Abstract
This article investigates the application of ML (Machine Learning) in multiple key areas of cross-border e-commerce, aiming to optimize user experience and improve business efficiency. The research covers core areas such as demand forecasting, inventory management, personalized recommendation systems, and fraud detection. In terms of demand forecasting, the author conducted experiments to compare the performance of different algorithms and found that the neural network model performs excellently on sales data, with higher prediction accuracy, and is expected to improve the efficiency of inventory management and customer satisfaction. Finally, research on fraud detection has shown that the Isolation Forest algorithm performs well in identifying potential fraudulent behavior, with high accuracy and recall, and can effectively protect the rights of merchants and customers. These experimental results provide strong guidance and decision support for cross-border e-commerce enterprises, helping them improve efficiency, reduce costs, and improve user experience in highly competitive markets. The application of ML has brought new opportunities for cross-border e-commerce and is expected to further promote innovation and development in this field.
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Huang, X., Yu, Y. (2024). Optimizing Cross Border E-commerce User Experience Through Machine Learning. In: Gupta, R., Bartolucci, F., Katsikis, V.N., Patnaik, S. (eds) Recent Advancements in Computational Finance and Business Analytics. CFBA 2024. Learning and Analytics in Intelligent Systems, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-031-70598-4_31
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DOI: https://doi.org/10.1007/978-3-031-70598-4_31
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