Construction of Cross-Border Logistics System based on Preference Recommendation Algorithm

This article proposes a cross-border logistics system based on preference recommendation algorithm, aiming to improve the logistics efficiency and customer satisfaction of cross-border e-commerce. With the deepening of globalization and the advancement of information technology revolution, cross-border e-commerce has become an important driving force for current economic development. The development of cross-border e-commerce relies on efficient and secure crossborder logistics systems to ensure that goods can be quickly and accurately delivered to consumers. Therefore, optimizing and improving the cross-border logistics system is particularly important. The research objective of this article is to provide a one-stop solution for cross-border sellers, promoting the rapid development of cross-border e-commerce business by recommending the most suitable logistics partners based on preferences and optimizing warehouse management. We use a preference recommendation algorithm based on user historical order data and preferences, and recommend the best logistics partners to users through a combination of collaborative filtering and deep learning. Detailed description of the system construction process, including data collection, data preprocessing, model building, and system implementation. We have established a preference recommendation model for crossborder logistics through data collection and preprocessing, and used this model to optimize various aspects of the cross-border logistics process. The system automatically learns feature representations of user preferences through deep learning and its effectiveness has been verified through experiments. The experimental results show that cross-border logistics systems based on preference recommendation algorithms can significantly improve logistics efficiency and customer satisfaction.