E-Commerce Data Mining Analysis based on User Preferences and Association Rules

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Zhiying Fan


Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.

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Special Issue - Graph Powered Big Aerospace Data Processing