基于关联规则的客户行为建模与商品推荐研究

doi:10.12052/gdutxb.180015

Abstract

Abstract: With the development of e-commerce in China, the traditional e-business service mode can no longer meet people's shopping needs. Personalized recommendation for customers is a problem worthy of study. In this research, the improvement of Apriori algorithm is used to mine user interest information and the user correlation. Then, a user behavior model is set up, and can recommend the goods of interest, and improve the user's purchase experience. Experiments show that the improved Apriori algorithm improves the accuracy and speed of the recommendation system.

Key words: E-commerce, personalized recommendation, the improvement of Apriori algorithm, user behavior modeling

CLC Number:

TP311

Lin Sui, Zheng Zhi-hao. A Research of a Recommender System Based on Customer Behavior Modeling by Mining Association Rules[J].Journal of Guangdong University of Technology, 2018, 35(03): 90-94.

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