Research on User Recommendation Algorithm Based on Cross-border E-commerce Platform

The development of economic globalization has led to the rapid growth of cross-border e-commerce. Due to the huge data of overseas commodities, it makes consumers encounter great confusion in the process of choosing commodities. How to let consumers quickly choose the goods they are satisfied with in a large number of goods is one of the urgent problems that cross-border e-commerce needs to solve. The target customer groups targeted by cross-border e-commerce are more inclined to pay attention to the quality and safety of goods. In this paper, we firstly analyze the current recommendation algorithm, and based on the shortcomings of the current recommendation algorithm, we propose an improved recommendation algorithm that incorporates label weights, and simulate and verify the algorithm, and the experimental results show that the recommendation algorithm has better recommendation effect compared with other recommendation algorithms.