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

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

Content Provider	Scilit
Author	Deng, Jianghua Qing, Haohua
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series 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.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1852/2/022040/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1852/2/022040
Journal	Journal of Physics: Conference Series
Issue Number	2
Volume Number	1852
Language	English
Publisher	IOP Publishing
Publisher Date	2021-04-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Information and Library Science Based On Data Mining Algorithms
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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