RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM OF CROSS-BORDER E-COMMERCE UNDER LARGE DATA BACKGROUND

With the expansion of the E-commerce industry, the coverage of goods is becoming increasingly wider. Moreover, foreign E-commerce industries have gradually expanded to the Chinese market, resulting in higher requirements of domestic consumers on the safety, variety and cost performance of foreign products. Therefore, how to make the sales of cross-border E-commerce more stable and keep the balance of cross-border E-commerce inventory and sales is an urgent problem to be solved. As traditional modes are not suitable for foreign E-commerce industries, a personalized recommendation system with favorable big data processing capacity is needed to address the problem. This paper introduced a common personalized recommendation system and applied the collaborative filtering algorithm as the main algorithm of the system to solve problems in practice. The results showed that the improved collaborative filtering recommendation system could meet the requirement of the times and was worth being promoted.