Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data

Abstract

This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed.

1. Introduction

The cross-border e-commerce industry necessitates intelligent empowerment. As the world’s largest trading nation, China has seen rapid growth in its cross-border e-commerce sector. Since 2014, customs codes “9610” and “1210” have included B2C retail and bonded models in statistics. Starting in 2020, codes “9710” and “9810” were introduced for B2B direct exports and exports to overseas warehouses. Cross-border e-commerce transactions surged from 36 billion RMB in 2015 to 2.38 trillion RMB in 2023, with exports consistently dominating over 75% of the total. In 2023, export transactions reached 1.83 trillion RMB (76.9%), while imports totaled 548.3 billion RMB (23.1%). To foster foreign trade, innovation, and internationalization, the State Council expanded comprehensive pilot zones to 165 from 2015 to 2022. The rapid global development of online trade has positioned cross-border e-commerce as a crucial infrastructure for foreign trade, with significant impacts on agriculture, manufacturing, and services. Leveraging new digital technologies effectively addresses supply–demand matching issues in the cross-border e-commerce supply chain. Intelligent decision-making in product development empowers upstream manufacturers to penetrate overseas markets and offers integrated international sales services, enhancing big data accumulation and an understanding of market demands. Downstream, intelligent decision-making aids distributors by identifying consumer needs and optimizing product selection and stocking, thus integrating global resources in manufacturing, logistics, marketing, and services and resolving issues from production to post-sale.

Predicting customer satisfaction can better serve downstream distributors by accurately identifying consumer needs based on the accumulation of big data in cross-border e-commerce. This capability assists cross-border e-commerce sellers in selecting and stocking products, facilitating the integration of global resources in manufacturing, logistics, marketing, and services and effectively addressing a series of issues from production to post-sale service [1]. Traditional satisfaction prediction requires the establishment of an indicator system and surveys consumer subjective evaluations through questionnaires. This approach is costly and time-consuming, and its accuracy depends on the design of the questionnaire and the precision of the survey sample [2]. E-commerce platforms typically rely on customer satisfaction ratings post-purchase, which is not applicable to products that are still in the testing phase or are newly launched with few consumers. New products often lack historical data, making the use of multimodal big data—such as product positioning and attributes—as feature variables for decision-making particularly suitable. This approach is not only faster and more cost-effective but also retains more sample information, enhancing decision-making capabilities. This study integrates multiple attributes and complex features of cross-border e-commerce sales data to develop a multimodal learning model that combines product and market attributes. This model offers a more comprehensive, precise, and in-depth understanding of consumer behavior, habits, preferences, and characteristics [3]. It rapidly collects extensive information on apparel features, enabling brands to accurately assess which products are suitable for market expansion and estimate consumer satisfaction levels. The model assists cross-border e-commerce operators in making well-informed decisions regarding product listings, selection, and development, allowing them to estimate sales and profits, optimize customer experiences, and support the internationalization and intelligent growth of cross-border e-commerce operations [4].

2. Literature Review

2.1. Research on Multimodal Learning

Multimodal Machine Learning (MMML) involves creating models that enable machines to learn from various modalities and facilitate information exchange across these modalities [5]. Multimodal learning has demonstrated effective applications across various fields such as education, healthcare, smart hardware, and gaming, showcasing strong potential for development [6]. In the education sector, multimodal technology can offer students richer learning resources and more interactive experiences [7,8]. In the healthcare industry, the application of multimodal techniques—combining image recognition, speech recognition, and natural language processing—enables the intelligent analysis and interpretation of medical imaging data, assisting doctors in making accurate diagnoses. In smart hardware, multimodal functions enhance devices’ perception and interaction capabilities; by integrating voice and image recognition technologies, these devices can more accurately understand user commands and offer a broader range of functionalities [9]. In the gaming industry, the combination of image recognition, speech recognition, and gesture recognition technologies creates more immersive virtual reality gaming experiences. Additionally, multimodal functionalities enable richer character expressions and action interactions, thereby enhancing the enjoyment and interactivity of games [10]. From the perspective of modality fusion, it includes data fusion and feature fusion. Feature fusion methods train a model for each modality, make decisions, and then use an attention mechanism to merge these decisions into a final comprehensive decision [11]. Data fusion directly links feature data from different modalities, using the fused data for model training.

2.2. Data Empowerment and Intelligent Marketing

Data empowerment is crucial for the digital transformation of enterprises [12]. Advances in data technology and analytical techniques have significantly enhanced data empowerment [13,14]. Intelligent sales link the supply chain ends, enabling differentiated demand mining at the front end and personalized production at the back end [15]. The apparel supply chain, characterized by rapid changes, short cycles, and flexibility, benefits from the integration of artificial intelligence, which offers innovative management models for the fashion supply chain system [16]. Leveraging business big data technology and machine learning algorithms can significantly enhance supply chain intelligence [17]. Artificial intelligence drives supply chain transformation in areas such as platform reconstruction, ecosystem reshaping, and advantage rebuilding [18,19].

Due to market uncertainty and intense competition, enterprises are compelled to try various strategies to improve performance [20]. Business intelligence helps companies quickly generate insights, guiding decision-makers to improve operational efficiency, seek new opportunities, and stand out from the competition [1,21,22]. E-commerce facilitates resource sharing, coordination, and optimized allocation, promoting intelligent marketing [23]. Intelligent marketing requires integrating networking, digital, and intelligent technologies for deep supply chain cooperation [24]. According to Senyo et al. [25], digital innovation fundamentally alters cooperation and competition among enterprises, focusing on building digital business ecosystems and collaboration networks. Innovations in digital business models and open organizational structures promote global innovation networks, better meeting customer needs and enhancing innovation capabilities [26]. Digital intelligent technologies drive supply chain reform from supply and demand perspectives [27,28]. Intelligent marketing expands resource allocation breadth and depth, creating competitive advantages at different product lifecycle stages [29].

2.3. Intelligent Forecasting in Cross-Border E-Commerce

With the rapid development of cross-border e-commerce, sales enterprises aim to accurately predict sales performance and reasonably develop products to achieve greater profits, attract more investment, and provide a better customer experience. When shopping online, users often communicate their needs to customer service through various modalities, including text, images, and videos, resulting in a significant amount of unstructured data [30]. Multimodal data, such as text and images, play a crucial role in e-commerce customer service [31]. However, traditional unimodal methods can only capture information from a single dimension, often failing to fully reflect the complexity of customer behavior. This limitation makes multimodality a significant challenge for e-commerce customer service systems [32]. Bi et al. [33] explored e-commerce product classification using a multimodal late fusion approach based on text and image modalities. Their research demonstrated that the proposed method outperformed traditional unimodal methods in multimodal product classification tasks. Cai et al. [34] proposed a spatial feature fusion and grouping strategy based on multimodal data and developed a neural network model for predicting e-commerce product demand. The experimental results confirmed the superiority and effectiveness of the proposed algorithm. Xu et al. [35] designed a multimodal analysis framework to predict product return rates in live-streaming e-commerce. Experiments using real-world data from Taobao Live demonstrated that multimodal signals from products and anchors effectively predict return rates. Wróblewska et al. [36] developed a machine learning-based recommendation system that supports the fusion of various data representations through multimodal methods. Their research showed that this system outperformed state-of-the-art techniques on open datasets. Xu et al. [37] designed a multimodal analysis framework for predicting product sales in live-streaming e-commerce and explored the impact of anchor reputation on sales. Experiments using real-world data from Douyin Live demonstrated the effectiveness of the constructed multimodal anchor reputation signals in predicting product sales. To address the challenges of sentiment and emotion modeling posed by unstructured big data with different modalities, Seng and Ang proposed a new architecture for multimodal sentiment and emotion modeling, which was validated for its performance [38]. Shoumy et al. highlighted that a new architecture combining different modalities can achieve more complex and accurate sentiment analyses [39]. These studies indicate that multimodal deep learning techniques in e-commerce demonstrate higher predictive accuracy and model robustness when dealing with heterogeneous data. Utilizing multimodal data to predict customer satisfaction not only improves prediction accuracy but also enhances the model’s adaptability to different customer groups [40].

Although deep learning has made significant progress in intelligent recognition and decision-making, especially in image processing, its application in management theory and practice has lagged behind. The globalization of cross-border e-commerce markets and the digitization of operations offer ample opportunities for the development of artificial intelligence and big data in management decision-making. Traditional methods, which do not account for the complex attributes of sales products and market characteristics, often result in time-consuming, labor-intensive, and insufficiently accurate predictions. Current research on intelligent decision-making in product development encompasses aspects such as customer value co-creation, dynamic capability evolution, supply chain collaboration, value chain enhancement, and open innovation [41]. However, there is limited focus on specific technologies for intelligent development, data analysis, and deployment implementation. Research methods primarily rely on traditional case analysis, econometric analysis, and structural equation modeling [2,42], elaborating on definitions, influencing factors, and significance. Basic theories and key technologies of artificial intelligence in intelligent decision-making for product development are rarely addressed. E-commerce market positioning and product attributes play a crucial role in consumer satisfaction, a point that intelligent marketing research has yet to fully consider. Introducing multimodal learning into the field of intelligent sales in cross-border e-commerce is highly beneficial for improving the accuracy of intelligent decision-making [43]. Research on intelligent marketing must also engage in interdisciplinary studies to promote the dual development of theoretical exploration and practical application.

3. Technical Construction

3.1. Data Collection and Processing

Taking the dress category in the cross-border e-commerce market as an example, data from the product detail pages of 862 dresses on the Amazon platform were collected. The data include variables such as Style, Price, Size, Season, Waistline, Neckline, Sleeve Length, Material, Fabric Type, Decoration, Pattern Type, and Rating. Among these, Style, Price, Size, Season, and Waistline are market positioning feature variables, while Neckline, Sleeve Length, Material, Fabric Type, Decoration, and Pattern Type are product attribute feature variables. Rating is used as the label variable. These 12 variables were further transformed into feature values, as shown in

Table 1. Variables and feature values.

Variable Name	Feature Value
Style	1—Bohemian; 2—brief; 3—casual; 4—cute; 5—fashion; 6—flare; 7—novelty; 8—OL party; 9—sexy; 10—vintage; 11—work
Price	1—low; 2—average; 3—medium; 4—high; 5—very high
Size	1—small; 2—S; 3—M; 4—L; 5—XL; 6—free
Season	1—autumn; 2—winter; 3—spring; 4—summer
Waistline	1—dropped; 2—empire; 3—natural; 4—princess; 5—null
Neckline	1—O-neck; 2—backless; 3—boat-neck; 4—bowneck; 5—halter; 6—mandarin collar; 7—open; 8—Peter Pan collar; 9—ruffled; 10—scoop; 11—slash-neck; 12—square collar; 13—sweetheart; 14—turndown collar; 15—V-neck
Sleeve Length	1—full; 2—half sleeve; 3—butterfly; 4—sleeveless; 5—short; 6—three-quarter; 7—turndown; 8—cap sleeves
Material	1—cotton; 2—wool; 3—microfiber; 4—polyester; 5—silk; 6—chiffon fabric; 7—nylon; 8—linen; 9—rayon; 10—Lycra; 11—milk silk; 12—acrylic; 13—spandex; 14—mix; 15—cashmere; 16—knitted; 17—chiffon; 18—viscose; 19—lace; 20—modal; 21—other
Fabric Type	1—chiffon; 2—broadcloth; 3—jersey; 4—batik; 5—worsted; 6—woolen; 7—satin; 8—flannel; 9—poplin; 10—dobby; 11—knitting; 12—flannel; 13—tulle; 14—satin; 15—organza; 16—lace; 17—corduroy; 18—terry; 19—none
Decoration	1—ruffles; 2—embroidery; 3—bow; 4—lace; 5—beading; 6—sashes; 7—hollow out; 8—pockets; 9—sequin; 10—applique; 11—button; 12—tiered; 13—rivet; 14—feathers; 15—flowers; 16—pearls; 17—pleat; 18—crystal; 19—ruched; 20—draped; 21—tassels; 22—plain; 23—cascading; 24—none
Pattern Type	1—animal; 2—print; 3—dot; 4—solid; 5—patchwork; 6—striped; 7—geometric; 8—plaid; 9—leopard; 10—floral; 11—character; 12—splice; 13—leopard; 14—none
Rating	1~5

.

The feature values in the dataset vary across different ranges, resulting in certain discrepancies. Before training, the feature values were standardized using the formula (X-mean)/std to ensure that all data are distributed with a variance of 1 within the range of −1 to 1. The holdout method was employed for data splitting, dividing the 862 records into two mutually exclusive sets: 80% of the data (689 records) was used for training, and 20% (173 records) was used for testing.

3.2. Unimodal Learning Model

The market and positioning of Modal I include data such as Style, Price, Size, Season, Waistline, and Rating. A deep neural network constructs a fully connected layer (dense_1) as the input layer for this modal, using the ReLU activation function to transform the 12-dimensional input into a 128-dimensional output. This layer generates 768 parameters to be estimated. To prevent overfitting, a dropout layer (dropout_1) is added next, randomly disconnecting 20% of the input neurons’ connections during each parameter update. Another fully connected layer (dense_2) is then established, using the ReLU activation function to convert the 128-dimensional input into a 32-dimensional output, generating 4218 parameters to be estimated. Finally, a fully connected layer (dense_3) is constructed, applying the ReLU function to transform the 32-dimensional input into a 1-dimensional output $Y^{\prime }$, as shown in

Table 2. Deep neural network structure of Modal I.

Layer (Type)	Output Shape	Param
dense_1 (Dense)	(None, 128)	768
activation_1 (Activation)	(None, 128)	0
dropout_1 (Dropout)	(None, 128)	0
dense_2 (Dense)	(None, 32)	4128
activation_2 (Activation)	(None, 32)	0
dense_3 (Dense)	(None, 1)	33
activation_3 (Activation)	(None, 1)	0
Total params: 4929 Trainable params: 4929 Non-trainable params: 0

.

The product attributes of Modal II include data such as Neckline, Sleeve Length, Material, Fabric Type, Decoration, Pattern Type, and Rating. A deep neural network is constructed for Modal II as follows:

Input Layer (dense_1): Uses the ReLU activation function to transform the 12-dimensional input into a 128-dimensional output, generating 896 parameters to be estimated.

Dropout Layer (dropout_1): Randomly disconnects 20% of the input neurons’ connections during training to prevent overfitting.

Hidden Layer (dense_2): Uses the ReLU activation function to convert the 128-dimensional input into a 32-dimensional output, generating 4218 parameters to be estimated.

Output Layer (dense_3): Uses the ReLU function to transform the 32-dimensional input into a 1-dimensional output $Y^{\prime }$, as shown in

Table 3. Deep neural network structure of Modal II.

Layer (Type)	Output Shape	Param
dense_1 (Dense)	(None, 128)	896
activation_1 (Activation)	(None, 128)	0
dropout_1 (Dropout)	(None, 128)	0
dense_2 (Dense)	(None, 32)	4128
activation_2 (Activation)	(None, 32)	0
dense_3 (Dense)	(None, 1)	33
activation_3 (Activation)	(None, 1)	0
Total params: 5070 Trainable params: 5070 Non-trainable params: 0

.

3.3. Multimodal Learning Model

A tensor is a higher-order extension of vectors and matrices, where the dimensions determine the tensor’s order. Tensor fusion networks can effectively integrate interaction information correlated between different modalities, preserving the original data to the maximum extent. This improves the recognition and prediction accuracy of multimodal data.

The market positioning information is input as $P={\left\{p_n\right\}}_{n=1}^N$ and the product attributes are input as $A={\left\{a_n\right\}}_{n=1}^N$. These two feature vectors are encoded and merged into the same space, forming the tensor fusion input: $M={\left\{{p_n,a}_{n,}\right\}}_{n=1}^N$. The multimodal fusion tensor, formed based on the tensor product, undergoes intelligent learning and training. The model is shown in Figure 1.

The neural network structure and parameters for multimodal learning are shown in

Table 4. Multilayer deep neural network structure.

Layer (Type)	Output Shape	Param
dense_1 (Dense)	(None, 128)	1536
activation_1 (Activation)	(None, 128)	0
dropout_1 (Dropout)	(None, 128)	0
dense_2 (Dense)	(None, 32)	4128
activation_2 (Activation)	(None, 32)	0
dense_3 (Dense)	(None, 1)	33
activation_3 (Activation)	(None, 1)	0
Total params:5697 Trainable params: 5697 Non-trainable params: 0

.

4. Analysis and Testing

Before machine learning, data preprocessing ensures that the data structure, features, quality, attributes, and distribution meet the standard requirements. Modal I and Modal II each use 689 data entries for training and 173 for testing.

In deep learning, RMSProp and Adam are high-performance optimizers. RMSProp (Root Mean Square Propagation) introduces a decay coefficient to reduce oscillations in gradient descent, improving convergence speed and stability. Adam (Adaptive Moment Estimation) combines momentum gradient descent and adaptive learning rate optimization. It uses exponential moving averages of gradients, computing first and second moment estimates, and designs adaptive learning rates for different parameters.

This study uses the RMSProp and Adam algorithms for deep learning and federated learning, performing a comparative analysis of the training and testing results.

4.1. Market and Positioning Modal Learning

Modal I was trained for 500 epochs using the Keras framework and the Adam optimizer. The results are shown in

Table 5. Learning results of Modal I using Adam.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 418 us/step	-loss: 7.6809	-mae: 2.4985
Epoch 2/500	689/689 [=============]	−0 s 110 us/step	-loss: 4.6210	-mae: 1.6709
Epoch 3/500	689/689 [=============]	−0 s 116 us/step	-loss: 4.4499	-mae: 1.7674
Epoch 4/500	689/689 [=============]	−0 s 110 us/step	-loss: 4.1976	-mae: 1.7066
Epoch 5/500	689/689 [=============]	−0 s 99 us/step	-loss: 4.1244	-mae: 1.6571
……
Epoch 496/500	689/689 [=============]	−0 s 115 us/step	-loss: 2.6092	-mae: 1.2116
Epoch 497/500	689/689 [=============]	−0 s 106 us/step	-loss: 2.6684	-mae: 1.1917
Epoch 498/500	689/689 [=============]	−0 s 104 us/step	-loss: 2.6892	-mae: 1.2178
Epoch 499/500	689/689 [=============]	−0 s 116 us/step	-loss: 2.5525	-mae: 1.2200
Epoch 500/500	689/689 [=============]	−0 s 116 us/step	-loss: 2.6202	-mae: 1.2045
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 219 us/step	-loss: 3.9180	-mae: 1.4277

. The training loss decreased from 7.6809 to 2.2602, and the training accuracy (mean absolute error) improved from 2.4985 to 1.2045. The test loss was 3.9180 and the test accuracy was 1.4277, indicating a moderate learning effect.

Using the Keras learning framework and the RMSProp optimizer with a learning rate of 0.001, Modal I was trained for 500 epochs. The results are shown in

Table 6. Learning results of Modal I using RMSProp.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 238 us/step	-loss: 6.2128	-mae: 2.2700
Epoch 2/500	689/689 [=============]	−0 s 93 us/step	-loss: 4.6579	-mae: 1.8378
Epoch 3/500	689/689 [=============]	−0 s 105 us/step	-loss: 4.3028	-mae: 1.7373
Epoch 4/500	689/689 [=============]	−0 s 105 us/step	-loss: 4.3562	-mae: 1.7212
Epoch 5/500	689/689 [=============]	−0 s 104 us/step	-loss: 4.1525	-mae: 1.6840
……
Epoch 496/500	689/689 [=============]	−0 s 99 us/step	-loss: 2.7705	-mae: 1.2560
Epoch 497/500	689/689 [=============]	−0 s 93 us/step	-loss: 2.7571	-mae: 1.2304
Epoch 498/500	689/689 [=============]	−0 s 93 us/step	-loss: 2.7844	-mae: 1.2510
Epoch 499/500	689/689 [=============]	−0 s 87 us/step	-loss: 2.7852	-mae: 1.2515
Epoch 500/500	689/689 [=============]	−0 s 81 us/step	-loss: 2.7196	-mae: 1.2271
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 167 us/step	-loss: 4.1295	-mae: 1.4984

. The training loss decreased from 6.2182 to 2.7196, and the training accuracy (mean absolute error) improved from 2.2700 to 1.2271. The test loss was 4.1295 and the test accuracy was 1.4984, indicating poor learning performance.

4.2. Product Attribute Modal Learning

Modal II was trained for 500 epochs using the Keras learning framework and the Adam optimizer. The results are shown in

Table 7. Learning results of Modal II using Adam.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 217 us/step	-loss: 8.8630	-mae: 2.4872
Epoch 2/500	689/689 [=============]	−0 s 59 us/step	-loss: 4.7590	-mae: 1.8383
Epoch 3/500	689/689 [=============]	−0 s 56 us/step	-loss: 4.4530	-mae: 1.7244
Epoch 4/500	689/689 [=============]	−0 s 57 us/step	-loss: 4.2655	-mae: 1.7089
Epoch 5/500	689/689 [=============]	−0 s 57 us/step	-loss: 4.1099	-mae: 1.6387
……
Epoch 496/500	689/689 [=============]	−0 s 48 us/step	-loss: 1.4138	-mae: 0.8143
Epoch 497/500	689/689 [=============]	−0 s 52 us/step	-loss: 1.4424	-mae: 0.8234
Epoch 498/500	689/689 [=============]	−0 s 50 us/step	-loss: 1.4560	-mae: 0.8057
Epoch 499/500	689/689 [=============]	−0 s 52 us/step	-loss: 1.4690	-mae: 0.8216
Epoch 500/500	689/689 [=============]	−0 s 49 us/step	-loss: 1.4559	-mae: 0.8144
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 294 us/step	-loss: 4.1140	-mae: 1.4259

. The training loss decreased from 8.8630 to 1.4559, and the training accuracy (mean absolute error) improved from 2.4872 to 0.8144. The test loss was 4.1140 and the test accuracy was 1.4259, indicating that the learning performance was similar to that of Modal I.

Using the Keras learning framework and the RMSProp optimizer with a learning rate of 0.001, Modal II was trained for 500 epochs. The results are shown in

Table 8. Learning results of Modal II using RMSProp.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 164 us/step	-loss: 6.4326	-mae: 2.2166
Epoch 2/500	689/689 [=============]	−0 s 50 us/step	-loss: 4.9162	-mae: 1.8606
Epoch 3/500	689/689 [=============]	−0 s 55 us/step	-loss: 4.5942	-mae: 1.7759
Epoch 4/500	689/689 [=============]	−0 s 56 us/step	-loss: 4.4481	-mae: 1.7342
Epoch 5/500	689/689 [=============]	−0 s 49 us/step	-loss: 4.2125	-mae: 1.6909
……
Epoch 496/500	689/689 [=============]	−0 s 45 us/step	-loss: 1.8812	-mae: 0.9679
Epoch 497/500	689/689 [=============]	−0 s 39 us/step	-loss: 1.9394	-mae: 0.9694
Epoch 498/500	689/689 [=============]	−0 s 38 us/step	-loss: 1.8735	-mae: 0.9542
Epoch 499/500	689/689 [=============]	−0 s 43 us/step	-loss: 1.7950	-mae: 0.9267
Epoch 500/500	689/689 [=============]	−0 s 39 us/step	-loss: 1.8072	-mae: 0.9188
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 173 us/step	-loss: 4.2477	-mae: 1.5463

. The training loss decreased from 6.4326 to 1.8072, and the training accuracy (mean absolute error) improved from 2.2166 to 0.9188. The test loss was 4.4277 and the test accuracy was 1.5643, indicating poor learning performance.

4.3. Multimodal Learning

The multimodal model was trained for 500 epochs using the Keras learning framework and the Adam optimizer. The results are shown in

Table 9. Learning results of multimodal model using Adam.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 210 us/step	-loss: 8.8611	-mae: 2.4433
Epoch 2/500	689/689 [=============]	−0 s 58 us/step	-loss: 4.5633	-mae: 1.7869
Epoch 3/500	689/689 [=============]	−0 s 65 us/step	-loss: 4.4982	-mae: 1.7732
Epoch 4/500	689/689 [=============]	−0 s 53 us/step	-loss: 4.1600	-mae: 1.6923
Epoch 5/500	689/689 [=============]	−0 s 56 us/step	-loss: 4.0209	-mae: 1.6389
……
Epoch 496/500	689/689 [=============]	−0 s 46 us/step	-loss: 0.4467	-mae: 0.3770
Epoch 497/500	689/689 [=============]	−0 s 59 us/step	-loss: 0.6286	-mae: 0.4305
Epoch 498/500	689/689 [=============]	−0 s 51 us/step	-loss: 0.5423	-mae: 0.4168
Epoch 499/500	689/689 [=============]	−0 s 69 us/step	-loss: 0.4598	-mae: 0.3890
Epoch 500/500	689/689 [=============]	−0 s 67 us/step	-loss: 0.5282	-mae: 0.4083
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 190 us/step	-loss: 3.3166	-mae: 1.0464

. The training loss decreased from 8.8611 to 0.5283, and the training accuracy (mean absolute error) improved from 2.4433 to 0.4083. The test loss was 3.3166 and the test accuracy was 1.0464, indicating a significant improvement in learning performance compared to the unimodal models.

Using the Keras learning framework and the RMSProp optimizer with a learning rate of 0.001, the multimodal model was trained for 500 epochs. The results are shown in

Table 10. Learning results of multimodal model using RMSProp.

Training Epochs		Training Time	Training Loss (MSE) Mean Squared Error	Training Accuracy (MAE) Mean Absolute Error
Epoch 1/500	689/689 [=============]	−0 s 138 us/step	-loss: 5.8074	-mae: 2.0678
Epoch 2/500	689/689 [=============]	−0 s 52 us/step	-loss: 4.7239	-mae: 1.8163
Epoch 3/500	689/689 [=============]	−0 s 53 us/step	-loss: 4.2364	-mae: 1.7059
Epoch 4/500	689/689 [=============]	−0 s 56 us/step	-loss: 4.1844	-mae: 1.6532
Epoch 5/500	689/689 [=============]	−0 s 55 us/step	-loss: 4.1077	-mae: 1.6797
……
Epoch 496/500	689/689 [=============]	−0 s 42 us/step	-loss: 0.6323	-mae: 0.5014
Epoch 497/500	689/689 [=============]	−0 s 43 us/step	-loss: 0.5574	-mae: 0.4812
Epoch 498/500	689/689 [=============]	−0 s 42 us/step	-loss: 0.5874	-mae: 0.4866
Epoch 499/500	689/689 [=============]	−0 s 44 us/step	-loss: 0.5736	-mae: 0.4876
Epoch 500/500	689/689 [=============]	−0 s 43 us/step	-loss: 0.5500	-mae: 0.4818
Test Samples		Testing Time	Test Loss	Test Accuracy
173/173 [========================]		−0 s 271 us/step	-loss: 3.3702	-mae: 1.0887

. The training loss decreased from 5.8074 to 0.5500, and the training accuracy (mean absolute error) improved from 2.0678 to 0.4818. The test loss was 3.3702 and the test accuracy was 1.0887, indicating good learning performance.

5. Conclusions and Recommendations

5.1. Conclusions

The training loss, training accuracy, test loss, and test accuracy for the market positioning modality using Adam optimization, market positioning modality using RMSProp optimization, product attribute modality using Adam optimization, product attribute modality using RMSProp optimization, multimodal Adam optimization, and multimodal RMSProp optimization are shown in

Table 11. Learning performance of various modalities.

	Training Loss (MSE)	Training Accuracy (MAE)	Test Loss (MSE)	Test Accuracy (MAE)
Market Positioning Modality with Adam Optimization	2.6202	1.2045	3.9180	1.4277
Market Positioning Modality with RMSProp Optimization	2.7196	1.2271	4.1295	1.4984
Product Attribute Modality with Adam Optimization	1.4559	0.8144	4.1140	1.4259
Product Attribute Modality with RMSProp Optimization	1.8072	0.9188	4.2477	1.5463
Multimodal Adam Optimization	0.5282	0.4083	3.3166	1.0464
Multimodal RMSProp Optimization	0.5500	0.4818	3.3702	1.0887

.

In terms of training loss, the multimodal Adam optimization has the smallest value at 0.5282. Regarding training accuracy, the multimodal Adam optimization is the highest, with a mean absolute error of 0.4083. For test loss, the multimodal Adam optimization is the lowest, at 3.3166. In terms of test accuracy, the multimodal Adam optimization is the highest, with a mean absolute error of 1.0464, as shown in Figure 2. Overall, multimodal learning demonstrates higher accuracy and lower loss compared to single-modal learning. The multimodal satisfaction prediction using the Adam optimizer performs better than when using the RMSProp optimizer.

5.2. Applications and Development Recommendations

5.2.1. Building a Big Data-Based AI E-Commerce Decision Support System

Promote innovation in e-commerce technology architecture and platforms by designing modular technology architectures that support plug-in technology upgrades. By introducing deep machine learning and data fusion methods, the decision support system can flexibly integrate the latest AI algorithms and data processing technologies, comprehensively considering intra-modal and inter-modal dependencies to address evolving market demands and technological changes. Deeply integrate cross-functional data flows to promote the integration and fusion of data across departments such as marketing, sales, customer service, and supply chain management within enterprises. This enhances multidimensional data analysis capabilities and facilitates information sharing and strategic collaboration between different departments. Through centralized management and analysis of multi-departmental data, the decision support system can gain a more comprehensive understanding of business processes and consumer behavior, leading to the generation of more accurate business insights.

Continuously develop dynamic optimization algorithms that automatically adjust parameters based on real-time market data, optimizing the decision-making process. By leveraging reinforcement learning and online learning technologies, the system can continuously learn and improve during transactions, achieving optimal marketing strategies and inventory management. Promote the practical application of advanced analytics and predictive models. Utilizing time series analysis, sentiment analysis, and complex event processing, companies can gain deep insights into market dynamics and consumer psychology, identify subtle market changes, and predict their potential impact on sales and brand loyalty, enabling proactive responses ahead of competitors.

Enhance the interpretability of decision models to ensure that all stakeholders can understand the basis of the model’s decisions. This helps build trust among internal users and maintains transparency with external regulators, especially when using complex algorithms like deep learning. Establish frameworks for monitoring and auditing AI-driven decisions to ensure transparency and compliance, thereby strengthening trust among stakeholders and complying with data regulations. These strategies enable e-commerce companies to develop efficient, transparent, and compliant big data-driven AI decision-making systems, enhancing responsiveness, accuracy, and competitiveness in the global market.

5.2.2. Develop a Multimodal Data Prediction Platform Integrating Geolocation and Product Characteristics

Build a multimodal prediction system based on geolocation and product attributes to analyze and forecast product demands and user preferences in different countries and regions. Establish a centralized cross-border e-commerce data management platform that integrates multiple data sources, including geographical information, product specifications, consumer interactions, and historical purchase records.

Utilize big data technologies like Hadoop or Spark to process and analyze large-scale datasets. Implement data quality control measures to ensure data accuracy and consistency, supporting subsequent analyses. Use NLP and image recognition to handle text and visual data, extracting key information for prediction models. Utilize GIS technology to analyze consumer distribution and market characteristics, integrating geolocation as a core dimension in model data. Develop and train hybrid neural network models to identify regional differences in product preferences. Use machine learning algorithms to process structured (product specifications, user geolocation) and unstructured data (user reviews, social media content, images). For instance, text analysis can interpret review sentiments, while image recognition assesses the impact of product images on decisions. This approach builds comprehensive user profiles and purchasing behavior models, allowing for tailored product recommendations and customized assistance.

Regularly review and adjust model parameters to maintain prediction accuracy and relevance, particularly when entering new markets or responding to emerging consumer trends. Establish a real-time feedback mechanism to translate predictions into actionable strategies, enabling swift responses from marketing and sales teams. Implement automated business-intelligence dashboards to monitor key performance indicators (KPIs) and provide instant data viewing for rapid decision-making. Adopt a continuous learning strategy using new data to constantly train and improve models, ensuring alignment with market dynamics. Ensure the system’s scalability to expand functionalities and data processing as the business grows. These strategies will provide deep insights into consumer behavior and market trends, enhancing the business’s responsiveness and accuracy in adapting to market changes, thus maintaining a competitive edge.

5.2.3. Implement Big Data Analytics-Based E-Commerce Precision Marketing Optimization Strategies

Utilize big data analytics and multimodal learning technologies to base decisions and actions on precise, real-time data insights. Use multimodal learning models to deeply analyze customer data on the cross-border e-commerce platform. Segment customers based on behavior patterns, purchase history, and social media activities to uncover unique needs and preferences, supporting customized marketing strategies. Develop and deploy personalized recommendation systems by combining users’ purchase histories and browsing behaviors. Implement machine learning algorithms such as collaborative filtering and content-based recommendation systems to improve the relevance and accuracy of the recommendations.

Evaluate the effectiveness of different recommendation models through A/B testing to select the optimal model for comprehensive deployment. Implement dynamic pricing strategies and intelligent promotional activities by developing demand-based dynamic pricing models. Use regression analysis and machine learning to predict price sensitivity and adjust prices according to market supply and demand. Analyze the impact of promotional activities on different customer groups, customizing targeted promotions based on historical data and consumer behavior patterns. Additionally, use big data analytics to optimize the timing and content of promotional activities, ensuring that marketing efforts reach the most interested customer segments directly.

Integrate cross-channel marketing efforts by combining multimodal data analysis to unify online and offline marketing channels, ensuring consistency in brand messaging and marketing activities across all touchpoints. Use cross-channel tracking tools to monitor and analyze the performance of marketing campaigns, and adjust strategies in real time to maximize return on investment. Establish a real-time feedback mechanism to continuously monitor the effectiveness of marketing activities and customer feedback, allowing for ongoing adjustments and optimization of marketing strategies to stay aligned with market trends and consumer expectations.

By implementing precision marketing strategies, cross-border e-commerce enterprises can more effectively meet the diverse needs of global consumers, enhance customer satisfaction, and strengthen market competitiveness.

5.2.4. Develop End-to-End Cross-Border E-Commerce Supply Chain Optimization Solutions

Enhance supply chain data transparency by building an integrated supply chain management system. Collect and analyze data in real-time across the entire supply chain, from suppliers to end consumers, creating a continuously updated and stable big data set. Utilize big data technologies and multimodal learning models to provide deep insights, predicting market trends and adjusting production plans accordingly. Gain a comprehensive understanding of demand characteristics in different markets and customer groups. Use predictive analytics to optimize inventory levels and implement precise inventory management strategies. Employ efficient logistics planning to reduce delivery times and costs. Dynamically adjust the supply chain to build a flexible network, adapting quickly to real-time data and market demands. For example, if there is a surge in demand in a specific region, the system can automatically adjust production priorities and logistics resources to ensure the timely fulfillment of demand.

Utilize big data-driven analytical tools to assess and categorize potential risks within the supply chain. By constructing a comprehensive risk-management framework, implement effective risk prevention and mitigation measures to ensure the stability and sustainability of the supply chain. Enhance customer engagement and feedback mechanisms by using multimodal data analysis to continuously understand customer behavior and feedback. This ongoing analysis helps optimize supply chain operations, ultimately improving customer satisfaction. Establish a customer feedback system that allows consumers to directly influence supply chain decisions, such as affecting inventory and production through their reviews and feedback.

By implementing these strategies, cross-border e-commerce enterprises can build a highly optimized and flexible supply chain system that quickly adapts to market changes and significantly enhances customer satisfaction, thereby gaining a competitive advantage in the global market. This end-to-end supply chain optimization solution will leverage big data and artificial intelligence technologies to revolutionize supply chain management and drive continuous business growth.

6. Contributions and Further Directions

This study can be expanded from multiple perspectives. First, by optimizing the multimodal learning framework, considering the advantages of multimodal learning in predicting customer satisfaction, the feature extraction process within each modality can be further refined. For example, deep convolutional neural networks (CNNs) can be introduced for image feature extraction, and Transformer-based models can be used for text analysis. Exploring more complex model fusion techniques, such as hierarchical fusion or feature-level fusion, can improve the efficiency of integrating signals from different data sources, thereby enhancing the overall performance of the model.

Second, further testing and optimization of the optimizer selection is necessary. The results of this study indicate that the Adam optimizer performs well in multimodal learning. Systematic testing of a broader range of optimizers, including some recently proposed adaptive learning rate optimizers, such as AdaBelief, LAMB, or Lookahead, can be conducted. Experimenting with different combinations of learning rates and batch sizes will help identify the best configuration for the current dataset. Additionally, using learning rate decay or cyclical learning rate adjustment strategies can further enhance the model’s performance.

Third, expanding the breadth and depth of the research by applying the current model to other types of cross-border e-commerce products, such as electronics or home goods, will verify the model’s generality and robustness. By integrating model interpretation tools and techniques, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), the research can provide deeper insights, helping merchants understand the factors that most significantly affect customer satisfaction.