Research on cross-border e-commerce recommendation system based on collaborative filtering

In the modern economic innovation and development, cross-border e-commerce as a new business industry, the actual data scale began to expand sharply as e-commerce, system users are faced with information overload and other problems, so researchers put forward to develop a corresponding recommendation system. Nowadays, when studying the recommendation system of cross-border e-commerce, scholars from various countries not only put forward a variety of recommendation system models, but also achieved excellent results in practice and exploration. Since cross-border e-commerce contains entry and exit information of multiple types of commodities, it will be affected by various policies and regulations, and the special needs of recommendation systems need to be comprehensively considered. Therefore, the traditional collaborative filtering recommendation algorithm does not meet the needs of e-commerce industry in the new era. On the basis of understanding the research status of cross-border e-commerce recommendation system in recent years, this paper deeply discusses the structure of cross-border e-commerce promotion system based on collaborative filtering according to the basic concept of collaborative filtering algorithm. The final experimental results show that the improved collaborative filtering algorithm has more application value and good recommendation effect than the traditional collaborative filtering algorithm.