E-commerce user behavior analysis method based on data mining

Abstract

The invention discloses an e-commerce user behavior analysis method based on data mining, and the method comprises the steps: collecting user behavior data through an e-commerce platform, and carrying out the preprocessing of the data; carrying out dimension reduction processing on the preprocessed user behavior data; converting the data after dimension reduction into a symbolized sequence, and extracting a typical behavior pattern reflecting a user behavior rule; extracting the comprehensive behavior characteristics of the user by combining the conventional behavior characteristic indexes and the behavior characteristics based on the time sequence; performing clustering analysis on the typical behavior patterns of the users, and dividing the users into different behavior groups; based on a clustering analysis result, constructing a user behavior association network based on graph data; and displaying the relationship between the user characteristics in a visual form to reveal the relationship between the user behavior modes. According to the method, the data processing efficiency is improved, the dynamic change rule of the user behavior can be captured, and the real behavior mode and preference of the user can be comprehensively and accurately reflected.

Description

Technical Field

[0001] The present invention relates to the field of e-commerce, and in particular to an e-commerce user behavior analysis method based on data mining. Background Art

[0002] With the booming development of the e-commerce industry, e-commerce platforms have accumulated a massive amount of user behavior data, which covers various user behaviors on the platform, including browsing, interaction, purchase, feedback and social interaction. These data contain important information such as user behavior habits, preferences and potential needs, which are of great value for e-commerce platforms to achieve personalized recommendations, precision marketing and user experience optimization. However, existing user behavior analysis technologies face many challenges in processing these data, making it difficult to fully realize the value of the data.

[0003] At present, the user behavior analysis methods of most e-commerce platforms mainly focus on the basic information of users and simple purchasing behavior. For example, the user value is analyzed only through indicators such as the user's purchase frequency and purchase amount, ignoring the behavioral characteristics of users in browsing, interaction and socializing. This single-dimensional analysis method cannot fully reflect the user's real behavior patterns and preferences, resulting in a significant reduction in the effectiveness of personalized recommendations and precision marketing. In addition, the existing technology fails to fully explore the time series characteristics in user behavior data and cannot capture the dynamic changes in user behavior, further limiting the depth and accuracy of the analysis.

[0004] In addition to the problem of insufficient analysis dimensions, existing technologies also have the defect of lacking deep correlation analysis. Most current methods view user behaviors in isolation, fail to effectively build a correlation network between user behaviors, and fail to reveal the intrinsic relationship between user groups. For example, it is impossible to identify the similarities or differences between different user groups, making it difficult to achieve accurate user group segmentation and behavior prediction. In addition, existing technologies are often inefficient when processing large-scale data, and it is difficult to reflect changes in user behavior in real time. This poor real-time problem makes it impossible for e-commerce platforms to capture users' immediate needs and behavior changes in a timely manner, making it difficult to achieve dynamic personalized recommendations and precision marketing.

[0005] In view of the above problems, the present invention provides an e-commerce user behavior analysis method based on data mining. Summary of the invention

[0006] The purpose of the present invention is to solve the problems of insufficient analysis dimensions, lack of deep correlation analysis, low data processing efficiency and poor real-time performance in the prior art, and to propose an e-commerce user behavior analysis method based on data mining.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: a method for analyzing e-commerce user behavior based on data mining, comprising the following steps: Step S1, collecting user behavior data through the e-commerce platform and pre-processing the data; Step S2, using a segmented aggregation approximation method to perform dimensionality reduction processing on the pre-processed user behavior data; Step S3, converting the reduced-dimensional data into a symbolic sequence based on a symbolic aggregation approximation method, and extracting a typical behavior pattern reflecting the user's behavior rules; Step S4, extracting the user's comprehensive behavior characteristics by combining conventional behavior characteristic indicators and behavior characteristics based on time series; Step S5, performing cluster analysis on typical behavior patterns of users based on the k-means clustering algorithm, and dividing users into different behavior groups; Step S6, based on the cluster analysis results, construct a user behavior association network based on graph data; Step S7, using graph modeling technology to visualize the relationship between user features, revealing the relationship between user behavior patterns.

[0008] Furthermore, in step S1, the following sub-steps are also included: S1-1, user behavior data collected through the e-commerce platform, the user behavior data includes user basic information, user browsing behavior, user interaction behavior, user purchase behavior, user feedback behavior, user social behavior, user session information, user device and network information; S1-2, clean the collected user behavior data to remove duplicate data, outliers and missing values; S1-3: Standardize the data and convert it into standardized data with a mean of 0 and a standard deviation of 1. The specific formula is: ; in, is the normalized data point, is the original data point, is the mean of the data, is the standard deviation of the data.

[0009] Furthermore, in step S2, the following sub-steps are also included: S2-1, divide the preprocessed user behavior data into subsequences of equal length, the data length is n, the number of segments is w, and the length of each segment is ; S2-2, calculate the mean of each subsequence, and replace all data points of the subsequence with the mean of the subsequence. The specific formula is: ; in, is the mean value of the jth segment, is the pth normalized data point, n represents the length of the original data, w represents the number of segments, j represents the index of the subsequence, and p represents the index of the data point; S2-3, reconstruct the data sequence after dimensionality reduction to form user behavior data after dimensionality reduction. The length of the data after dimensionality reduction is m, where m=w.

[0010] Furthermore, in step S3, the following sub-steps are also included: S3-1, according to the data sequence after dimensionality reduction, select a symbolic interval based on data distribution to map the data into symbols, wherein the symbolic interval based on data distribution uses a quantile division interval of a Gaussian distribution to divide the data into There are equal probability intervals, each interval corresponds to a symbol, and the interval splitting point is determined according to the Gaussian distribution table , where o=1, 2, 3, ..., ; S3-2, convert each data point into a corresponding symbol to form a symbolized sequence. The symbolization process is as follows: ; in, is a symbolized sequence, if means if, is the data point after dimensionality reduction, Represents the number of equally probable intervals, a, b, z are symbols in the symbol set, used to represent different intervals of data points after dimensionality reduction; S3-3, count the occurrence frequencies of symbolized sequences, and extract the symbolized sequences with the highest occurrence frequencies as typical behavior patterns.

[0011] Furthermore, in step S4, the following sub-steps are also included: S4-1, determining conventional behavior characteristic indicators, wherein the conventional behavior characteristic indicators include user login frequency, average browsing time, purchase frequency, shopping cart addition frequency, favorite frequency, click-through rate, conversion rate, user retention rate, user average purchase amount, user comment frequency, and user sharing frequency; S4-2, determining behavioral features based on time series, wherein the behavioral features based on time series include binning entropy, time series complexity invariant distance, nonlinear measure of time series, mean absolute value change, autocorrelation of time series, mutual information of time series, periodicity of time series, stationarity of time series, long-term dependence of time series, and fractal dimension of time series; S4-3, normalize the conventional behavior characteristic indicators and the behavior characteristics based on time series. The specific formula is: ; Among them, f is the original eigenvalue, and are the minimum and maximum values ​​of the feature, respectively. is the normalized eigenvalue; S4-4, determine the allocation weight of each feature, multiply the normalized feature by the weight to form the user's comprehensive behavior feature vector. The specific formula is: ; ; ; in, is the normalized eigenvector, represents the normalized eigenvalue of each feature, H represents the assigned weight vector of each feature, represents the assigned weight of each feature, represents element-wise multiplication, is the user's comprehensive behavior feature vector, N represents the number of normalized feature vectors and assigned weights, and the number is the same.

[0012] Furthermore, in step S5, the following sub-steps are also included: S5-1, initialize the cluster center of the k-means clustering algorithm and randomly select k data points as the initial cluster center; S5-2, calculate the distance between each user behavior pattern and the cluster center, and assign the user to the nearest cluster center. The distance formula is: ; Among them, d() represents the distance function, which is used to calculate the Euclidean distance between two vectors, e represents the index of the user behavior pattern, and g represents the index of the cluster center. represents the behavior pattern of the e-th user, represents the g-th cluster center, R is the feature dimension, and r represents the index of the feature dimension; S5-3, update the cluster center to the mean of all user behavior patterns assigned to the cluster. The specific formula is: ; in, is the jth cluster, is the updated cluster center; S5-4, repeat steps S5-2 and S5-3 until the cluster center no longer changes or reaches a preset number of iterations, where the preset number of iterations includes a fixed number of iterations, a dynamically adjusted number of iterations, and a number of iterations based on a time limit.

[0013] Furthermore, in step S6, the following sub-steps are also included: S6-1, according to the cluster analysis results, each user is assigned to a cluster center to form different user groups; S6-2, define the similarity measure between user groups by calculating the distance between user groups. The specific formula is: ; in, express and The similarity measure of and are two user groups, similarity() represents the similarity measurement function, is the cluster center and The distance between and is the corresponding cluster center, d() represents the distance function, is the maximum possible distance to the cluster center; S6-3, based on similarity measurement, construct a graph structure of user behavior association network, where nodes represent user groups and edges represent similarities between user groups; S6-4, define the weights between nodes, the weights represent the strength of association between user groups, and the weight calculation formula is: ; in, is the weight between node E and node G; S6-5, use the Louvain algorithm to analyze the community structure in the user behavior association network and reveal the intrinsic relationships between user groups, including interaction relationships, social relationships, preference relationships, behavior pattern relationships, regional relationships, time relationships, consumption level relationships, user loyalty relationships and user activity relationships.

[0014] Furthermore, in step S7, the following sub-steps are also included: S7-1, using graph modeling tools to visualize the user behavior association network, wherein the graph modeling tools include Graphviz, Gephi, Cytoscape, D3.js and NetworkX; S7-2, generating a visualization chart based on the graph structure of the user behavior association network to display the relationship between user groups, wherein the visualization chart includes nodes, edges, and weights; S7-3, using visual elements to distinguish different user groups and relationship strengths, wherein the visual elements include color, size and shape; S7-4: Analyze the relationship between user behavior patterns through visual charts to identify user groups and behavior patterns. The relationship between user behavior patterns includes similarity relationship, correlation relationship, sequential relationship, causal relationship, community structure relationship, and frequency relationship.

[0015] The beneficial effects brought about by the technical solution provided by the present invention include at least: The present invention collects user behavior data through an e-commerce platform and pre-processes the data; uses a segmented aggregation approximation method to reduce the dimension of the pre-processed user behavior data; based on a symbolic aggregation approximation method, the reduced-dimensional data is converted into a symbolic sequence, and a typical behavior pattern reflecting the regularity of user behavior is extracted; combined with conventional behavior feature indicators and behavior features based on time series, the comprehensive behavior features of users are extracted; based on a k-means clustering algorithm, cluster analysis is performed on the typical behavior patterns of users, and users are divided into different behavior groups; based on the clustering analysis results, a user behavior association network based on graph data is constructed; and the relationship between user features is displayed in a visual form through graph modeling technology, revealing the relationship between user behavior patterns.

[0016] The present invention not only analyzes the user's basic information and purchasing behavior, but also comprehensively covers multi-dimensional data such as the user's browsing behavior, interactive behavior, and social behavior. By introducing time series features, it can capture the dynamic changes in user behavior, thereby more comprehensively and accurately reflecting the user's real behavior patterns and preferences.

[0017] The present invention performs dimensionality reduction processing on data through segmented aggregation approximation and symbolic aggregation approximation methods, which significantly improves data processing efficiency and can reflect changes in user behavior in real time, enabling e-commerce platforms to capture users' immediate needs in a timely manner and provide timely support for personalized recommendations and precision marketing.

[0018] The present invention constructs a user behavior association network, uses graph algorithms to analyze community structure, and reveals the intrinsic relationships between user groups. It can not only achieve accurate user group division, but also predict user behavior trends, providing strong support for the operational decisions of e-commerce platforms.

[0019] The present invention uses visualization technology to intuitively display the relationship between user behavior patterns, making it easier for e-commerce platform operators to quickly understand and apply analysis results, improving decision-making efficiency and enabling operators to more effectively optimize platform operation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0021] Figure 1 A flow chart of a method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of an e-commerce user behavior analysis method based on data mining proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0025] The following is a detailed description of a specific scheme of an e-commerce user behavior analysis method based on data mining provided by the present invention in conjunction with the accompanying drawings.

[0026] Example See also Figure 1 , which shows a method flow chart of an e-commerce user behavior analysis method based on data mining provided by an embodiment of the present invention, the method comprising the following steps: Step S1: Collect user behavior data through the e-commerce platform and pre-process the data; Wherein step S1 also includes the following sub-steps: S1-1, user behavior data collected through the e-commerce platform, the user behavior data includes user basic information, user browsing behavior, user interaction behavior, user purchase behavior, user feedback behavior, user social behavior, user session information, user device and network information; S1-2, clean the collected user behavior data to remove duplicate data, outliers and missing values; S1-3: Standardize the data and convert it into standardized data with a mean of 0 and a standard deviation of 1. The specific formula is: ; in, is the normalized data point, is the original data point, is the mean of the data, is the standard deviation of the data.

[0027] It should be noted that the basic user information includes user ID (unique identifier), registration time, user gender, user age, user geographic location (city, country), user occupation, and user device type (mobile phone, computer).

[0028] User browsing behavior includes the URL of the page viewed, browsing time (timestamps of entering and leaving the page), page dwell time, browsing path (the navigation path of the user on the website), browsing depth (the number of pages visited by the user), and browsing frequency (the number of times the user visits the website within a certain period of time).

[0029] User interaction behaviors include click behavior (clicking on ads, buttons, links), scrolling behavior (the depth and frequency of page scrolling), hovering behavior (the time the mouse hovers over an element), search behavior (search keywords entered by users), collection behavior (products or pages collected by users), and sharing behavior (content shared by users on social media).

[0030] User purchasing behavior includes the ID of the purchased product, purchase time, purchase amount, purchase quantity, purchase frequency (the number of times the user purchases within a certain period of time), purchase conversion rate (the ratio from browsing to purchase), and purchase path (the path the user takes from entering the website to completing the purchase).

[0031] User feedback behaviors include user evaluations (product evaluations, service evaluations), user comments (user comments on product pages or communities), user complaints (problems or dissatisfaction reported by users), and user satisfaction survey results.

[0032] User social behavior includes the user's social interaction on the platform (likes, comments, shares), other users or brands the user follows, community activities the user participates in, and the user's behavior on social networks (forwards, likes) User session information includes session ID (unique identifier), session start and end time, session duration, and sequence of actions within the session (all actions taken by the user in one session).

[0033] User device and network information includes the type of device used by the user (mobile phone, tablet, computer), the type of browser used by the user (Chrome, Firefox), the type of network connection used by the user (Wi-Fi, 4G), and the user's IP address (for geographic location analysis).

[0034] Duplicate data: completely identical or highly similar records, including completely duplicate data (records with completely identical fields) and partially duplicate data (records with identical key fields). For completely duplicate data, delete the duplicate records and keep only one. For partially duplicate data, choose to keep one record or merge the records based on business needs.

[0035] Outliers: data points that significantly deviate from the normal range, including statistical outliers (data points that exceed the mean ± 3 times the standard deviation) and business logic-based outliers (data points that do not conform to business logic). For statistical outliers, data points that exceed the normal range are eliminated by calculating the mean and standard deviation. For business logic-based outliers, illogical data points are corrected or deleted according to business rules.

[0036] Missing values: The situation where some fields in the data set have no values, including completely missing values ​​(a field has no value at all) and partially missing values ​​(some fields have values, but key fields are missing). For completely missing values, directly delete the missing records; for partially missing values, choose to fill the missing values ​​(using the mean, median, or mode) or delete the records based on business needs.

[0037] Standardization is a common data preprocessing method that converts data into standardized data with a mean of 0 and a standard deviation of 1. It can eliminate the dimensional differences between different features and make the data comparable. Different features may have different dimensions. Standardization can eliminate these dimensional differences and make all features have the same scale for easy comparison and analysis.

[0038] Step S2: using a segmented aggregation approximation method to perform dimensionality reduction processing on the pre-processed user behavior data; Wherein step S2 also includes the following sub-steps: S2-1, divide the preprocessed user behavior data into subsequences of equal length, the data length is n, the number of segments is w, and the length of each segment is ; S2-2, calculate the mean of each subsequence, and replace all data points of the subsequence with the mean of the subsequence. The specific formula is: ; in, is the mean value of the jth segment, is the pth normalized data point, n represents the length of the original data, w represents the number of segments, j represents the index of the subsequence, and p represents the index of the data point; S2-3, reconstruct the data sequence after dimensionality reduction to form user behavior data after dimensionality reduction. The length of the data after dimensionality reduction is m, where m=w.

[0039] It should be noted that the segmented aggregation approximation method is a technology used to reduce the dimensionality of time series data. The core idea is to divide the time series into multiple subsequences of equal length and use the mean of each subsequence to approximate the data of the subsequence. It can effectively reduce the dimension of the data while retaining the overall trend characteristics of the time series.

[0040] The main purpose of dimensionality reduction is to reduce the dimension of the data while retaining the key features of the data. It can reduce computational complexity: the length of the data sequence after dimensionality reduction is significantly reduced, thereby reducing the computational complexity of subsequent analysis; it can eliminate noise: by aggregating the data points of the subsequences, local fluctuations can be smoothed out and the impact of noise can be reduced; it can retain trend characteristics: replacing the original data points with the mean of the subsequences can retain the overall trend of the time series, facilitating subsequent analysis.

[0041] Data length n: indicates the total length of the preprocessed user behavior data; segment number w: indicates how many equal-length subsequences the data is divided into; the length of each segment Represents the length of each subsequence, ensuring that each subsequence contains the same number of data points.

[0042] represents the mean of the jth subsequence, which is used to replace all data points of the subsequence. It represents the pth standardized data point. By calculating the mean of all data points in each subsequence, multiple data points in each subsequence are simplified to one mean, thereby achieving data dimensionality reduction.

[0043] Reconstruction process: The mean of each subsequence As the data points after dimensionality reduction, a new data sequence is formed, and the mean of each subsequence represents the overall trend of the subsequence, thereby reducing the dimension of the data.

[0044] In the segmented aggregation approximation method, the length of the data sequence after dimensionality reduction is m, where m=w, because each subsequence is replaced by a mean, and the number of subsequences is equal to the number of segments w. Therefore, the length of the data sequence after dimensionality reduction is equal to the number of segments w.

[0045] Step S3: convert the reduced-dimensional data into a symbolic sequence based on a symbolic aggregation approximation method, and extract typical behavior patterns that reflect the user's behavior patterns; Wherein step S3 also includes the following sub-steps: S3-1, according to the data sequence after dimensionality reduction, select a symbolic interval based on data distribution to map the data into symbols, wherein the symbolic interval based on data distribution uses a quantile division interval of a Gaussian distribution to divide the data into There are equal probability intervals, each interval corresponds to a symbol, and the interval splitting point is determined according to the Gaussian distribution table , where o=1, 2, 3, ..., ; S3-2, convert each data point into a corresponding symbol to form a symbolized sequence. The symbolization process is as follows: ; in, is a symbolized sequence, if means if, is the data point after dimensionality reduction, Represents the number of equally probable intervals, a, b, z are symbols in the symbol set, used to represent different intervals of data points after dimensionality reduction; S3-3, count the occurrence frequencies of symbolized sequences, and extract the symbolized sequences with the highest occurrence frequencies as typical behavior patterns.

[0046] It should be noted that the symbolization interval is the interval division of mapping the reduced-dimensional data to symbols. These intervals are usually determined based on the distribution characteristics of the data (Gaussian distribution).

[0047] Quantile of Gaussian distribution: Use the quantile of Gaussian distribution to divide the interval, ensuring that each interval contains data points with equal probability; if the symbol set size is , then determine according to the Gaussian distribution table Interval Splitting Point , where o=1, 2, 3, ..., .

[0048] The Gaussian distribution is as follows:

[0049] when =3: =0.43, =0.43, No value, No value, No numerical value; when =4: =-0.67, =0, =0.67, No value, No numerical value; when =5: =-0.84, =-0.25, =0.25, =0.84, No numerical value; when =6: =-0.97, =-0.43, =0, =0.43, =0.97.

[0050] Symbolization process: Each data point after dimensionality reduction Maps to symbols in a symbol set , the specific mapping rules are based on the interval splitting point Sure.

[0051] Symbol set: It is a set of symbols used to represent the interval to which a data point belongs, such as {a,b,…,z}.

[0052] Symbols in the symbol set: a, b, …, z are symbols in the symbol set, which are used to represent different intervals of data points after dimensionality reduction, and each symbol corresponds to an interval.

[0053] In the symbolic aggregation approximation method, the Gaussian distribution table is used to determine the splitting points of the symbolized intervals. The splitting points divide the data into intervals of equal probability, each interval corresponds to a symbol. The Gaussian distribution table provides the cumulative distribution function (CDF) values ​​of the standard normal distribution (mean 0, standard deviation 1), which are used to determine the quantiles of the data.

[0054] Statistical frequency: Count the frequency of occurrence of each symbolized sequence in the data.

[0055] Typical behavior pattern: Select the symbolic sequence with the highest frequency as the typical behavior pattern. The typical behavior pattern reflects the law of user behavior.

[0056] Step S4: extracting comprehensive behavior characteristics of users by combining conventional behavior characteristic indicators and behavior characteristics based on time series; Wherein step S4 also includes the following sub-steps: S4-1, determining conventional behavior characteristic indicators, wherein the conventional behavior characteristic indicators include user login frequency, average browsing time, purchase frequency, shopping cart addition frequency, favorite frequency, click-through rate, conversion rate, user retention rate, user average purchase amount, user comment frequency, and user sharing frequency; S4-2, determining behavioral features based on time series, wherein the behavioral features based on time series include binning entropy, time series complexity invariant distance, nonlinear measure of time series, mean absolute value change, autocorrelation of time series, mutual information of time series, periodicity of time series, stationarity of time series, long-term dependence of time series, and fractal dimension of time series; S4-3, normalize the conventional behavior characteristic indicators and the behavior characteristics based on time series. The specific formula is: ; Among them, f is the original eigenvalue, and are the minimum and maximum values ​​of the feature, respectively. is the normalized eigenvalue; S4-4, determine the allocation weight of each feature, multiply the normalized feature by the weight to form the user's comprehensive behavior feature vector. The specific formula is: ; ; ; in, is the normalized eigenvector, represents the normalized eigenvalue of each feature, H represents the assigned weight vector of each feature, represents the assigned weight of each feature, represents element-wise multiplication, is the user's comprehensive behavior feature vector, N represents the number of normalized feature vectors and assigned weights, and the number is the same.

[0057] It should be noted that conventional behavioral characteristic indicators refer to quantitative indicators that can reflect user behavior patterns and characteristics. They are calculated based on user behavior data and play an important role in user behavior analysis, user profiling, personalized recommendations and precision marketing.

[0058] User login frequency: the number of times a user logs into the system within a certain period of time; average browsing time: the average duration of time a user stays on a page each time they browse the page; purchase frequency: the number of times a user completes a purchase within a certain period of time; shopping cart addition frequency: the number of times a user adds an item to the shopping cart; favorite frequency: the number of times a user adds an item to their favorites; click-through rate: the ratio of the number of clicks a user makes on a page element (such as an advertisement, a product link) to the number of page displays; conversion rate: the ratio of the number of times a user completes a purchase to the number of times a page is visited; user retention rate: the proportion of users who continue to use the platform within a certain period of time; user activity: the frequency of active user behaviors (browsing, clicking, commenting) on ​​the platform; average user purchase amount: the average amount of each purchase made by a user; user purchase category diversity: the number of categories of products purchased by users; user comment frequency: the number of times a user comments on the platform; user sharing frequency: the number of times a user shares a product or page to social media.

[0059] Time series-based behavioral features refer to the extraction of features that can reflect user behavior patterns and regularities by analyzing the time series characteristics of user behavior data. They take into account changes and trends in the time dimension and can reveal the dynamics and periodicity of user behavior.

[0060] Binning entropy: measures the entropy of the probability distribution of time series data after binning, reflecting the complexity and randomness of the data; time series complexity invariant distance: measures the complexity of time series data, reflecting the volatility and peaks and valleys of the data; nonlinear measurement of time series: measures the nonlinear characteristics of time series data, including autocorrelation and mutual information; mean absolute value change: measures the average change amplitude of time series data, reflecting the volatility of data; autocorrelation of time series: measures the correlation between time series data at different time points; mutual information of time series: measures the degree of information sharing between two time series; periodicity of time series: measures the periodic characteristics of time series data, including period length and period strength; stationarity of time series: measures the stationarity of time series data, including whether the mean and variance change over time; long-term dependence of time series: measures the long-term dependence of time series data; fractal dimension of time series: measures the fractal characteristics of time series data, including box dimension and Kolmogorov dimension.

[0061] Normalization: Convert the eigenvalue f to a normalized value , so that its range is between [0,1]. Normalization can eliminate the dimensional differences between different features, make the features comparable, and improve the accuracy of subsequent analysis.

[0062] Assign weights: Assign a weight to each feature based on its importance. The weight vector H represents the weight of each feature.

[0063] Comprehensive behavioral feature vector: By normalizing the feature vector Multiply element by element with the weight vector H to form the user's comprehensive behavior feature vector ,The comprehensive behavioral feature vector can reflect the user’s behavioral characteristics more comprehensively.

[0064] Step S5: performing cluster analysis on typical behavior patterns of users based on the k-means clustering algorithm to divide users into different behavior groups; Wherein, in step S5, the following sub-steps are also included: S5-1, initialize the cluster center of the k-means clustering algorithm and randomly select k data points as the initial cluster center; S5-2, calculate the distance between each user behavior pattern and the cluster center, and assign the user to the nearest cluster center. The distance formula is: ; Among them, d() represents the distance function, which is used to calculate the Euclidean distance between two vectors, e represents the index of the user behavior pattern, and g represents the index of the cluster center. represents the behavior pattern of the e-th user, represents the g-th cluster center, R is the feature dimension, and r represents the index of the feature dimension; S5-3, update the cluster center to the mean of all user behavior patterns assigned to the cluster. The specific formula is: ; in, is the jth cluster, is the updated cluster center; S5-4, repeat steps S5-2 and S5-3 until the cluster center no longer changes or reaches a preset number of iterations, where the preset number of iterations includes a fixed number of iterations, a dynamically adjusted number of iterations, and a number of iterations based on a time limit.

[0065] It should be noted that the k-means clustering algorithm is a classic unsupervised learning algorithm that is used to divide a data set into k different clusters. Through iterative optimization, data points are assigned to the nearest cluster center and the cluster center is continuously updated until the convergence condition or the preset number of iterations is reached.

[0066] The algorithm steps of the k-means clustering algorithm include: 1. Initialize cluster centers: Randomly select k data points as initial cluster centers.

[0067] 2. Assign data points to the nearest cluster center: For each data point, calculate the distance (Euclidean distance) between it and each cluster center, and assign the data point to the nearest cluster center; this step forms the initial clustering.

[0068] 3. Update the cluster center: For each cluster, calculate the mean of all data points assigned to the cluster and use the mean as the new cluster center; this step ensures that the cluster center can better represent the data points in the cluster.

[0069] 4. Repeat allocation and update: Repeat steps 2 and 3 until the cluster center no longer changes or the preset number of iterations is reached. The algorithm stops when the change in the cluster center is less than a certain threshold (0.01) or when a fixed number of iterations is reached.

[0070] 5. Output clustering results: Finally, output the cluster to which each data point belongs and the center of each cluster.

[0071] Fixed number of iterations: A fixed positive integer is preset as the number of iterations, such as 10, 20, or 50. The advantage is that it is simple and easy to implement, and is suitable for scenarios with limited computing resources or high real-time requirements. The disadvantage is that it may not guarantee complete convergence of the algorithm, especially when the data complexity is high.

[0072] Dynamically adjust the number of iterations: Dynamically adjust the number of iterations according to the rate of change of the cluster center or the convergence speed. When the change of the cluster center is less than a certain threshold (0.01), the algorithm is considered to have converged and the iteration is stopped. The advantage is that it can adapt to the complexity of different data sets more flexibly and ensure that the algorithm achieves a good convergence effect within a reasonable time. The disadvantage is that additional computing resources are required to monitor the rate of change of the cluster center, which is relatively complex to implement.

[0073] Iterations based on time limit: complete as many iterations as possible within a certain period of time (1 hour or 24 hours), and stop iterating when the time is up. The advantage is that it is suitable for scenarios with high real-time requirements, ensuring that the algorithm completes the calculation within a limited time. The disadvantage is that it may not be possible to guarantee complete convergence of the algorithm, especially when the data volume is large or the data complexity is high.

[0074] Step S6: Based on the cluster analysis results, construct a user behavior association network based on graph data; Wherein, in step S6, the following sub-steps are also included: S6-1, according to the cluster analysis results, each user is assigned to a cluster center to form different user groups; S6-2, define the similarity measure between user groups by calculating the distance between user groups. The specific formula is: ; in, express and The similarity measure of and are two user groups, similarity() represents the similarity measurement function, is the cluster center and The distance between and is the corresponding cluster center, d() represents the distance function, is the maximum possible distance to the cluster center; S6-3, based on similarity measurement, construct a graph structure of user behavior association network, where nodes represent user groups and edges represent similarities between user groups; S6-4, define the weights between nodes, the weights represent the strength of association between user groups, and the weight calculation formula is: ; in, is the weight between node E and node G; S6-5, use the Louvain algorithm to analyze the community structure in the user behavior association network and reveal the intrinsic relationships between user groups, including interaction relationships, social relationships, preference relationships, behavior pattern relationships, regional relationships, time relationships, consumption level relationships, user loyalty relationships and user activity relationships.

[0075] It should be noted that the cluster analysis results: through the k-means clustering algorithm, each user is assigned to a cluster center to form different user groups.

[0076] User groups: Each cluster center represents a user group that has similar behavior patterns.

[0077] Similarity metric: The similarity metric is defined by calculating the distance between the cluster centers of two user groups. The smaller the distance, the higher the similarity.

[0078] Distance function: Use Euclidean distance to calculate the distance between cluster centers.

[0079] Normalization: By dividing the distance by the maximum possible distance, the similarity measure is normalized to between [0,1] for ease of subsequent analysis.

[0080] Graph structure: The user behavior association network is a graph in which nodes represent user groups and edges represent similarities between user groups.

[0081] Edge weight: The edge weight represents the similarity measure between user groups. The higher the weight, the more similar the two user groups are. The weight directly uses the value of the similarity measure function to ensure that the weight can reflect the similarity between user groups.

[0082] The Louvain algorithm is an efficient algorithm for community detection. It identifies the community structure in the network by optimizing the modularity of the network and can quickly discover dense subgraphs (i.e., communities) in the graph.

[0083] The core of the Louvain algorithm is to detect community structure by optimizing modularity. Modularity is an indicator to measure the quality of community structure in a network, reflecting whether the connections between nodes in the network tend to cluster together more than random connections. The higher the modularity value, the more obvious the community structure.

[0084] The specific steps of Louvain algorithm are: 1. Initialization: Initialize each node as an independent community.

[0085] 2. Local optimization: For each node, calculate the change in modularity after moving it to an adjacent community; if the modularity increases after the move, move the node to an adjacent community; repeat the above process until the modularity cannot be further increased by moving the node.

[0086] 3. Construct a hierarchical graph: Treat each community as a node and construct a new hierarchical graph. In the new graph, the edge weights between nodes represent the connection strength between communities in the original graph. 4. Repeat optimization: Repeat the local optimization steps on the new hierarchical graph to further optimize the modularity; repeat the process of building the hierarchical graph and optimizing the modularity until the modularity no longer increases.

[0087] 5. Output community structure: The final output community structure is obtained through gradual optimization of the above steps and can reflect the community distribution in the network.

[0088] Interaction relationship: By analyzing the direct or indirect interactions between users, the social connections or behavioral associations between users can be revealed.

[0089] Social relations: By analyzing the social connections between users, the social network structure between users can be revealed.

[0090] Preference relationship: By analyzing the similarity of users’ preferences for goods or services, user groups with similar preferences can be identified.

[0091] Behavioral pattern relationship: By analyzing the similarity of users’ behavioral patterns on the platform, we can reveal users’ behavioral habits and preferences.

[0092] Geographical relationships: By analyzing the geographic location similarities between users, user groups with geographical connections can be identified.

[0093] Temporal relationship: By analyzing the temporal similarity of user behaviors, we can identify groups of users who are active in the same time period.

[0094] Consumption level relationship: By analyzing the similarities in user consumption amounts or consumption frequencies, user groups at different consumption levels can be identified.

[0095] User loyalty relationships: By analyzing the similarities in users’ loyalty to a platform or brand, high-loyalty and low-loyalty user groups can be identified.

[0096] User activity relationship: By analyzing the similarity of users’ activity on the platform, we can identify high-activity and low-activity user groups.

[0097] Step S7: Visualize the relationship between user features by using graph modeling technology to reveal the relationship between user behavior patterns; Wherein, in step S7, the following sub-steps are also included: S7-1, using graph modeling tools to visualize the user behavior association network, wherein the graph modeling tools include Graphviz, Gephi, Cytoscape, D3.js and NetworkX; S7-2, generating a visualization chart based on the graph structure of the user behavior association network to display the relationship between user groups, wherein the visualization chart includes nodes, edges, and weights; S7-3, using visual elements to distinguish different user groups and relationship strengths, wherein the visual elements include color, size and shape; S7-4: Analyze the relationship between user behavior patterns through visual charts to identify user groups and behavior patterns. The relationship between user behavior patterns includes similarity relationship, correlation relationship, sequential relationship, causal relationship, community structure relationship, and frequency relationship.

[0098] It should be noted that graph modeling tools include Cytoscape: suitable for the analysis of biological networks and social networks, supporting multiple data formats and plug-ins; Gephi: suitable for the visualization and analysis of large-scale network data, providing rich layout and analysis functions; Graphviz: suitable for generating high-quality graph output, supporting multiple layout algorithms; D3.js: suitable for creating interactive and dynamic visualization Web applications, supporting multiple graph types; NetworkX: suitable for graph analysis and visualization in Python environment, supporting rich graph algorithms.

[0099] Graph structure: The graph structure of the user behavior association network consists of nodes (user groups) and edges (similarities between user groups).

[0100] Visualization chart: The generated visualization chart includes nodes, edges and weights, where nodes represent user groups; edges represent the similarity or association strength between user groups; weights represent the strength of edges, which are represented by the thickness or color depth of the edges.

[0101] Visual elements include color: different colors represent different user groups or relationship strengths; size: the size of the node or edge represents the size of the user group or the strength of the relationship; shape: different shapes represent different user groups or behavior patterns.

[0102] The relationships between user behavior patterns include similarity relationships: measured by calculating the distance or similarity coefficient between behavior patterns; correlation relationships: indicating that one behavior pattern may affect or cause another behavior pattern; sequential relationships: indicating the temporal sequence of user behaviors; causal relationships: indicating that one behavior pattern may be caused by another behavior pattern; community structure relationships: the intrinsic connections and clustering between user groups identified by community detection algorithms; and frequency relationships: certain behavior patterns appear more frequently, while other behavior patterns appear less frequently.

[0103] In this way, an e-commerce user behavior analysis method based on data mining can accurately identify the behavioral characteristics of user groups, provide strong support for personalized recommendations and precision marketing of e-commerce platforms, and has significant economic and social benefits.

[0104] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 application, and should all be included in the protection scope of the present application.

Claims

1. A method for analyzing e-commerce user behavior based on data mining, characterized in that: The method includes: Step S1, collecting user behavior data through the e-commerce platform and pre-processing the data; Step S2, using a segmented aggregation approximation method to perform dimensionality reduction processing on the pre-processed user behavior data; Step S3, converting the reduced-dimensional data into a symbolic sequence based on a symbolic aggregation approximation method, and extracting a typical behavior pattern reflecting the user's behavior rules; Step S4, extracting the user's comprehensive behavior characteristics by combining conventional behavior characteristic indicators and behavior characteristics based on time series; Step S5, performing cluster analysis on typical behavior patterns of users based on the k-means clustering algorithm, and dividing users into different behavior groups; Step S6, based on the cluster analysis results, construct a user behavior association network based on graph data; Step S7, using graph modeling technology to visualize the relationship between user features, revealing the relationship between user behavior patterns.

2. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein step S1 also includes the following sub-steps: S1-1, user behavior data collected through the e-commerce platform, the user behavior data includes user basic information, user browsing behavior, user interaction behavior, user purchase behavior, user feedback behavior, user social behavior, user session information, user device and network information; S1-2, clean the collected user behavior data to remove duplicate data, outliers and missing values; S1-3: Standardize the data and convert it into standardized data with a mean of 0 and a standard deviation of 1. The specific formula is: ； in, is the normalized data point, is the original data point, is the mean of the data, is the standard deviation of the data.

3. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein step S2 also includes the following sub-steps: S2-1, divide the preprocessed user behavior data into subsequences of equal length, the data length is n, the number of segments is w, and the length of each segment is ; S2-2, calculate the mean of each subsequence, and replace all data points of the subsequence with the mean of the subsequence. The specific formula is: ； in, is the mean value of the jth segment, is the pth normalized data point, n represents the length of the original data, w represents the number of segments, j represents the index of the subsequence, and p represents the index of the data point; S2-3, reconstruct the data sequence after dimensionality reduction to form user behavior data after dimensionality reduction. The length of the data after dimensionality reduction is m, where m=w.

4. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein step S3 also includes the following sub-steps: S3-1, according to the data sequence after dimensionality reduction, select a symbolic interval based on data distribution to map the data into symbols, wherein the symbolic interval based on data distribution uses a quantile division interval of a Gaussian distribution to divide the data into There are equal probability intervals, each interval corresponds to a symbol, and the interval splitting point is determined according to the Gaussian distribution table , where o=1, 2, 3, ..., ; S3-2, convert each data point into a corresponding symbol to form a symbolized sequence. The symbolization process is as follows: ； in, is a symbolized sequence, if means if, is the data point after dimensionality reduction, Represents the number of equally probable intervals, a, b, z are symbols in the symbol set, used to represent different intervals of data points after dimensionality reduction; S3-3, count the occurrence frequencies of symbolized sequences, and extract the symbolized sequences with the highest occurrence frequencies as typical behavior patterns.

5. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein step S4 also includes the following sub-steps: S4-1, determining conventional behavior characteristic indicators, wherein the conventional behavior characteristic indicators include user login frequency, average browsing time, purchase frequency, shopping cart addition frequency, favorite frequency, click-through rate, conversion rate, user retention rate, user average purchase amount, user comment frequency, and user sharing frequency; S4-2, determining behavioral features based on time series, wherein the behavioral features based on time series include binning entropy, time series complexity invariant distance, nonlinear measure of time series, mean absolute value change, autocorrelation of time series, mutual information of time series, periodicity of time series, stationarity of time series, long-term dependence of time series, and fractal dimension of time series; S4-3, normalize the conventional behavior characteristic indicators and the behavior characteristics based on time series. The specific formula is: ； Among them, f is the original eigenvalue, and are the minimum and maximum values ​​of the feature, respectively. is the normalized eigenvalue; S4-4, determine the allocation weight of each feature, multiply the normalized feature by the weight to form the user's comprehensive behavior feature vector. The specific formula is: ； ； ； in, is the normalized eigenvector, represents the normalized eigenvalue of each feature, H represents the assigned weight vector of each feature, represents the assigned weight of each feature, represents element-wise multiplication, is the user's comprehensive behavioral feature vector, N represents the number of normalized feature vectors and assigned weights, and the number is the same.

6. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein, in step S5, the following sub-steps are also included: S5-1, initialize the cluster center of the k-means clustering algorithm and randomly select k data points as the initial cluster center; S5-2, calculate the distance between each user behavior pattern and the cluster center, and assign the user to the nearest cluster center. The distance formula is: ； Among them, d() represents the distance function, which is used to calculate the Euclidean distance between two vectors, e represents the index of the user behavior pattern, and g represents the index of the cluster center. represents the behavior pattern of the e-th user, represents the g-th cluster center, R is the feature dimension, and r represents the index of the feature dimension; S5-3, update the cluster center to the mean of all user behavior patterns assigned to the cluster. The specific formula is: ； in, is the jth cluster, is the updated cluster center; S5-4, repeat steps S5-2 and S5-3 until the cluster center no longer changes or reaches a preset number of iterations, where the preset number of iterations includes a fixed number of iterations, a dynamically adjusted number of iterations, and a number of iterations based on a time limit.

7. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein, in step S6, the following sub-steps are also included: S6-1, according to the cluster analysis results, each user is assigned to a cluster center to form different user groups; S6-2, define the similarity measure between user groups by calculating the distance between user groups. The specific formula is: ； in, express and The similarity measure of and are two user groups, similarity() represents the similarity measurement function, is the cluster center and The distance between and is the corresponding cluster center, d() represents the distance function, is the maximum possible distance to the cluster center; S6-3, based on similarity measurement, construct a graph structure of user behavior association network, where nodes represent user groups and edges represent similarities between user groups; S6-4, define the weights between nodes, the weights represent the strength of association between user groups, and the weight calculation formula is: ； in, is the weight between node E and node G; S6-5, use the Louvain algorithm to analyze the community structure in the user behavior association network and reveal the intrinsic relationships between user groups, including interaction relationships, social relationships, preference relationships, behavior pattern relationships, regional relationships, time relationships, consumption level relationships, user loyalty relationships and user activity relationships.

8. The method for analyzing e-commerce user behavior based on data mining according to claim 1, characterized in that: Wherein, in step S7, the following sub-steps are also included: S7-1, using graph modeling tools to visualize the user behavior association network, wherein the graph modeling tools include Graphviz, Gephi, Cytoscape, D3.js and NetworkX; S7-2, generating a visualization chart based on the graph structure of the user behavior association network to display the relationship between user groups, wherein the visualization chart includes nodes, edges, and weights; S7-3, using visual elements to distinguish different user groups and relationship strengths, wherein the visual elements include color, size and shape; S7-4: Analyze the relationship between user behavior patterns through visual charts to identify user groups and behavior patterns. The relationship between user behavior patterns includes similarity relationship, correlation relationship, sequential relationship, causal relationship, community structure relationship, and frequency relationship.

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