Cross-border E-commerce Course Construction Based on Data Mining Algorithm

Abstract

In recent years, Cross-border(CB) e-commerce(EC) has developed rapidly in our country, and the government has also introduced a series of measures to promote the development of CB EC. In the era of big data, recommendation systems are widely used in CB EC platforms, and more and more attention is paid to its personalized recommendation technology. Based on this, this paper studies the application of data mining algorithms in the construction of CB EC courses, which has certain guiding significance for the current market and enterprise development. By taking two CB EC companies as the research object, this paper uses data mining algorithms to mine and analyze the relevant data of these two EC companies, and find that a platform with a data mining system has more advantages in researching customer shopping preferences;comparing people’s satisfaction with EC companies with data mining systems and traditional companies, 83% of the participants in the experimental group were satisfied, of which 60% were very satisfied, while only 59% in the control group of people are satisfied.This shows that data mining algorithms are of great significance to the development of CB EC, and it also points out a new direction for the construction of CB EC courses, such as CB EC based on data mining algorithms. On the one hand, this will also play a positive role in the future development of CB EC.

Personalized Clustering Method of Cross-Border e-Commerce Topics Based on ART Algorithm

“…As a novel business model, CBEC enables local industries to upgrade and iterate on a variety of dimensions. However, it is critical to examine the current state of a airs and identify growth-restraining constraints during the development process in order to further optimize CBEC support policies [4][5][6].…”