大数据
计算机科学
顾客满意度
人工智能
多式联运
芯(光纤)
机器学习
产品(数学)
深度学习
数据科学
数据挖掘
营销
业务
数学
电信
几何学
作者
Xiaodong Zhang,Chunrong Guo
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2024-09-11
卷期号:14 (18): 8181-8181
被引量:1
摘要
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.
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