# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
## 2 Literature Review
### 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]. [PERSON] 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. [PERSON] 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. [PERSON] 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. [PERSON] 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. [PERSON] 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, [PERSON] and [PERSON] proposed a new architecture for multimodal sentiment and emotion modeling, which was validated for its performance [38]. [PERSON] 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. Utilizingmultimodal 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.