A cross-border e-commerce marketing strategy intelligent management system based on big data

Abstract

The application discloses a kind of based on big data's cross-border e-commerce marketing strategy intelligent management system, it is related to cross-border e-commerce marketing strategy management technical field, the present application constructs user-goods-culture relation graph using heterogeneous graph neural network, and through attention mechanism, each node is given dynamic weight, effectively capture the difference of each regional consumer in historical transaction, click rate and cultural similarity, to break through the problem of insufficient adaptability of traditional fixed rule model in multicultural market;Adopt the structure of Transformer to extract sentiment and semantic features from multilingual review texts, and combine meta-learning strategies to enable the model to quickly adapt in data-scarce situations;This combination can accurately extract deep semantic information reflecting the sentiments and preferences of different regions, and by fusing graph embeddings and text processing results, generate cross-cultural adaptive recommendation results, making marketing content more in line with the individual needs of consumers in different regions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cross-border e-commerce marketing strategy management, and particularly relates to an intelligent cross-border e-commerce marketing strategy management system based on big data. BACKGROUND

[0002] The existing cross-border e-commerce marketing system mostly relies on transaction records, click rates and user browsing data, and achieves personalized recommendation through statistical learning after constructing a unified user portrait.

[0003] However, the global market is increasingly diverse, and the aesthetics, consumption habits and cultural backgrounds of consumers in different regions are different. Traditional systems mostly process data of different countries in the same model, which leads to deviation of the recommendation strategy in the market with different cultural backgrounds. For example, the same product emphasizes design concept more in the European and American markets, while pays more attention to cost performance and practicality in Asia. However, the single rule or manual partitioning method is difficult to capture the emotional changes of consumers to products and local hotspots on social media in time, which leads to disconnection between marketing content and actual demand.

[0004] In view of this, some enterprises add regional rules and manual optimization, but such methods have lagging data updates and are difficult to balance real-time and accuracy, which still leads to a significant gap between the recommendation effect and the actual preferences of users. At the same time, the changing market environment and dynamic feedback of social media in the process of globalization further expose the shortcomings of traditional systems in cross-cultural adaptability, which affects the conversion rate and customer loyalty. Under this background, how to use more dimensional data resources to deeply mine the cultural information behind user behavior has become an urgent problem to be solved. SUMMARY

[0005] In view of the above existing problems, the present application is proposed.

[0006] The present application provides an intelligent cross-border e-commerce marketing strategy management system based on big data, which solves the problem that traditional recommendation algorithms ignore the subtle behaviors and emotional preferences of consumers under different cultural backgrounds, leading to low hit rate of recommendations in European and American markets, Southeast Asian markets and the like.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] The present application provides an intelligent cross-border e-commerce marketing strategy management system based on big data, which solves the problem that traditional recommendation algorithms ignore the subtle behaviors and emotional preferences of consumers under different cultural backgrounds, leading to low hit rate of recommendations in European and American markets, Southeast Asian markets and the like.

[0009] The e-commerce data acquisition module acquires original e-commerce data and constructs a heterogeneous data set containing users, goods and cultural characteristics;

[0010] The data preprocessing module is used for preprocessing the heterogeneous data set;

[0011] The personalized recommendation and localization optimization module is constructed based on a multi-task joint learning model, and the model comprises:

[0012] A heterogeneous graph neural network is used to establish a relationship graph among users, commodities and cultural labels, and an attention mechanism is used to give dynamic weights to the relationships between nodes in the relationship graph.

[0013] A Transformer structure is used to process multi-language text and social comment information, and extract semantic information reflecting regional sentiment and preference from original e-commerce data.

[0014] A meta-learning sub-module is used to adaptively adjust the model parameters in the case of data scarcity.

[0015] A feedback adjustment module is used to collect the output of the management system and real-time feedback data from the market in real time, and to update and optimize the model parameters in the personalized recommendation and localization optimization module according to the predetermined strategy.

[0016] As a preferred scheme of the intelligent management system for cross-border e-commerce marketing strategy based on big data, the original e-commerce data comprises e-commerce platform transaction records, user behavior logs and social media text data.

[0017] The e-commerce data collection module comprises an interface for cross-platform data connection and a big data engine for real-time data transmission, which is used to ensure the timeliness and continuity of data collection, so that the user-commodity-cultural feature matrix has dynamic updating capability.

[0018] As a preferred scheme of the intelligent management system for cross-border e-commerce marketing strategy based on big data, the preprocessing comprises cleaning, normalizing and data fusion of the data in the heterogeneous data set to form a user-commodity-cultural label feature matrix in a unified format, wherein the cultural feature matrix is labeled according to the internationally recognized cultural index system.

[0019] As a preferred scheme of the intelligent management system for cross-border e-commerce marketing strategy based on big data, in the personalized recommendation and localization optimization module, the heterogeneous graph neural network constructs graph embedding according to the historical transaction data, click rate and cultural similarity of the user in different regions; the Transformer structure performs sentiment and semantic modeling based on multi-language comment text, and the combination of the two outputs a cross-cultural adaptive recommendation result, which is used to reflect the different focus and consumption preferences of different markets for the same commodity; product recommendation is performed based on the cross-cultural adaptive recommendation result.

[0020] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, in the individualized recommendation and localization optimization module, the step of constructing the graph embedding according to the historical transaction data, the click rate and the cultural similarity of the user in different regions is,

[0021] According to the historical transaction data, the click rate and the cultural similarity between nodes, the correlation score between nodes is calculated, and the calculation formula is:

[0022]

[0023] Wherein, e ij represents the correlation score between node i and node j, a represents the attention weight vector, and the transpose is used to map the spliced vector to the score space, b represents the feature transformation matrix, x i and x j are the initial feature vectors of node i and node j respectively, ‖ represents splicing two vectors, c1 represents the coefficient for transaction data T ij , c2 represents the coefficient for click rate data C ij , and c3 represents the coefficient for cultural similarity data R ij , T ij , C ij and R ij represent the historical transaction data, the click rate index and the cultural similarity index between node i and node j, and i and j are node indexes.

[0024] The normalized attention weight between nodes is calculated, and the formula is:

[0025]

[0026] Wherein, α ij represents the normalized attention weight between node i and node j, exp(·) is an exponential function, represents the neighbor set of node i, k is the neighbor node index, e ij and e ik are the correlation scores between nodes.

[0027] The node embedding is updated by aggregating neighbor information, and the update formula is:

[0028]

[0029] Wherein, h i represents the graph embedding of node i, α ij is the normalized attention weight of node i and neighbor node j, b is the feature transformation matrix, x jis the initial feature vector of the neighbor node j, j is the index of the neighbor node, is the neighbor set of node i.

[0030] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, the Transformer structure is used for sentiment and semantic modeling based on multilingual review texts, and the step of combining the two to output cross-cultural adaptability recommendation results is,

[0031] For multilingual review texts, the Transformer structure is used for sentiment and semantic feature extraction, the preprocessed text is represented as an embedding vector z0, and then the projection of the query, key and value is performed, and the formula is:

[0032] Q=z0d, K=z0e, V=z0f,

[0033] Wherein, Q represents the query vector in the Transformer, K represents the key vector in the Transformer, V represents the value vector in the Transformer, z0 is the text embedding after preprocessing, d is the query projection matrix, e is the key projection matrix, and f is the value projection matrix.

[0034] Feature fusion is performed using the scaled dot-product attention mechanism, and the fusion formula is:

[0035]

[0036] Wherein, A represents the vector output by the attention mechanism, The transpose of the key vector K, g is the scaling factor, softmax(·) represents the standard normalization function, and V is the value vector.

[0037] The attention output is normalized and fused with the initial embedding to obtain the semantic representation:

[0038] z=LayerNorm(z0+A),

[0039] Wherein, z represents the semantic representation output by the Transformer structure, LayerNorm(·) represents the layer normalization operation, z0 is the initial text embedding, and A is the attention output.

[0040] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, the Transformer structure is used for sentiment and semantic modeling based on multilingual review texts, and the step of combining the two to output cross-cultural adaptability recommendation results is,

[0041] Perform sentiment analysis through a fully connected layer:

[0042] s = σ(Iz + J),

[0043] where s represents the sentiment score, sigma (·) represents the activation function, I is the sentiment analysis weight matrix, z is the semantic representation output by the Transformer, and J is the sentiment analysis bias;

[0044] Let the heterogeneous graph embedding be h, and fuse h, the Transformer semantic representation z and the sentiment score s:

[0045]

[0046] where r represents the final output cross-cultural adaptability recommendation result, k is the fusion weight matrix,

[0047] represents a vector formed by vertically concatenating the graph embedding h, the semantic representation z and the sentiment score s,

[0048] l is the fusion bias, and h is the node embedding.

[0049] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, the adaptive adjustment mode of the meta-learning sub-module to the model parameters in the case of data scarcity is:

[0050] The meta-learning strategy based on gradient update is used to update each task τ in the inner loop, and the formula is:

[0051]

[0052] where m ' τ represents the model parameters obtained after updating in the task τ, m represents the initial model parameters, and p is the inner update step, represents the gradient calculation of the model parameters m, l τ (m) represents the loss function of the task τ, τ is the task index, and different tasks

[0053] In the outer loop, the global parameter update is performed according to the loss information of all tasks, and the update formula is:

[0054]

[0055] where m represents the updated model parameters, q is the outer update step, which controls the amplitude of the global parameter update, and ∑ τ (·) represents the summation operation of all task losses, represents the gradient operation with respect to the parameters m, l τ (m' τ ) represents the loss value under each task τ after the inner loop update.

[0056] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, the feedback adjustment module comprises a monitoring submodule for monitoring the effect of the marketing strategy in real time and a decision submodule for adaptive updating of the feedback-driven model, the monitoring submodule uses a real-time data statistical analysis management system to monitor performance indicators, and the decision submodule automatically optimizes model parameters according to preset safety margins and benefit thresholds; the system can respond to price fluctuations, inventory status and regional promotion strategies in real time in the cross-border e-commerce environment.

[0057] The management system output data includes recommendation results generated by the personalized recommendation and localization optimization module, content display data and system self-diagnosis logs.

[0058] The market real-time feedback data includes real-time transaction data, user click rate, conversion rate, online comments, social media sentiment analysis results, inventory status, exchange rate fluctuations and competitor price dynamics from e-commerce platforms.

[0059] The real-time data used by the monitoring submodule refers to a data set that is updated at a time scale of milliseconds to minutes in the management system output data and the market real-time feedback data.

[0060] The performance indicators reflect the overall operation efficiency and recommendation effect of the system, including click rate, conversion rate, user dwell time, recommendation accuracy, inventory turnover rate and advertising cost return rate.

[0061] The management system output data here reflects the system internal self-diagnosis and strategy execution effect, and the market real-time feedback data is the real-time dynamic monitoring of the external environment, both of which exist in the form of high-frequency real-time data, and the statistical and analysis results constitute the system performance indicators, which are used to drive the decision submodule to adaptively update and optimize the model parameters according to the preset safety margins and benefit thresholds.

[0062] As a preferred scheme of the cross-border e-commerce marketing strategy intelligent management system based on big data, in the feedback adjustment module, the step of online updating and optimizing the model parameters in the personalized recommendation and localization optimization module according to the predetermined strategy is,

[0063] The feedback adjustment module collects management system output data and market real-time feedback data in real time, constructs comprehensive performance indicators, and online updates the model parameters according to the preset strategy, and the comprehensive performance indicator calculation formula is:

[0064] P = γX + (1-γ)Y,

[0065] Wherein, P represents the comprehensive performance index, gamma is the weight coefficient of the management system output data, X represents the management system output data, Y represents the market real-time feedback data, 1-gamma is the supplementary weight, represents the contribution of the market feedback data,

[0066] According to the performance index and the preset safety margin and benefit threshold, the deviation function is defined:

[0067] If P < u, then I(P) = u-P,

[0068] If P > v, then I(P) = P-v,

[0069] Otherwise, I(P) = 0.

[0070] Wherein, I(P) represents the performance deviation function, u is the preset safety margin, v is the preset benefit threshold, and P is the comprehensive performance index.

[0071] The online updating formula of the final model parameter is:

[0072]

[0073] Wherein, m represents the current model parameter, w is the online updating control weight, I(P) is the deviation value obtained from the performance deviation function, Indicates the loss function gradient about the model parameter m, and l(m) is the loss function of the model.

[0074] The present application has the beneficial effects that: the present application integrates global e-commerce transaction records, user behavior logs and social media text data, and labels the data according to the international cultural index system, realizes the heterogeneous data fusion of users, goods and cultural labels, uses a heterogeneous graph neural network to construct a user-goods-culture relationship graph, and assigns dynamic weights to each node through an attention mechanism, effectively capturing the differences of consumers in different regions in historical transactions, click rates and cultural similarity, thereby breaking through the problem of insufficient adaptability of traditional fixed rule models in multicultural markets.

[0075] The present application adopts the Transformer structure to extract sentiment and semantic features from multilingual review texts, and realizes the rapid self-adaptation of the model in the case of data scarcity by combining the meta-learning strategy; the combination can accurately extract deep semantic information reflecting the sentiments and preferences of different regions, and generate cross-cultural adaptive recommendation results by fusing graph embedding and text processing results, so that the marketing content is more in line with the individualized needs of consumers in different places.

[0076] The application sets a real-time feedback adjustment module, acquires management system output and market dynamic data in real time, including transaction volume, click rate, inventory status, exchange rate fluctuation and competitor price dynamics, and constructs a comprehensive performance index. According to preset safety margin and benefit threshold, system deviation is automatically determined and model parameters are updated online by using gradient descent method, so as to realize instant response to price fluctuation and promotion strategy, thereby improving the overall operation efficiency and stability of the system.

[0077] In summary, the application not only solves the limitations of traditional cross-border e-commerce systems in dealing with cross-cultural differences, data dynamic updating and real-time feedback adjustment, but also provides reliable technical support for enterprises to establish an efficient and agile marketing system in a global multi-market, and has significant economic benefits and application value. BRIEF DESCRIPTION OF DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0079] Figure 1 The figure is a schematic diagram of the framework of the cross-border e-commerce marketing strategy intelligent management system based on big data of the application. DETAILED DESCRIPTION

[0080] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings of the specification.

[0081] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the application, therefore the application is not limited to the specific embodiments disclosed below.

[0082] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the application. In this specification, "in one embodiment" appearing in different places does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0083] Embodiment 1, refer to Figure 1 The embodiment provides a cross-border e-commerce marketing strategy intelligent management system based on big data, which comprises:

[0084] An e-commerce data collection module collects original e-commerce data and constructs a heterogeneous dataset containing users, goods, and cultural characteristics;

[0085] The original e-commerce data includes e-commerce platform transaction records, user behavior logs, and social media text data;

[0086] The e-commerce data collection module includes an interface for cross-platform data connection and a big data engine for real-time data transmission, which ensures the timeliness and continuity of data collection, thereby enabling the user-goods-cultural characteristic matrix to have dynamic updating capability;

[0087] A data preprocessing module is used to preprocess the heterogeneous dataset;

[0088] The preprocessing includes cleaning, normalizing, and data fusion of the data in the heterogeneous dataset to form a user-goods-cultural label feature matrix in a unified format, wherein the cultural characteristic matrix is labeled according to an internationally recognized cultural index system;

[0089] An individualized recommendation and localization optimization module is constructed based on a multi-task joint learning model, which includes:

[0090] A heterogeneous graph neural network is used to establish a relationship graph between users, goods, and cultural labels, and to assign dynamic weights to the relationships between nodes in the relationship graph through an attention mechanism;

[0091] A Transformer structure is used to process multi-language text and social comment information, and to extract semantic information reflecting regional sentiment and preferences from the original e-commerce data;

[0092] A meta-learning sub-module is used to adaptively adjust the model parameters in the case of data scarcity;

[0093] In the individualized recommendation and localization optimization module, the heterogeneous graph neural network constructs graph embeddings based on historical transaction data, click rates, and cultural similarities of users in different regions; the Transformer structure performs sentiment and semantic modeling based on multi-language review text, and the combination of the two outputs a cross-cultural adaptive recommendation result that reflects different market focuses and consumer preferences for the same product; product recommendation is performed based on the cross-cultural adaptive recommendation result;

[0094] In the individualized recommendation and localization optimization module, the step of constructing graph embeddings by the heterogeneous graph neural network based on historical transaction data, click rates, and cultural similarities of users in different regions is,

[0095] According to the historical transaction data, click rates, and cultural similarities between nodes, the correlation score between nodes is calculated, and the calculation formula is:

[0096]

[0097] Among them, e ij represents the association score between node i and node j, a represents the attention weight vector, and its transpose Used to map the concatenated vector to the score space, b represents the feature transformation matrix, x i with x j are the initial feature vectors of node i and node j respectively, ‖ represents the concatenation of the two vectors, and c1 represents the transaction data T ij The coefficient of C2 is used for click rate data C ij The coefficient of c3 is used for cultural similarity data R ij The coefficient of T ij 、C ij and R ij Represent the historical transaction data, click-through rate index and cultural similarity index between node i and node j respectively, i and j are node indexes;

[0098] Calculate the normalized attention weight between nodes using the formula:

[0099]

[0100] Among them, α ij represents the normalized attention weight between node i and node j, exp(·) is the exponential function, represents the neighbor set of node i, k is the neighbor node index, e ij With e ik are the association scores between nodes respectively;

[0101] Update node embedding by aggregating neighbor information. The update formula is:

[0102]

[0103] Among them, h i represents the graph embedding of node i, α ij is the normalized attention weight of node i and its neighbor node j, b is the feature transformation matrix, x j is the initial feature vector of neighbor node j, j is the neighbor node index, is the neighbor set of node i;

[0104] Specifically, the heterogeneous graph composed of users, commodities and cultural tags is regarded as nodes in the network, the association strength between nodes is measured by fusing historical transaction data, click rate and cultural similarity, the concatenated features are nonlinearly mapped by using LeakyReLU function, and the information of each neighbor node is involved in the update of node embedding with different weights by combining normalized attention mechanism, so as to realize the conversion of multi-source data to graph embedding;

[0105] The Transformer structure performs sentiment and semantic modeling based on multilingual review texts, and the steps for outputting cross-cultural adaptive recommendation results by combining the two are,

[0106] For multilingual review texts, the Transformer structure is used to extract sentiment and semantic features, the preprocessed text is represented as an embedding vector z0, and then the projection of query, key and value is performed, and the formula is:

[0107] Q = z0d, K = z0e, V = z0f,

[0108] Wherein, Q represents the query vector in the Transformer, K represents the key vector in the Transformer, V represents the value vector in the Transformer, z0 is the text embedding after preprocessing, d is the query projection matrix, e is the key projection matrix, and f is the value projection matrix.

[0109] Feature fusion is performed by using the scaled dot-product attention mechanism, and the fusion formula is:

[0110]

[0111] Wherein, A represents the vector output by the attention mechanism, represents the transpose of the key vector K, g is a scaling factor, softmax(·) represents a standard normalization function, and V is a value vector.

[0112] The attention output and the initial embedding are normalized and fused to obtain the semantic representation:

[0113] z = LayerNorm(z0 + A),

[0114] Wherein, z represents the semantic representation output by the Transformer structure, LayerNorm(·) represents a layer normalization operation, z0 is the initial text embedding, and A is the attention output.

[0115] The Transformer structure performs sentiment and semantic modeling based on multilingual review texts, and the steps for outputting cross-cultural adaptive recommendation results by combining the two are,

[0116] Perform sentiment analysis through a fully connected layer:

[0117] s = σ(Iz + J),

[0118] where s denotes the sentiment score, σ(·) denotes the activation function, I is the sentiment analysis weight matrix, z is the semantic representation output by the Transformer, and J is the sentiment analysis bias;

[0119] Let the heterogeneous graph embedding be h, and fuse h, the Transformer semantic representation z, and the sentiment score s:

[0120]

[0121] where r represents the final output of the cross-cultural adaptability recommendation result, k is the fusion weight matrix,

[0122] represents a vector formed by vertically concatenating the graph embedding h, the semantic representation z, and the sentiment score s,

[0123] l is the fusion bias, and h is the node embedding;

[0124] Specifically, the Transformer structure is used here to process multilingual review texts, semantic information is extracted by projecting the text embedding for query, key, and value respectively, combined with the scaled dot-product attention mechanism, LayerNorm operation is used to ensure the stability of the output, and then a fully connected layer and an activation function are used for sentiment score calculation. Finally, the text processing result is fused with the heterogeneous graph embedding to construct a recommendation result reflecting the cross-cultural user preferences;

[0125] The way the meta-learning sub-module adaptively adjusts the model parameters in the case of data scarcity is:

[0126] The meta-learning strategy based on gradient update is used to update each task τ in the inner loop, and the formula is:

[0127]

[0128] where m' τ represents the model parameters obtained after updating in task τ, m represents the initial model parameters, and p is the inner update step size, represents the gradient calculation of the model parameters m, l τ (m) represents the loss function of task τ, τ is the task index, and different tasks

[0129] In the outer loop, the global parameter update is performed according to the loss information of all tasks, and the update formula is:

[0130]

[0131] wherein, m represents the updated model parameters, q is an outer update step that regulates the magnitude of global parameter update, ∑ τ (·) represents a summation operation over all task losses, represents a gradient operation with respect to the parameter m, l τ (m' τ ) represents the loss value under each task τ after the inner loop update;

[0132] Specifically, the meta-learning strategy of the inner and outer loops is used to solve the problem of rapid adaptation of the model under the condition of data scarcity. The inner loop fine-tunes the model parameters using single-task data to form task-specific parameter updates. The outer loop integrates the update information of all tasks to correct the global parameters of the model.

[0133] Under the premise of ensuring the uniqueness and consistency of parameters among tasks, efficient parameter iteration is achieved under the condition of small samples;

[0134] a feedback adjustment module that collects the management system output and real-time market feedback data in real time, and updates and optimizes the model parameters in the individualized recommendation and localization optimization module according to the predetermined strategy;

[0135] The feedback adjustment module includes a monitoring submodule for real-time monitoring of marketing strategy effectiveness and a decision submodule for feedback-driven model adaptive update. The monitoring submodule uses the performance indicators of the real-time data statistical analysis management system, and the decision submodule automatically optimizes the model parameters according to the preset safety margin and benefit threshold. This realizes the system's immediate response to price fluctuations, inventory status, and regional promotion strategies in the cross-border e-commerce environment.

[0136] The management system output data includes the recommendation results generated by the individualized recommendation and localization optimization module, content display data, and system self-diagnosis logs;

[0137] The real-time market feedback data includes real-time transaction data, user click-through rate, conversion rate, online reviews, social media sentiment analysis results, inventory status, exchange rate fluctuations, and competitor price dynamics from e-commerce platforms;

[0138] The real-time data used by the monitoring submodule refers to the data set updated at a time scale of milliseconds to minutes in the management system output data and the real-time market feedback data;

[0139] The performance indicators reflect the quantitative indicators of the overall system efficiency and recommendation effectiveness, including click-through rate, conversion rate, user dwell time, recommendation accuracy, inventory turnover rate, and return on advertising spend;

[0140] The management system output data here is output as a reflection of system internal self-diagnosis and strategy implementation effect, and the market real-time feedback data is used as instant dynamic monitoring of external environment, both of which exist in the form of high-frequency real-time data, and the statistical and analysis results thereof constitute system performance indexes, which are used to drive the decision sub-module to adaptively update and optimize the model parameters according to preset safety margin and benefit threshold;

[0141] In the feedback adjustment module, the step of online updating and optimizing the model parameters in the individualized recommendation and localization optimization module according to the predetermined strategy is,

[0142] The feedback adjustment module collects the management system output data and the market real-time feedback data in real time, constructs a comprehensive performance index, and online updates the model parameters according to a preset strategy, and the comprehensive performance index calculation formula is:

[0143] P = γX + (1-γ)Y,

[0144] Wherein, P represents the comprehensive performance index, γ is the weight coefficient of the management system output data, X represents the management system output data, Y represents the market real-time feedback data, and 1-γ is the complementary weight, representing the contribution of the market feedback data,

[0145] According to the performance index and the preset safety margin and benefit threshold, a deviation function is defined:

[0146] If P < u, then I(P) = u-P,

[0147] If P > v, then I(P) = P-v,

[0148] Otherwise, I(P) = 0.

[0149] Wherein, I(P) represents the performance deviation function, u is the preset safety margin, v is the preset benefit threshold, and P is the comprehensive performance index.

[0150] The online updating formula of the final model parameter is:

[0151]

[0152] Wherein, m represents the current model parameter, w is the online updating control weight, I(P) is the deviation value obtained from the performance deviation function, represents the loss function gradient with respect to the model parameter m, and l(m) is the loss function of the model.

[0153] Specifically, the feedback adjustment module is described herein by fusing the market feedback data output by the management system to construct a comprehensive performance index, and using a preset safety margin and benefit threshold value to determine the system deviation, according to the update amount calculated by the deviation function, the model parameters are adjusted online through the gradient descent method, and the adaptive optimization of the system is realized. This process makes full use of real-time data feedback to realize accurate correction of parameters, thereby ensuring that the cross-border e-commerce recommendation system can quickly respond to changes in the external environment.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. A big data-based cross-border e-commerce marketing strategy intelligent management system, characterized in that: The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. 2.The big data-based cross-border e-commerce marketing strategy intelligent management system according to claim 1, characterized in that: The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. 3.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 2, wherein: The application relates to a cross-cultural adaptive recommendation system and method. 4.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 1, wherein: The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. Among them, e ij represents the association score between node i and node j, a represents the attention weight vector, and its transpose a T Used to map the concatenated vector to the score space, b represents the feature transformation matrix, x i with x j are the initial feature vectors of node i and node j respectively, ‖ represents the concatenation of the two vectors, and c1 represents the vector used for T ij The coefficient of C2 is used for ij The coefficient of c3 is used for R ij The coefficient of T ij 、C ij and R ij Represent the historical transaction data, click-through rate index and cultural similarity index between node i and node j respectively, i and j are node indexes; The application relates to a cross-cultural adaptive recommendation system and method. wherein, α ij represents the normalized attention weight between node i and node j, exp(·) is an exponential function, represents the neighbor set of node i, k is the neighbor node index, e ij and e ik are the correlation scores between nodes, respectively; The application relates to a cross-cultural adaptive recommendation system and method. where h i denotes the graph embedding of node i, a ij is the normalized attention weight of node i and neighbor node j, b is the feature transformation matrix, x j is the initial feature vector of neighbor node j, j is the index of neighbor node, is the neighbor set of node i. 5.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 4, wherein: The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. The application relates to a cross-cultural adaptive recommendation system and method. 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The application relates to a cross-cultural Feature fusion is performed using scaled dot-product attention mechanism, and the fusion formula is: where A denotes the vector output of the attention mechanism, denotes the transpose of the key vector K, g is a scaling factor, softmax(•) denotes the standard normalization function, and V is the value vector. The attention output is normalized and fused with the initial embedding to obtain the semantic representation: z = LayerNorm(z0 + A), where z represents the semantic representation output by the Transformer structure, and LayerNorm(·) represents the layer normalization operation. 6.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 5, wherein: The Transformer structure performs sentiment and semantic modeling based on multilingual review text, and the steps of combining the two to output cross-cultural adaptability recommendation results further include: Perform sentiment analysis through a fully connected layer: s = σ(Iz + J), where s represents the sentiment score, σ(·)I represents the activation function, h is the sentiment analysis weight matrix, z is the semantic representation output by the Transformer, and J is the sentiment analysis bias; Let the heterogeneous graph embedding be h, and fuse h, the Transformer semantic representation z, and the sentiment score s: where r represents the final output of the cross-cultural adaptability recommendation result, k is the fusion weight matrix, a vector formed by concatenating the graph embedding h, the semantic representation z, and the sentiment score s longitudinally, and l is the fusion bias. 7.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 6, wherein: The meta-learning sub-module adjusts the model parameters adaptively in the case of data scarcity in the following manner: The meta-learning strategy based on gradient update is used to update each task τ in the inner loop, and the formula is: wherein m ' τ denotes the model parameters obtained after updating within the task τ, m denotes the initial model parameters, p is the inner update step, denotes the gradient calculation of the model parameters m, l τ (m) denotes the loss function of the task τ, τ is the task index, denotes different tasks, In the outer loop, the global parameter update is performed according to the loss information of all tasks, and the update formula is: where m represents the updated model parameters, q is the outer update step, which regulates the magnitude of global parameter update, ∑ τ (·) represents the summation operation on all task losses, represents the gradient operation with respect to the parameter m, l τ (m' τ ) represents the loss value under each task τ after the inner loop update. 8.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 7, wherein: The feedback adjustment module includes a monitoring sub-module for real-time monitoring of marketing strategy effectiveness and a decision sub-module for feedback-driven model adaptive update, the monitoring sub-module uses the performance indicators of the real-time data statistical analysis management system, and the decision sub-module automatically optimizes the model parameters according to the preset safety margin and benefit threshold; The management system output data includes the recommendation results generated by the personalized recommendation and localization optimization module, the content display data, and the system self-diagnosis log; The market real-time feedback data includes real-time transaction data, user click rate, conversion rate, online reviews, social media sentiment analysis results, inventory status, exchange rate fluctuations, and competitor price dynamics from e-commerce platforms; The real-time data used by the monitoring sub-module refers to the data set updated at a time scale of milliseconds to minutes in the management system output data and the market real-time feedback data; The performance indicators reflect the quantitative indicators of the overall system operation efficiency and recommendation effect, including click-through rate, conversion rate, user dwell time, recommendation accuracy, inventory turnover rate, and advertising cost return rate. 9.The big data-based cross-border e-commerce marketing strategy intelligent management system of claim 8, wherein: In the feedback adjustment module, the step of online updating and optimizing the model parameters in the personalized recommendation and localization optimization module according to the predetermined strategy is, The feedback adjustment module collects the management system output data and market real-time feedback data in real time, constructs a comprehensive performance indicator, and online updates the model parameters according to the preset strategy, and the comprehensive performance indicator calculation formula is: P = γX + (1 - γ)Y, where P represents the comprehensive performance indicator, γ is the weight coefficient of the management system output data, X represents the management system output data, Y represents the market real-time feedback data, 1 - γ is the supplementary weight, representing the contribution of market feedback data, According to the performance index and the preset safety margin and benefit threshold, a deviation function is defined: If P < u, I(P) = u-P, If P > v, I(P) = P-v, Otherwise, I(P) = 0; Wherein, I(P) represents the performance deviation function, u is the preset safety margin, v is the preset benefit threshold, and P is the comprehensive performance index; The online updating formula of the final model parameters is: wherein m represents the current model parameter, w is an online update control weight, I(P) is a bias value obtained by a performance bias function, denotes the gradient of the loss function with respect to the model parameter m, l(m) is the loss function of the model.

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