 : Using a double-augmented tensor rank minimization algorithm, based on the consistent stacking tensor and the consistent rotation tensor, determine the intra-view high-order information corresponding to the consistent stacking tensor and the inter-view high-order information corresponding to the consistent rotation tensor.

[0072] It should be noted that, based on the diversified separation of inconsistent information, the present invention uses the hidden Figure 1 The high-order information of the consistent part is used to improve the performance of the model. However, the tensor nuclear norm, which is usually used to explore the high-order information of the view, has the same penalty for different singular values ​​and cannot well approximate the true rank of the tensor. Therefore, this paper introduces a double-enhanced tensor rank minimization algorithm. The definition of the enhanced tensor rank is as follows:

[0073] ;

[0074] in, To enhance the tensor rank and represent high-order information; is the dimension of the third dimension of the tensor; To represent the tensor P f The bth frontal slice of ; is an adjustable parameter, ; is a tensor Singular values ​​obtained after tensor singular value decomposition in the Fourier domain; is an adjustable parameter; t is a square matrix Dimensions, , for The dimension of the first dimension, for The dimensionality of the second dimension.

[0075] Furthermore, in order to simultaneously obtain high-level information between views and within views, the present invention stacks all consistency matrices to obtain tensor A, and rotates tensor A to obtain , tensor rank minimization (double enhanced tensor rank minimization algorithm) is used to process A and , then A can be used to mine high-level information within the attempt, Used to mine high-level information between views.

[0076] Step 104: Optimize each initial similarity graph matrix using the independence criterion and value and the consistency tensor, and determine the target similarity graph matrix corresponding to each initial similarity graph matrix.

[0077] It should be noted that the optimization process of the initial similarity graph matrix is ​​as follows:

[0078] ;

[0079] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; For trade-off parameters; is an adjustable parameter; is the independence criterion and value; For the independence principle; It is high-level information in the view; It is the high-level information between views; For stacking; is a rotation; A is a consistent stacking tensor; Rotate the tensor for consistency.

[0080] Step 105: Generate a fused similarity graph matrix based on multiple target similarity graph matrices.

[0081] It should be noted that the consistent parts of all target similarity graph matrices are fused to obtain a unified fused similarity graph matrix Z.

[0082] Step 106: Perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results.

[0083] It should be noted that spectral clustering is performed on Z to obtain the clustering results (consumer user classification results).

[0084] For example, see Figure 2 First, a preset similarity algorithm is used to process the input multiple consumer behavior data to obtain multiple initial similarity graph matrices. Each initial similarity graph matrix is ​​separated, and the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix are determined. Then, combined with the preset multi-view processing algorithm, all consistency matrices and inconsistency matrices are processed, and the independence criterion and value and high-order information between views within a view are output. The independence criterion and value and high-order information between views within a view are used to optimize each initial similarity graph matrix, and the target similarity graph matrix corresponding to each initial similarity graph matrix is ​​determined. Finally, spectral clustering is performed on the fused similarity graph matrix generated according to multiple target similarity graph matrices to generate consumer user classification results.

[0085] For comparison purposes, we can refer to existing technologies. Numerous methods have been proposed to improve the performance of multi-view clustering for multi-view data. Existing multi-view clustering methods can be broadly categorized into matrix-based and tensor-based methods. Matrix-based methods can be further categorized into subspace learning, matrix factorization, and graph learning. Among these methods, graph-based clustering methods can effectively learn similarity relationships between samples, achieving excellent clustering results. Meanwhile, tensor-based clustering methods have become a hot topic in multi-view clustering due to their excellent performance in capturing high-order information from views. However, these methods suffer from the following issues: 1) they fail to account for the diversity of inconsistent information between consumer behaviors, resulting in poor performance; 2) they fail to adequately account for higher-order correlations between and within behaviors; and 3) they impose the same penalty on different singular values ​​of the tensor.

[0086] In response to the above problems, the present invention proposes a consumer user classification method based on multi-view clustering. By analyzing various behaviors of consumers and using a multi-view clustering method based on tensor rank minimization and consideration of view inconsistency, consumers can be better grouped and suitable products can be recommended to different consumer groups. Specifically, consumers' reading, collecting, adding to shopping carts, purchasing, after-sales and other behaviors are sampled, and the initial similarity graphs of each behavior (i.e., views) are extracted, and the factors of inconsistency between views are considered. The inconsistency may be noise caused by consumer erroneous operations, or it may be a view-specific feature. Enhanced tensor rank minimization is used to enhance the high-order correlation of consistency information between and within views, and the consistency tensors of each view are fused into a graph. The fused graph is spectrally clustered to achieve consumer grouping, and suitable products are recommended to consumers based on related searches.

[0087] In an embodiment of the present invention, the present invention provides a consumer user classification method based on multi-view clustering, firstly, a plurality of consumer behavior data are obtained, and a preset similarity algorithm is used to perform similarity graph learning on the plurality of consumer behavior data to generate a plurality of initial similarity graph matrices; then, each initial similarity graph matrix is ​​separated to determine the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; a preset multi-view processing algorithm is used to output the independence criterion and value and the high-order information between views within a view according to the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix; the independence criterion and value and the consistency tensor are used to optimize each initial similarity graph matrix respectively to determine the target similarity graph matrix corresponding to each initial similarity graph matrix; according to the plurality of target similarity graph matrices , generate a fused similarity graph matrix; finally, perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results; based on the above scheme, combined with the preset multi-view processing algorithm and the preset similarity algorithm, multiple consumer behavior data are processed, and the independence criteria and values ​​and high-order information between views within a view are output. The independence criteria and values ​​and high-order information between views within a view are used to optimize each initial similarity graph matrix, determine the target similarity graph matrix corresponding to each initial similarity graph matrix, and then perform spectral clustering on the fused similarity graph matrix generated according to multiple target similarity graph matrices to generate the consumer user classification result. This process can better consider higher-order correlations between and within behaviors, thereby better grouping consumers and thereby improving the reliability of the classification results.

[0088] For better explanation, refer to Figure 3 , which shows a flow chart of a consumer user classification method based on multi-view clustering provided by the second embodiment of the present invention, including:

[0089] First, based on the input consumer behavior data, each initial similarity graph matrix is ​​learned. Then, the initial similarity graph matrix S is separated into consistent parts and inconsistent parts, and then all the consistent parts are superimposed into a three-dimensional tensor A. It can be obtained by rotating the tensor A. Then the tensor is subjected to enhanced tensor rank minimization and fed back to the initial similarity graph matrix for optimization. Finally, the consistent parts of all optimized similarity matrices (target similarity graph matrices) are fused to obtain the fused similarity graph matrix Z, and spectral clustering is performed on Z to obtain the clustering results.

[0090] See also Figure 4 , Figure 4 This is a structural block diagram of a consumer user classification system based on multi-view clustering provided in the third embodiment of the present invention.

[0091] The present invention provides a consumer user classification system based on multi-view clustering, comprising:

[0092] An acquisition module 401 is used to acquire a plurality of consumer behavior data and perform similarity graph learning on the plurality of consumer behavior data using a preset similarity algorithm to generate a plurality of initial similarity graph matrices;

[0093] A separation module 402 is used to separate each initial similarity graph matrix and determine a consistency matrix and an inconsistency matrix corresponding to each initial similarity graph matrix;

[0094] Output module 403, configured to output inconsistency independence criteria and values ​​and intra-view and inter-view high-order information based on the consistency matrix and inconsistency matrix corresponding to each initial similarity graph matrix using a preset multi-view processing algorithm;

[0095] An optimization module 404 is configured to optimize each initial similarity graph matrix using an inconsistency independence criterion and a value and a consistency tensor, and determine a target similarity graph matrix corresponding to each initial similarity graph matrix;

[0096] A generating module 405 is used to generate a fused similarity graph matrix based on multiple target similarity graph matrices;

[0097] The spectral clustering module 406 is used to perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results.

[0098] Furthermore, the preset multi-view processing algorithm includes an independence criterion and a double-enhanced tensor rank minimization algorithm; the intra-view and inter-view high-order information includes inter-view high-order information and intra-view high-order information; the output module 403 is specifically used to:

[0099] Using the independence criterion, multiple independence criterion values ​​are determined according to the inconsistency matrix corresponding to each initial similarity graph matrix;

[0100] Sum up the values ​​of each independence criterion to determine the independence criterion and value;

[0101] Stack each consistency matrix to generate a consistency stack tensor;

[0102] Rotate the consistent stacked tensor to determine the consistent rotated tensor;

[0103] A double-augmented tensor rank minimization algorithm is used to determine the intra-view high-order information corresponding to the consistent stacking tensor and the inter-view high-order information corresponding to the consistent rotation tensor based on the consistent stacking tensor and the consistent rotation tensor.

[0104] Furthermore, the generating module 405 is specifically configured to:

[0105] Separate each target similarity graph matrix and determine the target consistency matrix corresponding to each target similarity graph matrix;

[0106] Multiple target consistency matrices are fused to generate a fused similarity graph matrix.

[0107] Furthermore, the construction process of the initial similarity graph matrix is ​​as follows:

[0108] ;

[0109] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization.

[0110] Furthermore, the separation process of the initial similarity graph matrix is ​​as follows:

[0111] ;

[0112] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; is a trade-off parameter.

[0113] Furthermore, the optimization process of the initial similarity graph matrix is ​​as follows:

[0114] ;

[0115] in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; For trade-off parameters; is an adjustable parameter; is the independence criterion and value; For the independence principle; It is high-level information in the view; It is the high-level information between views; For stacking; is a rotation; A is a consistent stacking tensor; Rotate the tensor for consistency.

[0116] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] An embodiment of the present invention also provides a computer device including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the consumer user classification method based on multi-view clustering as described in any of the above embodiments.

[0118] An embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the consumer user classification method based on multi-view clustering as described in any of the above embodiments are implemented.

[0119] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the consumer user classification method based on multi-view clustering as in any of the above embodiments.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0122] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A consumer user classification method based on multi-view clustering, characterized in that: include: Acquire multiple consumer behavior data, and use a preset similarity algorithm to perform similarity graph learning on the multiple consumer behavior data to generate multiple initial similarity graph matrices; Separating each of the initial similarity graph matrices to determine a consistency matrix and an inconsistency matrix corresponding to each of the initial similarity graph matrices; Using a preset multi-view processing algorithm to output independence criteria and values ​​and intra-view and inter-view high-order information based on the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices; Optimizing each of the initial similarity graph matrices using the independence criterion and value and the consistency tensor, and determining a target similarity graph matrix corresponding to each of the initial similarity graph matrices; generating a fused similarity graph matrix according to the plurality of target similarity graph matrices; Performing spectral clustering on the fused similarity graph matrix to generate consumer user classification results; The construction process of the initial similarity graph matrix is ​​specifically as follows: ； in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; The optimization process of the initial similarity graph matrix is ​​specifically as follows: ； in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; For trade-off parameters; is an adjustable parameter; is the independence criterion and value; For the independence principle; It is high-level information in the view; It is the high-level information between views; For stacking; is a rotation; A is a consistent stacking tensor; Rotate the tensor for consistency.

2. The consumer user classification method based on multi-view clustering according to claim 1 is characterized in that: The preset multi-view processing algorithm includes an independence criterion and a double-enhanced tensor rank minimization algorithm; the intra-view and inter-view high-order information includes inter-view high-order information and intra-view high-order information; the preset multi-view processing algorithm is used to output the independence criterion and value and the intra-view and inter-view high-order information according to the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices, including: Determining a plurality of independence criterion values ​​according to the inconsistency matrix corresponding to each of the initial similarity graph matrices using an independence criterion; Summing the independence criterion values ​​to determine a sum of the independence criterion values; stacking the consistency matrices to generate a consistency stack tensor; Rotating the consistent stacked tensor to determine a consistent rotated tensor; A double-augmented tensor rank minimization algorithm is used to determine, based on the consistent stacking tensor and the consistent rotation tensor, the intra-view high-order information corresponding to the consistent stacking tensor and the inter-view high-order information corresponding to the consistent rotation tensor.

3. The consumer user classification method based on multi-view clustering according to claim 1 is characterized in that: Generating a fused similarity graph matrix according to the plurality of target similarity graph matrices includes: Separating each of the target similarity graph matrices and determining a target consistency matrix corresponding to each of the target similarity graph matrices; A plurality of target consistency matrices are fused to generate a fused similarity graph matrix.

4. The consumer user classification method based on multi-view clustering according to claim 1 is characterized in that: The separation process of the initial similarity graph matrix is ​​specifically as follows: ； in, is the vth initial similarity graph matrix; m is the total number of initial similarity graph matrices; n is the total number of samples of consumer behavior data; is the initial similarity value corresponding to the vth initial similarity graph matrix composed of the i-th consumer behavior data and the j-th consumer behavior data; is the i-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the j-th consumer behavior data corresponding to the v-th initial similarity graph matrix; is the square of the Euclidean norm; T is the transpose; For Perform regularization; is the consistency matrix corresponding to the vth initial similarity graph matrix; is the inconsistency matrix corresponding to the vth initial similarity graph matrix; is a trade-off parameter.

5. A consumer user classification system based on multi-view clustering, applied to the consumer user classification method based on multi-view clustering according to claim 1, characterized in that: include: An acquisition module is used to acquire a plurality of consumer behavior data and perform similarity graph learning on the plurality of consumer behavior data using a preset similarity algorithm to generate a plurality of initial similarity graph matrices; A separation module is used to separate the initial similarity graph matrices and determine the consistency matrix and the inconsistency matrix corresponding to each of the initial similarity graph matrices; An output module, configured to output an inconsistency independence criterion and value and intra-view and inter-view high-order information based on a consistency matrix and an inconsistency matrix corresponding to each of the initial similarity graph matrices using a preset multi-view processing algorithm; an optimization module, configured to optimize each of the initial similarity graph matrices using the inconsistency independence criterion and value and the consistency tensor, and determine a target similarity graph matrix corresponding to each of the initial similarity graph matrices; A generating module, configured to generate a fused similarity graph matrix based on the plurality of target similarity graph matrices; The spectral clustering module is used to perform spectral clustering on the fused similarity graph matrix to generate consumer user classification results.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor performs the steps of the consumer user classification method based on multi-view clustering according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the consumer user classification method based on multi-view clustering according to any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the consumer user classification method based on multi-view clustering as described in any one of claims 1-4.

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