User behavior analysis method and system for AI capability fusion platform

Abstract

The present invention discloses a user behavior analysis method and system for an AI capability fusion platform, which belongs to the field of big data analysis technology. The method identifies and collects historical data related to user behavior, and cleans and standardizes the collected data; defines target variables and features, and constructs a sample matrix; calculates the correlation between each eigenvector in the sample matrix and the target variable, as well as the behavioral features of each sample sequence, to generate a behavior sample matrix; constructs a user prediction model, deeply mines and analyzes user behavior data, and predicts user purchase intentions; and obtains user behavior data in real time to generate a product recommendation table; collects user feedback data on the product recommendation table in real time, calculates the conversion rate of the product recommendation table, and the similarity between the product recommendation table and actual purchase situations, and dynamically adjusts the user prediction model.

Description

Technical Field

[0001] The present invention relates to the field of big data analysis technology, and more specifically to a user behavior analysis method and system for an AI capability fusion platform. Background Art

[0002] Traditional user behavior analysis methods are usually based on simple statistical and classification techniques, such as frequency statistics and user classification. Although these methods can reveal some basic characteristics of user behavior to a certain extent, their limitations are becoming increasingly apparent when faced with the complexity and diversity of the big data era. Traditional statistical and classification techniques can often only capture the surface phenomena of user behavior and are difficult to deeply explore the underlying laws and patterns behind user behavior. At the same time, these methods are significantly limited in efficiency and accuracy when processing large-scale data, making it difficult to meet the real-time and precision requirements of modern enterprises for user behavior analysis. To this end, it is necessary to use more advanced and efficient big data analysis technologies to fully utilize the diversity and complexity of big data, deeply explore the potential value in user behavior data, and provide users with more accurate behavior predictions and personalized recommendations.

[0003] The existing Chinese patent with authorization announcement number CN114925273B discloses a user behavior prediction method and AI prediction analysis system based on big data analysis. First, a user behavior data set including multiple user historical operations is obtained; then the confidence of the broad user key behavior corresponding to each user historical operation is obtained, and the user historical operation distribution knowledge graph corresponding to the target user historical operation analyzed based on the candidate narrow time dimension is analyzed to perform narrow time dimension analysis, and the confidence of the broad user key behavior of the target user historical operation is used to perform confidence screening, and finally the target user historical operation after confidence screening is used to predict the user behavior of the user to be processed, and the target user historical operation for predicting user behavior is determined by combining different time dimensions.

[0004] While existing technologies effectively address the problem of unreliable prediction results in complex user behavior predictions based solely on historical user actions that are relatively recent in time, they fail to consider deeper user behavior mining, such as the impact of new trends, new products, and changes in the surrounding cultural environment on user behavior. Dynamic changes in user behavior can lead to biased prediction results, resulting in poor personalized recommendation results. Therefore, this application provides a user behavior analysis method and system for an AI capability fusion platform. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a user behavior analysis method and system for an AI capability fusion platform, build a user prediction model, conduct in-depth mining and analysis of user behavior, predict user purchase intentions, generate personalized product recommendation tables, and dynamically adjust the user prediction model based on user feedback data to ensure that the product recommendation table always keeps up with the user's latest needs and preference changes.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] User behavior analysis methods for AI capability integration platforms include:

[0008] Step S1: Identify and collect historical data related to user behavior, and clean and standardize the collected historical data;

[0009] Step S2: define the target variable and features, and construct a sample matrix; calculate the correlation between each feature vector in the sample matrix and the target variable, as well as the behavioral features of each sample sequence, to generate a behavioral sample matrix;

[0010] Step S3: Build a user prediction model, analyze user behavior data, and predict user purchase intentions; obtain user behavior data in real time and generate a product recommendation table;

[0011] Step S4: collecting user feedback data on the product recommendation form in real time, calculating the conversion rate of the product recommendation form and the similarity between the product recommendation form and actual purchase situations, and dynamically adjusting the user prediction model;

[0012] The step S2 comprises:

[0013] S2.1: Define purchase decision as the target variable, the factors affecting the target variable as features, and construct a sample matrix (Ψ,Φ); where Ψ is the feature matrix, and Ψ=(Ψ1,…,Ψ a ), a is the number of feature types, Φ is the target variable vector, Ψ a is the ath eigenvector that affects the target variable;

[0014] S2.2: Calculate the eigenvector Ψ in the characteristic matrix x The correlation between the target variable vector and the target variable vector is generated, and the correlation matrix R = (r1, r2, ..., r a ); where Ψ x is the eigenvector of the Xth feature in the feature matrix, x = 1, 2, ..., a, r a is the correlation between the xth feature and the target variable vector;

[0015] S2.3: Set the correlation threshold to rth , filter out the features related to the target variable; if |r x |≥r th , define the eigenvector Ψ x is the relevant eigenvector; if |r x | <r th , define the eigenvector Ψ x is an irrelevant eigenvector;

[0016] S2.4: Obtain relevant feature vectors and generate relevant sample matrix in, is the correlation feature matrix;

[0017] S2.5: Collect the user's behavior records at the corresponding time point of each sample sequence in the relevant sample matrix, set the time window to T, and calculate the behavior frequency and average duration of each behavior type of the user within the time window;

[0018] S2.6: Add the calculated behavior frequency and average duration as new features to the related sample matrix to generate a behavior sample matrix in, is the behavioral feature matrix.

[0019] Specifically, step S3 includes:

[0020] S3.1: Use a multi-layer perceptron (MLP) model to build a user prediction model. The model includes an input layer, a hidden layer, and an output layer.

[0021] S3.2: Initializing parameters of the user prediction model;

[0022] S3.3: The behavior sample matrix Dividing the data into a training set and a test set, and using the training set to train the user prediction model to optimize the parameters of the user prediction model;

[0023] S3.4: Validating the user prediction model using the test set;

[0024] S3.5: Obtain the user's current behavior data in real time, use the user prediction model to predict the user's behavior data, and obtain the user's predicted purchase decision;

[0025] S3.6: Obtain the products with purchase intention output by the user prediction model, sort the products in descending order based on purchase probability, and generate a product recommendation table.

[0026] Specifically, the S3.1 further includes:

[0027] S3.11: Set the number of hidden layers to L, and the first layer contains k l neurons, using an activation function to process the behavior feature matrix to generate a behavior hidden matrix;

[0028] S3.12: Process the behavior hidden matrix using a Sigmoid activation function to calculate the probability of the user's purchase decision;

[0029] S3.13: Set a binary classifier after the output layer and set the threshold to 0.5; if Y out ≥0.5, then let φ pred =1, it is predicted that the user has purchase intention; if Y out <0.5, then let φ pred =0, it is predicted that the user has no purchase intention; where Y out is the probability of user purchase decision, φ pred Predicted value for user purchase decision.

[0030] Specifically, the S3.3 also includes:

[0031] S3.31: Input the behavioral feature matrix in the training set into the user prediction model to obtain the predicted value φ of the user's purchase decision pred ;

[0032] S3.32: Define cross entropy loss as the loss function of the user prediction model, and calculate the difference between the predicted value and the actual value;

[0033] S3.33: Calculate the gradient of the loss function with respect to the user prediction model parameters using a back-propagation algorithm;

[0034] S3.34: Update the parameters of the user prediction model using stochastic gradient descent;

[0035] S3.35: Repeat S3.32 to S3.34 until the preset number of iterations is reached and the loss function value is less than the preset loss threshold, and the training ends.

[0036] Specifically, step S4 includes:

[0037] S4.1: Set the feedback interval to t. After the time period t of generating the product recommendation table, collect the user's real-time feedback data on the product recommendation table and pre-process the real-time feedback data.

[0038] S4.2: Calculate the user's conversion rate P for the product recommendation table CR , the expression is as follows:

[0039]

[0040] Where, F buy F is the actual purchase quantity after the user clicks on the recommended product. click The total number of recommended products clicked by users.

[0041] Specifically, the step S4 further includes:

[0042] S4.3: For the product recommendation table and the products actually purchased by the user, extract the key features of each product and generate the recommended product vector H = (H1, H2, ..., H m ) and the actual product vector E=(E1,E2,…,E n ); where m is the number of recommended products in the product recommendation table, H m is the product vector of the mth recommended product in the product recommendation table, n is the number of products actually purchased, E n is the product vector of the nth product among the products actually purchased;

[0043] S4.4: Calculate the similarity between each product vector in the product recommendation table and each product vector actually purchased, and generate a similarity matrix S;

[0044] S4.5: Calculate the average similarity of the similarity matrix S

[0045] S4.6: Set the user's conversion threshold to P th , the average similarity threshold is Determine whether the product recommendation table is consistent with the user's actual purchase situation; if P CR ≥P th and The product recommendation table is consistent with the user's actual purchase situation; if P CR <P th or The product recommendation table does not conform to the user's actual purchase situation, and returns to S2.1 to update the sample matrix to update the parameters in the user prediction model.

[0046] The user behavior analysis system for the AI capability integration platform includes: data collection module, feature extraction module and prediction module;

[0047] The data acquisition module is used to collect historical data related to user behavior and pre-process the data;

[0048] The feature extraction module is used to define the target variable and the features that affect the target variable, and to construct a sample matrix (Ψ, Φ); the sample matrix is processed to generate a behavior sample matrix

[0049] The prediction module is used to build a user prediction model, predict the user's purchase intention, and generate a product recommendation table; collect user feedback data on the product recommendation table in real time, analyze the feedback data, and dynamically adjust the user prediction model.

[0050] Specifically, the feature extraction module includes a similarity unit and a behavior unit;

[0051] The similarity unit is used to calculate the correlation between each feature vector in the sample matrix and the target variable, filter out features related to the target variable, and generate a correlation sample matrix;

[0052] The behavior unit is used to collect behavior records and set the time window to T, calculate the behavior frequency and average duration of each behavior type of the user within the time window, and generate a behavior sample matrix;

[0053] Wherein, the prediction module includes a recommendation unit and a feedback update unit;

[0054] The recommendation unit is used to build a user prediction model, obtain the user's current behavior data in real time, calculate the user's predicted purchase decision, and generate a product recommendation table;

[0055] The feedback update unit collects user feedback data on the product recommendation table in real time, calculates the conversion rate and average similarity, determines whether the product recommendation table is consistent with the user's actual purchase situation, updates the sample matrix, and updates the parameters in the user prediction model.

[0056] Beneficial effects of the present invention:

[0057] 1. By defining the target variable and screening features that are highly correlated with the target variable, a behavior sample matrix is constructed. The matrix only contains features that are closely related to the target variable, which more effectively captures user behavior patterns. At the same time, by removing features with low correlation with the target variable, the amount of information that the model needs to process is reduced, the redundancy of the data is reduced, and the computational complexity of the model training process is significantly reduced, thereby reducing the risk of overfitting, improving the model's prediction accuracy, and making the model more robust and reliable in practical applications.

[0058] 2. To achieve a more personalized user experience and precise marketing strategies, we acquire user behavior data in real time and build a user prediction model to conduct in-depth mining and analysis of user behavior, capturing users' potential needs and preferences in real time, thereby more accurately predicting their purchasing intentions and generating personalized product recommendation tables. Each user can receive product recommendations tailored to their personal preferences and current needs. This not only meets the unique needs of different users, but also greatly improves their shopping experience and satisfaction. At the same time, personalized recommendation strategies also improve users' purchase conversion rates, increase platform sales and user stickiness, and bring direct benefits and long-term competitive advantages to the business.

[0059] 3. Collect user feedback on product recommendation forms in real time. Directly measure the effectiveness of the model by calculating conversion rates and the similarity between product recommendations and actual purchases. Dynamically adjust the model based on feedback, optimize feature selection and weight distribution, and continuously improve the accuracy and relevance of recommendations. This ensures that product recommendations always keep up with users' latest needs and preference changes, and continuously optimizes recommendation effectiveness. This not only improves user satisfaction and platform interactivity, but also promotes a virtuous cycle between users and the platform, laying a solid foundation for the long-term development of the business. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of a user behavior analysis method for an AI capability fusion platform;

[0061] Figure 2 A feature extraction flow chart for the user behavior analysis method used in the AI capability fusion platform;

[0062] Figure 3 A personalized recommendation flowchart for user behavior analysis methods used in AI capability integration platforms;

[0063] Figure 4 Real-time update of flowcharts for user behavior analysis methods used in AI capability integration platforms;

[0064] Figure 5 This is a structural diagram of the user behavior analysis system used for the AI capability fusion platform. DETAILED DESCRIPTION

[0065] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0066] Example 1

[0067] refer to Figures 1 to 4 As shown, this embodiment introduces a user behavior analysis method for an AI capability fusion platform, including the following steps:

[0068] Step S1: Identify and collect historical data related to user behavior, including user historical operations, social media data, geographic location data, and browsing history data, such as browsing, clicking, searching, and purchasing history. These multi-dimensional data are obtained through various channels, such as log files, API interfaces, and third-party data providers, to ensure the comprehensiveness and representativeness of the data in order to more comprehensively understand user behavior patterns and preferences; clean the collected data to remove useless data, such as missing values, duplicate values, and outliers. At the same time, standardize the data, such as normalizing the data to eliminate dimensional differences between different features, and standardizing the data to improve the convergence speed and performance of the algorithm, so that the data is suitable for subsequent algorithm training and modeling, and improve the accuracy and reliability of data analysis;

[0069] Step S2: Based on the preprocessed data, define the target variable and the features that affect the target variable, and construct a sample matrix; calculate the correlation between each feature vector in the sample matrix and the target variable, screen out features that are highly correlated with the target variable, collect user behavior records, calculate the behavior features of each sample sequence, capture more user behavior patterns, and generate a behavior sample matrix;

[0070] Step S3: Based on the behavior sample matrix and target variables, a user prediction model is constructed to conduct in-depth mining and analysis of user behavior data, identify users' potential needs and preferences, and predict users' purchase intentions for different types of products or services. User behavior data is obtained in real time, and the user prediction model is used to obtain users' predicted purchase decisions and generate a product recommendation table.

[0071] Step S4: Collect user feedback data on the product recommendation form in real time, and calculate the conversion rate of the product recommendation form and the similarity between the product recommendation form and the actual purchase situation based on the feedback data, evaluate the consistency between the product recommendation form and the actual purchase situation, and dynamically adjust the user prediction model to update the user's product recommendation form to improve the platform's interactivity and user satisfaction.

[0072] Specifically, the specific steps of step S2 include:

[0073] S2.1: Based on business needs and data characteristics, define the user's purchase decision for the product as the target variable, and define 0 to represent no purchase and 1 to represent purchase. Define various factors that affect the target variable as features. These factors are other factors in the historical data besides the user's purchase decision, and construct a sample matrix Among them, the sample matrix is a data set containing various features and target variables, Ψ is the feature matrix, a is the number of feature types, Ψ a is the ath eigenvector that affects the target variable, Φ is the target variable vector, b is the number of samples, (ψ1(b),…,ψ a (b),φ(b)) is the b-th group of sample sequence in the sample matrix,ψ a (b) is the ath eigenvalue in the bth group of sample sequences, and φ(b) is the target variable value in the bth group of sample sequences;

[0074] S2.2: Using the Pearson correlation coefficient, calculate the eigenvector Ψ in the sample matrix x The correlation between the target variable vector and the target variable vector is generated, and the correlation matrix R = (r1, r2, ..., r a ), where each element in the correlation matrix represents the correlation between a feature and the target variable; the expression is as follows:

[0075]

[0076] Where, x is the eigenvector of the xth feature in the feature matrix, r x is the correlation between the xth feature and the target variable vector, x=1,2,…,a, is the eigenvector Ψ x The mean of is the mean of the target variable vector φ,

[0077] S2.3: Set the correlation threshold to r th , traverse the correlation matrix R, filter out the features that are highly correlated with the target variable, and reduce the data dimension; if |r x |≥r th , define the eigenvector Ψ x is the relevant eigenvector; if |r x | <r th , define the eigenvector Ψ x is an irrelevant eigenvector;

[0078] S2.4: Get the relevant eigenvectors in the sample matrix (Ψ, Φ) and generate the relevant sample matrix in, is the correlation feature matrix, a th is the number of feature types in the correlation feature matrix, a th ≤a, is the ath in the correlation feature matrix th Correlation feature vectors;

[0079] S2.5: Since the correlation sample matrix It is directly extracted from the historical data of user behavior, and only records the data or behavior at a single time point, which cannot directly reflect certain potential laws or patterns in the data. Perform calculations to capture more user behavior patterns, thereby enhancing the model's ability to predict user purchase decisions. Collect user behavior records at the corresponding time points for each sample sequence in the relevant sample matrix, such as behavior records, behavior types, and behavior duration at each time point. Set the time window to T, such as the past week or month. For each sample sequence, calculate the user's behavior frequency and average duration for each behavior type within the time window. The expression is as follows:

[0080]

[0081]

[0082] Where η c is the frequency of the cth behavior type in the time window, c = 1, 2, ..., M, M is the total number of behavior types, N c is the number of occurrences of the cth behavior type in the time window, D c is the average duration of the cth behavior type in the time window, d z is the duration of the zth behavior in the cth behavior type, z=1,2,…,N c ;

[0083] S2.6: Add the calculated behavior frequency and average duration as new features to the relevant sample matrix to generate the behavior sample matrix in, is the behavioral feature matrix, a c is the number of feature types in the behavior feature matrix, and a c >a th , is the ath in the behavior feature matrix c behavioral feature vectors.

[0084] Specifically, the specific steps of step S3 include:

[0085] S3.1: Use a multi-layer perceptron (MLP) model to build a user prediction model. The model consists of an input layer, a hidden layer, and an output layer. The input layer is used to input the behavior sample matrix into the user prediction model. The number of nodes in the input layer should match the number of features in the behavior feature matrix. The hidden layer is used to extract the deep-level features of user behavior. The output layer contains a neuron with a Sigmoid activation function. The output value is a probability value between 0 and 1, indicating the user's purchase probability of different types of products or services. A binary classifier is set after the output layer to convert the probability value into a purchase decision prediction of 0 or 1.

[0086] S3.2: Initialize the parameters of the user prediction model (including the weight matrix and bias matrix of each layer in the user prediction model) to small random numbers;

[0087] S3.3: Behavior Sample Matrix Divide the dataset into a training set and a test set with a ratio of α:(1-α), and use the training set to train the user prediction model and optimize the parameters of the user prediction model; in this embodiment, α=0.8;

[0088] S3.4: Use the test set to verify the user prediction model; if the verification fails, expand the training set and continue training the model; if the verification passes, the user prediction model is successfully built;

[0089] S3.5: Obtain the user's current behavior data in real time, pre-process the data, and use the user prediction model to predict the user's behavior data to obtain the user's predicted purchase decision. The predicted purchase decision includes whether the user has purchase intention or not.

[0090] S3.6: Obtain the products with purchase intention output by the user prediction model, sort the products in descending order based on purchase probability, and generate a product recommendation table.

[0091] Specifically, the specific steps of S3.1 also include:

[0092] S3.11: Set the number of hidden layers to L, and the first layer contains k l The hidden layer receives the behavior feature matrix from the input layer or the hidden matrix of the previous layer, processes the behavior feature matrix using the activation function, and generates a behavior hidden matrix. The expression is as follows:

[0093] H l =γ(W l ·H l-1 +B l )

[0094] Where l = 1, 2, ..., L, H lis the behavior hidden matrix of the lth hidden layer, W l is the weight matrix from layer l-1 to layer l, B l is the bias matrix of the lth layer, γ(·) is the ReLU function, which introduces nonlinearity and improves the expressiveness of the model. For the first hidden layer, its input is the output of the input layer, and the output is H1=f(W1·X+B1), where X is the output matrix of the input layer. For subsequent hidden layers, their input is the output of the previous layer.

[0095] S3.12: Use the Sigmoid activation function to process the behavior hidden matrix and calculate the probability of the user's purchase decision. The expression is as follows:

[0096] Y out =σ(W out ·H L +B out )

[0097] Where Y out is the probability of the user's purchase decision, σ(·) is the Sigmoid activation function, H L is the output of the last hidden layer, W out 、B out are the weight matrix and bias matrix from the last hidden layer to the output layer respectively;

[0098] S3.13: Set a binary classifier after the output layer and set the threshold to 0.5; if Y out ≥0.5, then let φ pred =1, it is predicted that the user has purchase intention; if Y out <0.5, then let φ pred =0, it is predicted that the user has no purchase intention; where, φ pred Predicted value for user purchase decision.

[0099] Specifically, the specific steps of S3.3 also include:

[0100] S3.31: Input the behavioral feature matrix in the training set into the user prediction model and calculate the predicted value of the user's purchase decision layer by layer. pred , the target variable vector in the training set is taken as the actual value φ of the user's purchase decision;

[0101] S3.32: Define cross entropy loss as the loss function of the user prediction model, and calculate the difference f between the predicted value and the actual value loss , the expression is as follows:

[0102]

[0103] In the formula, G is the number of samples in the training set, φ(g) is the g-th element in the actual value, and φ pred (g) is the g-th element in the predicted value, g = 1, 2, ..., G;

[0104] S3.33: Use the backpropagation algorithm to calculate the gradient of the loss function with respect to the user prediction model parameters. Calculate the gradient of the loss function with respect to the output layer output. Then, use the chain rule to calculate the gradient backward layer by layer. Calculate the gradient of the loss function with respect to the weight matrix and bias matrix in each layer. The expression is as follows:

[0105]

[0106] Where, is the gradient of the loss function with respect to the output layer output, is the gradient of the output layer weight matrix, σ' is the derivative of the activation function σ(·), is the gradient of the output layer bias matrix, is the gradient of the weight matrix of the lth hidden layer, γ' is the derivative of the activation function γ(·), is the gradient of the bias matrix of the lth hidden layer;

[0107] S3.34: Use stochastic gradient descent to update the parameters of the user prediction model to minimize the loss function, which is expressed as follows:

[0108]

[0109]

[0110] Where W old is the weight matrix before update, W new is the updated weight matrix, B old is the bias matrix before updating, B new is the updated bias matrix, λ is the learning rate, is the gradient of the corresponding parameter;

[0111] S3.35: Repeat S3.32 to S3.34 until the preset number of iterations is reached and the loss function value is less than the preset loss threshold, and the training ends.

[0112] Specifically, the specific steps of step S4 include:

[0113] S4.1: Set the feedback interval to t. After the product recommendation form is generated for a period of time t, collect real-time user feedback data on the product recommendation form, such as the number of clicks on the product recommendation form, browsing time, adding items to the shopping cart, purchase times, and actual purchases made by users during the period of time t when the product recommendation form was generated. This data intuitively reflects the user's satisfaction and acceptance of the product recommendation form. Preprocess the real-time feedback data, including cleaning, denoising, and formatting, to provide a high-quality data foundation for subsequent analysis.

[0114] S4.2: Calculate the user's conversion rate P for the product recommendation table CR The conversion rate is used to measure the degree to which the recommended products in the product recommendation table meet user needs. It reflects the proportion of users who actually complete purchases after clicking on the recommended products. The expression is as follows:

[0115]

[0116] Where, F buy The number of products that users actually purchased after clicking on the recommended product. click The total number of recommended products clicked by users, indicating how many products in the product recommendation table were clicked and viewed by users;

[0117] S4.3: For the product recommendation table and the products actually purchased by the user, extract the key features of each product, such as price, brand, and category, and convert the features of each product into a feature vector with the same dimension to generate the recommended product vector H = (H1, H2, ..., H m ) and the actual product vector E=(E1,E2,…,E n ); where m is the number of recommended products in the product recommendation table, n is the number of products actually purchased, and H i is the product vector of the i-th recommended product in the product recommendation table, i=1,2,…,m, E j is the product vector of the jth product among the actually purchased products, j = 1, 2, …, n, and the dimension of the product vector of each product is k;

[0118] S4.4: Calculate the product vector H in the product recommendation table i Vector E of the product actually purchased j The similarity s i,j , similarity reflects the similarity or consistency between the recommended products in the product recommendation table and the products actually purchased by the user, and generates a similarity matrix S, which is expressed as follows:

[0119]

[0120] Where, (·) Tis the transpose operation of the vector, ||·|| is the modular operation of the vector;

[0121] S4.5: Calculate the average similarity of the similarity matrix S The average similarity is used to measure the consistency between the product recommendation table and the actual purchase situation. The expression is as follows:

[0122]

[0123] S4.6: Set the user's conversion threshold to P th , the average similarity threshold is Determine whether the product recommendation table is consistent with the user's actual purchase situation; if P CR ≥P th and The product recommendation table is consistent with the user's actual purchase situation; if P CR <P th or The product recommendation table does not match the user's actual purchase situation, and returns to S2.1 to update the sample matrix to update the parameters in the user prediction model so that the model can more accurately reflect the user's preferences.

[0124] Example 2

[0125] See also Figure 5 , another embodiment provided by the present invention: a user behavior analysis system for an AI capability fusion platform, comprising: a data acquisition module, a feature extraction module, and a prediction module;

[0126] The data collection module is used to obtain historical data related to user behavior from various channels, such as log files, API interfaces, and third-party data providers, including user historical operations, social media data, geographic location data, and browsing history data; and pre-process the data and save the pre-processed data into the user behavior database;

[0127] The feature extraction module is used to define the target variable and the features that affect the target variable, and to construct a sample matrix (Ψ, Φ); process the sample matrix, filter out the features that have a significant impact on the prediction results, and calculate the behavioral features in the sample matrix to generate a behavioral sample matrix Among them, Ψ is the feature matrix, Φ is the target variable vector, is the behavioral feature matrix;

[0128] The prediction module is used to build a user prediction model, conduct in-depth mining and analysis of user behavior data, predict users' purchase intentions for different types of products or services, and generate a product recommendation form; collect user feedback data on the product recommendation form in real time, calculate the conversion rate of the product recommendation form, and the similarity between the product recommendation form and the actual purchase situation, evaluate the consistency between the product recommendation form and the actual purchase situation, and dynamically adjust the user prediction model.

[0129] Specifically, the feature extraction module includes similarity units and behavior units;

[0130] The similarity unit calculates the correlation between each eigenvector in the sample matrix and the target variable through correlation analysis methods, such as Pearson correlation coefficient, generates the correlation matrix R, and sets the correlation threshold r th , traverse the correlation matrix R, compare the elements in the correlation matrix R with the correlation threshold, screen out the features that are highly correlated with the target variable, and generate the correlation sample matrix Reduce the data dimension; among them, is the correlation feature matrix;

[0131] The behavior unit is used to collect the relevant sample matrix The behavior record of each sample sequence at the corresponding time point in the time window is set as T, and the behavior frequency η of each behavior type of the user in the time window is calculated. c and the average duration D c , add the calculated behavior frequency and average duration as new features to the relevant sample matrix to generate the behavior sample matrix Among them, η c is the behavior frequency of the cth behavior type in the time window, c=1,2,…,M, M is the total number of behavior types, D c is the average duration of the cth behavior type in the time window.

[0132] Specifically, the prediction module includes a recommendation unit and a feedback update unit;

[0133] The recommendation unit is used to build a user prediction model and transform the behavior sample matrix The data is divided into training and test sets. The training set is used to train the user prediction model and optimize the parameters of the user prediction model. The test set is used to verify the user prediction model. The user's current behavior data is obtained in real time. The user prediction model is used to predict the behavior data to obtain the user's predicted purchase decision. The products are sorted in descending order based on the purchase probability to generate a product recommendation table.

[0134] The feedback update unit collects user feedback data on the product recommendation form in real time through the set feedback interval t, and calculates the user's conversion rate P for the product recommendation form.CR The conversion rate is used to measure the degree to which the recommended products in the product recommendation table meet the user's needs, reflecting the proportion of users who actually complete purchases after clicking on the recommended products; extract the key features of each product in the product recommendation table and the user's actual purchase, and generate the recommended product vector H = (H1, H2, ..., H m ) and the actual product vector E=(E1,E2,…,E n ), calculate the average similarity between the product recommendation table and the user's actual purchase Based on the conversion rate and average similarity, we determine whether the product recommendation table matches the user's actual purchase situation and update the sample matrix to update the parameters in the user prediction model so that the model more accurately reflects user preferences. m is the number of recommended products in the product recommendation table, and n is the number of products actually purchased.

[0135] In summary, the present invention defines the target variable and the features that affect the target variable, constructs a sample matrix, calculates the similarity between the features and the target variable, screens out the features related to the target variable, calculates the behavioral features, and generates a behavioral sample matrix; by constructing a user prediction model, generating a product recommendation table, and collecting user feedback data on the product recommendation table in real time, calculating the conversion rate of the product recommendation table, and the similarity between the product recommendation table and the actual purchase situation, the user prediction model is dynamically adjusted.

[0136] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A user behavior analysis method for an AI capability fusion platform, characterized in that: include: Step S1: Identify and collect historical data related to user behavior, and clean and standardize the collected historical data; Step S2: Define target variables and features, and construct a sample matrix; Calculating the correlation between each eigenvector in the sample matrix and the target variable, as well as the behavioral characteristics of each sample sequence, to generate a behavioral sample matrix; Step S3: Build a user prediction model, analyze user behavior data, and predict user purchase intentions; And obtain user behavior data in real time to generate product recommendation tables; Step S4: collecting user feedback data on the product recommendation form in real time, calculating the conversion rate of the product recommendation form and the similarity between the product recommendation form and actual purchase situations, and dynamically adjusting the user prediction model; The step S2 comprises: S2.1: Define purchase decision as the target variable, the factors affecting the target variable as features, and construct a sample matrix (Ψ,Φ); where Ψ is the feature matrix, and Ψ=(Ψ1,…,Ψ a ), a is the number of feature types, Φ is the target variable vector, Ψ a is the ath eigenvector that affects the target variable; S2.2: Calculate the eigenvector Ψ in the characteristic matrix x The correlation between the target variable vector and the target variable vector is generated, and the correlation matrix R = (r1, r2, ..., r a ); where Ψ x is the eigenvector of the xth feature in the feature matrix, x=1,2,…,a,r a is the correlation between the xth feature and the target variable vector; S2.3: Set the correlation threshold to r th , filter out the features related to the target variable; if |r x |≥r th , define the eigenvector Ψ x is the relevant eigenvector; if |r x | <r th , define the eigenvector Ψ x is an irrelevant eigenvector; S2.4: Obtain relevant feature vectors and generate relevant sample matrix in, is the correlation feature matrix; S2.5: Collect the user's behavior records at the corresponding time point of each sample sequence in the relevant sample matrix, set the time window to T, and calculate the behavior frequency and average duration of each behavior type of the user within the time window; S2.6: Add the calculated behavior frequency and average duration as new features to the related sample matrix to generate a behavior sample matrix in, is the behavioral feature matrix.

2. The user behavior analysis method for the AI capability fusion platform according to claim 1 is characterized in that: The step S3 comprises: S3.1: Use a multi-layer perceptron model to build a user prediction model. The model includes an input layer, a hidden layer, and an output layer. S3.2: Initializing parameters of the user prediction model; S3.3: The behavior sample matrix Dividing the data into a training set and a test set, and using the training set to train the user prediction model to optimize the parameters of the user prediction model; S3.4: Validating the user prediction model using the test set; S3.5: Obtain the user's current behavior data in real time, use the user prediction model to predict the user's behavior data, and obtain the user's predicted purchase decision; S3.6: Obtain the products with purchase intention output by the user prediction model, sort the products in descending order based on purchase probability, and generate a product recommendation table.

3. The user behavior analysis method for the AI capability fusion platform according to claim 2 is characterized in that: Said S3.1 also includes: S3.11: Set the number of hidden layers to L, and the first layer contains k l neurons, using an activation function to process the behavior feature matrix to generate a behavior hidden matrix; S3.12: Process the behavior hidden matrix using a Sigmoid activation function to calculate the probability of the user's purchase decision; S3.13: Set a binary classifier after the output layer and set the threshold to 0.5; if Y out ≥0.5, then let φ pred =1, it is predicted that the user has purchase intention; if Y out <0.5, then let φ pred =0, it is predicted that the user has no purchase intention; where Y out is the probability of user purchase decision, φ pred Predicted value for user purchase decision.

4. The user behavior analysis method for the AI capability fusion platform according to claim 3 is characterized in that: Said S3.3 also includes: S3.31: Input the behavioral feature matrix in the training set into the user prediction model to obtain the predicted value φ of the user's purchase decision pred ; S3.32: Define cross entropy loss as the loss function of the user prediction model, and calculate the difference between the predicted value and the actual value; S3.33: Calculate the gradient of the loss function with respect to the user prediction model parameters using a back-propagation algorithm; S3.34: Update the parameters of the user prediction model using stochastic gradient descent; S3.35: Repeat S3.32 to S3.34 until the preset number of iterations is reached and the loss function value is less than the preset loss threshold, and the training ends.

5. The user behavior analysis method for the AI capability fusion platform according to claim 4 is characterized in that: The step S4 comprises: S4.1: Set the feedback interval to t. After the time period t of generating the product recommendation table, collect the user's real-time feedback data on the product recommendation table and pre-process the real-time feedback data. S4.2: Calculate the user's conversion rate P for the product recommendation table CR , the expression is as follows: Where, F buy F is the actual purchase quantity after the user clicks on the recommended product. click The total number of recommended products clicked by users.

6. The user behavior analysis method for the AI capability fusion platform according to claim 5 is characterized in that: The step S4 further includes: S4.3: For the product recommendation table and the products actually purchased by the user, extract the key features of each product and generate the recommended product vector H = (H1, H2, ..., H m ) and the actual product vector E=(E1,E2,…,E n ); where m is the number of recommended products in the product recommendation table, H m is the product vector of the mth recommended product in the product recommendation table, n is the number of products actually purchased, E n is the product vector of the nth product among the products actually purchased; S4.4: Calculate the similarity between each product vector in the product recommendation table and each product vector actually purchased, and generate a similarity matrix S; S4.5: Calculate the average similarity of the similarity matrix S S4.6: Set the user's conversion threshold to P th , the average similarity threshold is Determine whether the product recommendation table is consistent with the user's actual purchase situation; if P CR ≥P th and The product recommendation table is consistent with the user's actual purchase situation; if P CR <P th or The product recommendation table does not conform to the user's actual purchase situation, and returns to S2.1 to update the sample matrix to update the parameters in the user prediction model.

7. A user behavior analysis system for an AI capability fusion platform, which is used to implement the user behavior analysis method for an AI capability fusion platform according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, feature extraction module and prediction module; The data acquisition module is used to collect historical data related to user behavior and pre-process the data; The feature extraction module is used to define the target variable and the features that affect the target variable, and to construct a sample matrix (Ψ, Φ); the sample matrix is processed to generate a behavior sample matrix The prediction module is used to build a user prediction model, predict the user's purchase intention, and generate a product recommendation table; collect user feedback data on the product recommendation table in real time, analyze the feedback data, and dynamically adjust the user prediction model.

8. The user behavior analysis system for the AI capability fusion platform according to claim 7 is characterized in that: The feature extraction module includes a similarity unit and a behavior unit; The similarity unit is used to calculate the correlation between each feature vector in the sample matrix and the target variable, filter out features related to the target variable, and generate a correlation sample matrix; The behavior unit is used to collect behavior records and set the time window to T, calculate the behavior frequency and average duration of each behavior type of the user within the time window, and generate a behavior sample matrix; Wherein, the prediction module includes a recommendation unit and a feedback update unit; The recommendation unit is used to build a user prediction model, obtain the user's current behavior data in real time, calculate the user's predicted purchase decision, and generate a product recommendation table; The feedback update unit collects user feedback data on the product recommendation table in real time, calculates the conversion rate and average similarity, determines whether the product recommendation table is consistent with the user's actual purchase situation, updates the sample matrix, and updates the parameters in the user prediction model.

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