摘要
随着我国电子商务事业的发展,传统的电子商务服务模式已经不能满足人们的购物需求,针对客户个性化推荐的研究具有一定的意义.本文将Apriori算法进行改进,利用改进的Apriori算法对用户兴趣信息进行挖掘,挖掘用户之间的关联性,建立用户行为模型,为用户推荐其感兴趣的商品,提升用户的购买体验.实验表明,改进的算法提高了推荐的精度和速度.
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.
作者
林穗
郑志豪
Lin Sui;Zheng Zhi-hao(School of Computers, Guangdong University of Technology, Guangzhou 510006, China)
出处
《广东工业大学学报》
CAS
2018年第3期90-94,共5页
Journal of Guangdong University of Technology
基金
广州市科技计划项目(2017010160012)
关键词
电子商务
个性化推荐
APRIORI算法改进
用户行为建模
E-commerce
personalized recommendation
the improvement of Apriori algorithm
user behavior modeling
