The Design of Cross-border E-commerce Recommendation System Based on Big Data Technology

Abstract

With the outbreak of the new technological revolution cross-border e-commerce came into being, building a new way for enterprises to export goods, a trade method that binds commodity trade with Internet information technology to form a convenient and open trade system and achieve trade interconnection of global economies. Many Chinese enterprises participate in cross-border e-commerce exports, but their export marketing strategies have drawbacks, and their marketing targeting and precision are not high, which hinder Chinese cross-border e-commerce enterprises from carrying out international marketing. In the context of big data, cross-border e-merchants can use collected data to establish a customer database, build a customer portrait model through machine learning technology, and use personalized recommendation systems to realize accurate marketing with two-way customer interaction. To address the problems in cross-border e-commerce marketing in the context of big data, this paper studies the user behavior data generated by cross-border e-commerce. Based on a large amount of low-value density behavioral data of cross-border e-commerce consumers, this paper designs a cross-border e-commerce precision marketing system applicable to processing user behavioral data, and provides a reference for those who are engaged in cross-border e-commerce.