A method for dynamically generating a mobile banking activity collection page

Abstract

The application discloses a method for dynamically generating a mobile phone bank activity collection page, constructs a tree structure, wherein a root node represents an activity collection page, a first-level node represents a business category, and a sub-node is dynamically generated according to marketing activity data, a DIEN model including an embedding layer, an interest extraction layer, an interest evolution layer and a prediction layer is used to predict the interest degree of a user to different marketing activities, and user interest prediction is realized; the user geographic position, preference setting and interest prediction value are comprehensively used to calculate the weight of the sub-node, a descending order sorting algorithm is used to sort the weight of the sub-node, the activity collection page is dynamically laid out according to the sorting result, and personalized recommendation and efficient marketing are realized. The application improves system efficiency and user experience through modular design and real-time dynamic updating.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and mainly to a method for dynamically generating a mobile banking activity collection page. Background Art

[0002] With the rapid development of the mobile internet, mobile banking has become a vital channel for banking services. To attract customers and enhance user experience, banks need to frequently launch various marketing campaigns and quickly adjust their content based on market changes and customer needs. However, most mobile banking campaign pages currently require a complex development, testing, and launch process, requiring a long timeframe of a month or even longer.

[0003] Existing mobile banking page layouts mostly rely on static configurations, meaning each time the page layout or content is adjusted, they need to be redeveloped and released. This approach is not only time-consuming but also significantly limits business flexibility and responsiveness. For example, Chinese Patent Publication No. CN116501434A discloses a decision-tree-based method and system for automatic mobile banking theme switching, which belongs to the field of financial data processing technology. The method includes: managing control parameters required for automatic mobile banking theme switching via a server; the control parameters include resident region information, interface version, maintenance organization, parameter validity period, and parameter maintainer; parameter management includes parameter query, parameter modification, parameter maintenance, parameter approval, and historical version backup management of control parameters; a server-side topic modeling system establishes a mobile banking theme automatic switching model based on sample data; the model is sent to the client for mobile banking theme determination, followed by automatic mobile banking theme switching. The present invention can automatically select an appropriate version based on regional information, device information, customer age, and other information, controlling and implementing automatic switching of personalized interfaces and version functions within the mobile banking client. However, the above patent mainly relies on server-side parameter management and a theme switching model established on the server side. The client passively receives the model to switch themes, which cannot ensure that the displayed page always meets the user's current needs. In addition, the system design and processing methods are relatively traditional, and the system efficiency improvement is limited. Therefore, there is an urgent need for a method for dynamically generating mobile banking activity collection pages that effectively improves user participation and system efficiency. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present application provides a method for dynamically generating a mobile banking activity collection page.

[0005] The technical solution of this application is as follows:

[0006] A method for dynamically generating a mobile banking activity collection page, the method comprising:

[0007] Constructing a tree structure corresponding to the mobile banking activity collection page, the tree structure including a root node and multiple first-level nodes, wherein the root node represents the page corresponding to the mobile banking activity collection page, and the first-level nodes represent business categories;

[0008] Obtain the current marketing activity data of mobile banking and dynamically generate sub-nodes corresponding to different marketing activities;

[0009] Collecting user behavior data, geographic location, and preference settings in mobile banking, building a user interest prediction model, using the behavior data to train the user interest prediction model to obtain a trained user interest prediction model, and using the trained user interest prediction model to predict the user's interest in different marketing activities;

[0010] The weight of each sub-node is calculated based on the user's geographic location, preference settings, and interest in different marketing activities. The weights of the sub-nodes are sorted using a weight sorting algorithm to obtain the sorting results. The mobile banking activity collection page is dynamically laid out based on the sorting results.

[0011] As a preferred embodiment of the present invention, obtaining the current marketing activity data of mobile banking and dynamically generating sub-nodes corresponding to different marketing activities are specifically as follows:

[0012] According to the marketing activity data, the marketing activity number, marketing activity name, marketing activity icon, marketing activity link and marketing activity sequence are obtained. The marketing activity number is a unique identifier. According to the business category to which the marketing activity type belongs, a new child node is generated under the corresponding first-level node. The child node includes a node number, a node name, a leaf node flag, a parent node number, a node type, a picture name, a jump link and a serial number, wherein: the node number corresponds to the marketing activity number; the node name corresponds to the marketing activity name; the leaf node flag is specifically used to determine whether the current node is a child node. If the current node is a child node, the leaf node flag is displayed as "yes", and if the current node is not a child node, the leaf node flag is displayed as "no"; the parent node number is the corresponding first-level node number; the node type corresponds to the marketing activity type; the picture name corresponds to the marketing activity icon; the jump link corresponds to the marketing activity link; and the serial number corresponds to the marketing activity sequence.

[0013] As a preferred embodiment of the present invention, the user interest prediction model is constructed based on the DIEN model, including an embedding layer, an interest extraction layer, an interest evolution layer and a prediction layer, wherein the behavior data is input into the embedding layer as input data, and the behavior data includes user ID, product ID and behavior type, and the embedding layer outputs the user embedding vector , product embedding vector and behavior type embedding vector The product embedding vector and the behavior type embedding vector are concatenated to form the behavior sequence embedding vector , expressed as:

[0014] ;

[0015] Embedding behavior sequences into vectors As the input data of the interest extraction layer, it outputs a hidden state sequence;

[0016] The user embedding vector output by the embedding layer and the hidden state sequence output by the interest extraction layer are used as the input data of the interest evolution layer, and the interest vector is output. ;

[0017] The interest vector As the input data of the prediction layer, it outputs the predicted value of the user's interest in different marketing activities.

[0018] As a preferred embodiment of the present invention, the interest extraction layer is a GRU structure, including an update gate and a reset gate, wherein:

[0019] The reset gate is used to control the impact of historical information on the current time step, which is expressed as:

[0020] ;

[0021] Where, is the output of the reset gate, is the sigmoid activation function, To influence the weight matrix of historical information in the reset gate; is the hidden state of the previous time step, Reset the weight matrix of the current input information in the gate to influence it; is the bias vector for resetting the gate;

[0022] The update gate is used to control the contribution of the candidate hidden state of the current time step to the final hidden state, which is expressed as:

[0023] ;

[0024] Where, is the output of the update gate, is the weight matrix that affects the historical information in the update gate; is the weight matrix that affects the current input information in the update gate; is the bias vector of the update gate;

[0025] Among them, the hidden state is expressed as:

[0026]

[0027]

[0028] Where, is the hidden state of the current time step, ⊙ is the XOR operation, is the candidate hidden state of the current time step; is the hyperbolic tangent activation function; is the weight matrix that affects the historical information in the candidate hidden state; is the weight matrix that affects the current input information in the candidate hidden state.

[0029] As a preferred embodiment of the present invention, the specific steps of the interest evolution layer outputting the interest vector are:

[0030] The user embedding vector is used as the query vector and the hidden state sequence is used as the key vector. The similarity score between the query vector and the key vector is calculated and expressed as:

[0031] ;

[0032] Where, The query vector element and the key vector The similarity score between elements;

[0033] The attention weight is obtained by normalizing the similarity score through the softmax function and expressed as:

[0034] ;

[0035] Where, For the The attention weight corresponding to the hidden state, The element index for traversing similarity scores;

[0036] The interest vector is obtained by weighted summing of the key vectors through the attention weights , expressed as:

[0037] .

[0038] As a preferred embodiment of the present invention, the prediction layer includes an input layer, a fully connected layer and an output layer. The input layer takes the interest vector output by the interest evolution layer as input data. The fully connected layer has two layers, each with 128 neurons, and the activation function is ReLU, wherein the first layer is expressed as follows:

[0039] ;

[0040] Where, is the output of the first layer of fully connected layer, is the weight matrix of the first layer of fully connected layer, is the bias vector of the first layer of fully connected layer;

[0041] The second layer is expressed in formula as:

[0042]

[0043] In the formula, is the output of the second layer of fully connected layer, is the weight matrix of the second layer of fully connected layer, is the bias vector of the second layer of fully connected layer;

[0044] The output layer is a linear layer, and the output is the predicted value of the user's interest in different marketing activities, which is expressed in formula as:

[0045]

[0046] In the formula, is the output of the output layer, is the weight matrix of the output layer, is the bias vector of the output layer.

[0047] As a preferred embodiment of the present application, the weight of each sub-node is calculated according to the user's geographic location, preference settings and interest in different marketing activities, which is specifically:

[0048] Check if the user's geographic location matches the geographic location of the activity. If it matches, the geographic location weight is 1; otherwise, it is 0;

[0049] Check if the user's preference settings match the category of the activity. If it matches, the geographic location preference weight is 1; otherwise, it is 0;

[0050] Based on the geographic location weight, the preference setting weight and the interest in different marketing activities, the total weight of each sub-node to the user is calculated, which is expressed in formula as:

[0051] Total weight = geographic location weight * preference setting weight * user's interest in different marketing activities.

[0052] As a preferred embodiment of the present application, the weight sorting algorithm is a descending sorting algorithm. After sorting, the sub-node with the highest total weight will be placed at the front, and the sub-node with the lowest total weight will be placed at the end to obtain the sorting result.

[0053] ​​The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamically generating a mobile banking activity collection page as described in any embodiment of the present invention is implemented.

[0054] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for dynamically generating a mobile banking activity collection page as described in any embodiment of the present invention.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1) This invention provides a method for dynamically generating a mobile banking activity collection page. This method uses user behavior data, geographic location, and preference settings to accurately predict user interests, implement personalized recommendations, and improve user engagement and satisfaction. Furthermore, the activity collection page is dynamically updated based on real-time user behavior and geographic location changes, ensuring that the displayed activities always meet the user's current needs.

[0057] 2) This invention provides a method for dynamically generating a mobile banking activity collection page. The modular design of the tree structure makes the addition and management of activities more flexible, facilitating system maintenance and expansion. The optimization of the user interest prediction model and weight calculation algorithm improves the system's ability and efficiency in processing large amounts of data, ensuring page loading speed and user experience.

[0058] 3) The present invention provides a method for dynamically generating a mobile banking activity collection page. Through a sorting algorithm, the activities that users are most interested in are ranked in the most prominent position, thereby increasing the click-through rate and conversion rate of the activities and effectively improving marketing effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0061] The present invention provides the following technical solution: a method for dynamically generating a mobile banking activity collection page.

[0062] Example 1:

[0063] This embodiment provides a method for dynamically generating a mobile banking activity collection page, the method comprising:

[0064] S1. Construct a tree structure corresponding to the mobile banking activity collection page, wherein the tree structure includes a root node and multiple first-level nodes, wherein the root node represents the page corresponding to the mobile banking activity collection page, and the first-level nodes represent business categories;

[0065] Preferably, the root node and the first-level node are fixed;

[0066] Each first-level node represents a business category. In this implementation, the first-level nodes include the following categories: loan services, deposit products, investment and financial management, credit card services, and life payments;

[0067] Through the above design, different marketing activities can be classified and organized according to business categories, which facilitates subsequent dynamic generation and display;

[0068] S2. Obtain the current marketing activity data of mobile banking and dynamically generate sub-nodes corresponding to different marketing activities;

[0069] Obtaining the currently ongoing marketing activity data from the mobile banking database or other data sources, and dynamically generating a sub-node corresponding to each marketing activity based on the marketing activity data;

[0070] In this embodiment, a marketing campaign number, a marketing campaign name, a marketing campaign icon, a marketing campaign link, and a marketing campaign sequence are obtained based on the marketing campaign data. The marketing campaign link jump interface corresponds to the start and end time of the activity, participation conditions, and promotion methods. The marketing campaign number is a unique identifier. According to the business category to which the marketing campaign type belongs, a new child node is generated under the corresponding first-level node. The child node includes a node number, a node name, a leaf node flag, a parent node number, a node type, a picture name, a jump link, and a sequence number. Specifically, the node number corresponds to the marketing campaign number; the node name corresponds to the marketing campaign name; the leaf node flag is specifically used to determine whether the current node is a child node. If the current node is a child node, the leaf node flag is displayed as "yes", and if the current node is not a child node, the leaf node flag is displayed as "no"; the parent node number is the corresponding first-level node number; the node type corresponds to the marketing campaign type; the picture name corresponds to the marketing campaign icon; the jump link corresponds to the marketing campaign link; and the sequence number corresponds to the marketing campaign sequence.

[0071] Preferably, in order to ensure the consistency and integrity of the tree structure, the root node and the first-level node also include the elements included in the child nodes, that is, the root node and the first-level node also include the node number, node name, leaf node flag, parent node number, node type, picture name, jump link and serial number. The difference is that, among the elements included in the root node and the first-level node, the node number of the root node is 0, and there is no parent node number, picture name and jump link, and the serial number is 0; the node number of the first-level node increases from 1, the node name corresponds to the business category name, the parent node number is the root node number 0, the picture name corresponds to the business category icon, there is no jump link, and the serial number increases from 1;

[0072] S3. Collecting user behavior data, geographic location, and preference settings in mobile banking, including browsing history and transaction data; building a user interest prediction model, using the behavior data to train the user interest prediction model to obtain a trained user interest prediction model, and using the trained user interest prediction model to predict the user's interest in different marketing activities;

[0073] S31. The behavioral data includes the user's browsing history, transaction history, click behavior, and dwell time. The user ID, product ID, behavior type, and timestamp are obtained based on the user behavioral data. The geographic location includes the user's current location or frequently visited places to help understand the user's geographic preferences. The preference settings include the personalized preferences set by the user in mobile banking, including the types of services they are interested in, their risk preferences, and subscriptions to promotional activities.

[0074] S32, user interest prediction model;

[0075] The user interest prediction model is constructed based on the DIEN model, including an embedding layer, an interest extraction layer, an interest evolution layer, and a prediction layer, wherein:

[0076] S321, embedding layer;

[0077] The embedding layer includes multiple embedding matrices, each of which corresponds to a discrete feature. In this embodiment, the embedding layer includes three embedding matrices with an embedding dimension of 64. The discrete features include user ID, product ID, and behavior type.

[0078] The discrete features are input into the embedding layer and the user embedding vector is output. , product embedding vector and behavior type embedding vector The product embedding vector and the behavior type embedding vector are concatenated to form the behavior sequence embedding vector , expressed as:

[0079] ;

[0080] S322, interest extraction layer;

[0081] Embedding behavior sequences into vectors As the input data of the interest extraction layer, the interest extraction layer is a GRU structure, including an update gate and a reset gate, where:

[0082] The reset gate is used to control the impact of historical information on the current time step, which is expressed as:

[0083] ;

[0084] Where, is the output of the reset gate, is the sigmoid activation function, To influence the weight matrix of historical information in the reset gate; is the hidden state of the previous time step, Reset the weight matrix of the current input information in the gate to influence it; is the bias vector for resetting the gate;

[0085] The update gate is used to control the contribution of the candidate hidden state of the current time step to the final hidden state, which is expressed as:

[0086] ;

[0087] Where, is the output of the update gate, is the weight matrix that affects the historical information in the update gate; is the weight matrix that affects the current input information in the update gate; is the bias vector of the update gate;

[0088] Among them, the hidden state is expressed as:

[0089]

[0090]

[0091] Where, is the hidden state of the current time step, ⊙ is the XOR operation, is the candidate hidden state of the current time step; is the hyperbolic tangent activation function; is the weight matrix that affects the historical information in the candidate hidden state; is the weight matrix that affects the current input information in the candidate hidden state;

[0092] S323, interest evolution layer;

[0093] The user embedding vector output by the embedding layer and the hidden state sequence output by the interest extraction layer are used as input data of the interest evolution layer. The user embedding vector is used as the query vector, and the hidden state sequence is used as the key vector. The similarity score between the query vector and the key vector is calculated, which can be expressed as follows:

[0094] ;

[0095] Where, The query vector element and the key vector The similarity score between elements;

[0096] The attention weight is obtained by normalizing the similarity score through the softmax function and expressed as:

[0097] ;

[0098] Where, For the The attention weight corresponding to the hidden state, The element index for traversing similarity scores;

[0099] The interest vector is obtained by weighted summing of the key vectors through the attention weights , expressed as:

[0100] ;

[0101] S324, prediction layer;

[0102] In this embodiment, the prediction layer includes an input layer, a fully connected layer, and an output layer. The input layer uses the interest vector output by the interest evolution layer as input data. The fully connected layer has two layers, each with 128 neurons, and the activation function is ReLU. The first layer is expressed as follows:

[0103] ;

[0104] Where, is the output of the first fully connected layer, is the weight matrix of the first fully connected layer, is the bias vector of the first fully connected layer;

[0105] The second layer is expressed in the formula:

[0106] ;

[0107] Where, is the output of the second fully connected layer, is the weight matrix of the second fully connected layer, is the bias vector of the second fully connected layer;

[0108] The output layer is a linear layer that outputs the predicted value of the user's interest in different marketing activities, which can be expressed as:

[0109] ;

[0110] Where, is the output of the output layer, is the weight matrix of the output layer, is the bias vector of the output layer;

[0111] S4. Calculate the weight of each child node based on the user's geographic location, preferences, and interest in different marketing activities. Specifically:

[0112] Check if the user's geolocation matches the activity's geolocation. If so, the geolocation weight is 1; otherwise, it is 0.

[0113] Check if the user's preferences match the active category. If so, the location preference weight is 1; otherwise, it is 0.

[0114] The total weight of each child node to the user is calculated based on the geographic location weight, preference weight, and interest in different marketing activities, and is expressed as follows:

[0115] Total weight = geographic location weight * preference weight * user interest in different marketing activities;

[0116] S5. Sort the weights of the child nodes using a weight sorting algorithm. The weight sorting algorithm described in this embodiment is a descending sorting algorithm. After sorting, the child node with the highest total weight will be ranked first, and the child node with the lowest total weight will be ranked last. A sorting result is obtained, and the mobile banking activity collection page is dynamically laid out based on the sorting result. The child node with the higher ranking result has a larger proportion in the mobile banking activity collection page and is displayed at a higher front.

[0117] Example 2:

[0118] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamically generating a mobile banking activity collection page as described in Example 1 is implemented.

[0119] Example 3:

[0120] The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for dynamically generating a mobile bank activity collection page as described in the embodiment 1 of the application.

[0121] The above merely illustrates the embodiments of the application, and does not limit the patent scope of the application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which are made by using the content of the specification and drawings, are also included in the patent protection scope of the application.

Claims

1. A method for dynamically generating a mobile banking activity collection page, characterized in that: The method comprises: Constructing a tree structure corresponding to the mobile banking activity collection page, the tree structure including a root node and multiple first-level nodes, wherein the root node represents the page corresponding to the mobile banking activity collection page, and the first-level nodes represent business categories; Get the current marketing activity data of mobile banking and dynamically generate sub-nodes corresponding to different marketing activities, specifically: A marketing campaign number, a marketing campaign name, a marketing campaign icon, a marketing campaign link, and a marketing campaign sequence are obtained based on the marketing campaign data. The marketing campaign number is a unique identifier. Based on the business category to which the marketing campaign type belongs, a new child node is generated under the corresponding first-level node. The child node includes a node number, a node name, a leaf node flag, a parent node number, a node type, a picture name, a jump link, and a sequence number. The node number corresponds to the marketing campaign number; the node name corresponds to the marketing campaign name; the leaf node flag is specifically used to determine whether the current node is a child node. If the current node is a child node, the leaf node flag is displayed as "yes", and if the current node is not a child node, the leaf node flag is displayed as "no"; the parent node number is the corresponding first-level node number; the node type corresponds to the marketing campaign type; the picture name corresponds to the marketing campaign icon; the jump link corresponds to the marketing campaign link; and the sequence number corresponds to the marketing campaign sequence. Collecting user behavior data, geographic location, and preference settings in mobile banking, building a user interest prediction model, using the behavior data to train the user interest prediction model to obtain a trained user interest prediction model, and using the trained user interest prediction model to predict the user's interest in different marketing activities; The weight of each sub-node is calculated based on the user's geographic location, preference settings, and interest in different marketing activities, and the weights of the sub-nodes are sorted using a weight sorting algorithm to obtain the sorting results. The mobile banking activity collection page is dynamically laid out based on the sorting results. The sub-nodes with higher rankings have a larger proportion in the mobile banking activity collection page and are displayed at the front.

2. A method for dynamically generating a mobile banking activity collection page according to claim 1, characterized in that: The user interest prediction model is built based on the DIEN model, including an embedding layer, an interest extraction layer, an interest evolution layer, and a prediction layer. The behavior data is input into the embedding layer as input data. The behavior data includes user ID, product ID, and behavior type. The embedding layer outputs the user embedding vector , product embedding vector and behavior type embedding vector The product embedding vector and the behavior type embedding vector are concatenated to form the behavior sequence embedding vector , expressed as: ； Embedding behavior sequences into vectors As the input data of the interest extraction layer, it outputs a hidden state sequence; The user embedding vector output by the embedding layer and the hidden state sequence output by the interest extraction layer are used as the input data of the interest evolution layer, and the interest vector is output. ; The interest vector As the input data of the prediction layer, it outputs the predicted value of the user's interest in different marketing activities.

3. A method for dynamically generating a mobile banking activity collection page according to claim 2, characterized in that: The interest extraction layer is a GRU structure, including an update gate and a reset gate, where: The reset gate is used to control the impact of historical information on the current time step, which is expressed as: ； Where, is the output of the reset gate, is the sigmoid activation function, To influence the weight matrix of historical information in the reset gate; is the hidden state of the previous time step, Reset the weight matrix of the current input information in the gate to influence it; is the bias vector for resetting the gate; The update gate is used to control the contribution of the candidate hidden state of the current time step to the final hidden state, which is expressed as: ； Where, is the output of the update gate, is the weight matrix that affects the historical information in the update gate; is the weight matrix that affects the current input information in the update gate; is the bias vector of the update gate; Among them, the hidden state is expressed as: Where, is the hidden state of the current time step, ⊙ is the XOR operation, is the candidate hidden state of the current time step; is the hyperbolic tangent activation function; is the weight matrix that affects the historical information in the candidate hidden state; is the weight matrix that affects the current input information in the candidate hidden state.

4. A method for dynamically generating a mobile banking activity collection page according to claim 2, characterized in that: The specific steps of the interest evolution layer outputting the interest vector are: The user embedding vector is used as the query vector and the hidden state sequence is used as the key vector. The similarity score between the query vector and the key vector is calculated and expressed as: ； Where, is the query vector element and the key vector The similarity score between elements; The attention weight is obtained by normalizing the similarity score through the softmax function and expressed as: ； Where, For the The attention weight corresponding to the hidden state, The element index for traversing similarity scores; The interest vector is obtained by weighted summing of the key vectors through the attention weights , expressed as: 。 5. The method for dynamically generating a mobile banking activity collection page according to claim 2, characterized in that: The prediction layer includes an input layer, a fully connected layer, and an output layer. The input layer takes the interest vector output by the interest evolution layer as input data. The fully connected layer has two layers, each with 128 neurons, and the activation function is ReLU. The first layer is expressed as follows: ； Where, is the output of the first fully connected layer, is the weight matrix of the first fully connected layer, is the bias vector of the first fully connected layer; The second layer is expressed in the formula: ； Where, is the output of the second fully connected layer, is the weight matrix of the second fully connected layer, is the bias vector of the second fully connected layer; The output layer is a linear layer that outputs the predicted value of the user's interest in different marketing activities, which can be expressed as: ； Where, is the output of the output layer, is the weight matrix of the output layer, is the bias vector of the output layer.

6. A method for dynamically generating a mobile banking activity collection page according to claim 2, characterized in that: The weight of each child node is calculated based on the user's geographic location, preferences, and interest in different marketing activities: Check if the user's geolocation matches the activity's geolocation. If so, the geolocation weight is 1; otherwise, it is 0. Check if the user's preferences match the active category. If so, the location preference is weighted as 1. Otherwise, 0; The total weight of each child node to the user is calculated based on the geographic location weight, preference weight, and interest in different marketing activities, and is expressed as follows: Total weight = geographic location weight * preference weight * user interest in different marketing activities.

7. A method for dynamically generating a mobile banking activity collection page according to claim 6, characterized in that: The weight sorting algorithm is a descending sorting algorithm. After sorting, the child node with the highest total weight will be placed at the front, and the child node with the lowest total weight will be placed at the end to obtain the sorting result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for dynamically generating a mobile banking activity collection page according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for dynamically generating a mobile banking activity collection page as described in any one of claims 1 to 7 is implemented.

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