E-COMMERCE DATA MINING ANALYSIS BASED ON USER PREFERENCES AND ASSOCIATION RULES

With the development of network technology, online shopping is becoming more and more convenient. But the increasing number of products also makes it difficult for consumers to make the right decision. When there is no apparent market demand, how to recommend products with commercial potential to customers has become an urgent problem for businesses to solve. This paper proposes e-commerce product recommendation based on user preference and association rule algorithm aiming at the problems existing in e-commerce product recommendation. Firstly, this paper constructs a user interest modeling method. Through analyzing users' interests and preferences, to provide users with timely and accurate personalized services. Then, the FP_Growth algorithm is optimized and improved. A more effective CTE-MARM algorithm is designed, and an association rules database based on user benefit items is constructed and analyzed jointly. Analyze products with strong correlations. According to consumers' interest levels, TOP-N is the best product choice. Experiments show that the algorithm has higher prediction accuracy. The research results of this project can not only improve enterprises' ability to analyse data and provide data support for enterprises to carry out effective marketing management.