关联规则在移动电子商务推荐系统中的应用

以数据挖掘中的关联规则为基础，深入研究了个性化推荐系统的架构和推荐系统模型。针对传统关联规则挖掘算法在解决移动电子商务环境个性化推荐问题时反复扫描数据库，频繁项挖掘效率低，关联规则挖掘准确率低以及规则大量冗余等不足，采用基于事务矩阵和兴趣度的关联规则挖掘算法构建个性化的推荐系统模型，提升了个性化推荐质量。

Application of Association Rules in E-commerce Recommendation System

On the basis of association rules in data mining,the architecture of personalized recommendation system and recommendation system model is deeply researched.However, there are several disadvantages when traditional ARM are applied in personalized recommendation system in mobile e-commerce,such as ,repeat databases scanning with low mining efficiency, mining loads of redundancy rules with low accuracy.Therefore,this paper use the transacition-matrix- ＆-user-interestingness-based association rule mining algorithm,MIbARM, to bulid a personalized recommendation system model.It is very effective for personalized recommendation problem in mobile E-commerce.