Information Personalized Recommendation Algorithm for Cross-Border E-Commerce Guide Platform Based on Constrained Clustering

Abstract

The traditional algorithm costs a lot in calculation and storage, Cross-border e-commerce brings more opportunities to international logistics, but challenges are also very severe, but there are also great problems, such as customer satisfaction, return and exchange of goods, parcel loss and so on. This paper explores the matching convergence function of cross-border e-commerce logistics platform based on artificial intelligence and the ecosystem architecture of supply chain system, deeply analyzes the operation mode of four typical cases of cross-border e-commerce platform, further analyzes the risk distribution of cross-border e-commerce platform, and puts forward relevant risk prevention and control measures. Based on the machine learning algorithm Light GBM model as the core, multi-dimensional features are constructed and analyzed based on user behavior data and historical sales data, and the prediction is completed through machine learning modeling. The application of artificial intelligence to live streaming e-commerce is bound to bring revolutionary changes to the development of live streaming e-commerce.