摘要
研究基于Apriori算法的电子商务个性化推荐系统。通过该系统,可以实现对电商的日常交易行为大数据的挖掘,并为用户量身定制推荐相关内容。实现该系统的关键是,在大型存储库中发现关联规则,并通过修改后的Apriori技术将关联规则转换为用户偏好推荐。在此基础上,根据权重对算法进行优化剪枝,以降低算法的复杂度,提高算法的实时性。
The automatic personalized recommendation system based on Apriori algorithm is studied in this paper.The system can mine the big data of daily transaction behavior of e-commerce to help user select customized recommendation content.The algorithm focuses on the process of discovering association rules in a large repository and converts the association rules into recommendations for different user preferences based on improved Apriori technology.On this basis,weights are cited to optimize pruning of the algorithm to reduce complexity and further improve the real-time performance of the algorithm.
作者
陶庆凤
TAO Qingfeng(Practice Teaching Center of Minnan Institute of Technology,Shishi Fujian 362700,China)
出处
《重庆科技学院学报（自然科学版）》
CAS
2020年第6期62-64,共3页
Journal of Chongqing University of Science and Technology：Natural Sciences Edition
基金
2019年度福建省教育厅中青年教师教育科研项目“基于虚拟现实(VR)的手势控制器开发”(JAT190880)。
作者简介
陶庆凤(1983—),女,实验师,研究方向为系统开发、实验教学与管理。
