# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 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. **Author Contributions:** Conceptualization, X.Z. and C.G. ; methodology, X.Z. ; software, X.Z. ; validation, X.Z. ; formal analysis, X.Z. and C.G. ; investigation, X.Z. ; data curation, X.Z. ; writing--original draft preparation, X.Z. and C.G. ; writing--review and editing, X.Z. and C.G. ; visualization, X.Z. and C.G. ; supervision, X.Z. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Inner Mongolia Natural Science Foundation, \"Research on Intelligent Marketing of Cross-Border E-commerce with Multimodal Learning and Federated Learning Collaborative Embedding\" (Project Number: 2024 MS/07009); Interdisciplinary Research Fund of Inner Mongolia Agricultural University, \"Research on Open Innovation Intelligent Decision-making in E-commerce Based on Federated Learning\" (Project No. BR231518); Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region, \"Research on Intelligent Marketing in E-commerce Based on Multimodal Learning\" (Project No. NJ)T24014); National Key R&D Program of China, \"Research on Sino-Mongolian Agricultural and Pastoral Supply Chain Collaboration\" (Project Number: 2021 YFE0190200); National Social Science Fund of China Post-funding Project, \"Research on the Internationalization Development of Chinese Cross-border E-commerce Brands\" (Project Number: 20 FGLB033); China Society of Logistics and China Federation of Logistics & Purchasing General Research Project, \"Research on the Operation of Agricultural and Animal Husbandry Supply Chain between China and Mongolia under the Digital Trade Environment\" (Project Number: 2024 CSLKT3-022); Inner Mongolia Autonomous Region Graduate Education Teaching Reform Project, \"Research on the Training Model for New Business Graduates in Inner Mongolia under the Background of Digital Economy\" (Project Number: JJCG2022059). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used in this article are available upon request from the corresponding author.