摘要
通过对用户进行模糊C均值聚类,使其以不同的隶属度隶属于不同聚类,解决了因硬聚类导致的推荐准确度低的问题,获得更加准确的聚类效果;针对推荐算法的隐私泄露问题,通过将Laplace噪声引入到模糊C均值聚类过程中,实现基于差分隐私保护的模糊C均值聚类推荐.实验结果表明,该算法在保证推荐质量的同时有效改善了推荐系统的安全性.
The users are classified by different membership degrees with fuzzy C-means clustering. A more accurate clustering effect has been obtained and the problem of low recommendation accuracy caused by hard clustering is solved.Aiming at the privacy leakage problem of recommendation algorithm, the Laplace noise is introduced into the fuzzy C-means clustering process, and the differential privacy protection based fuzzy C-means clustering recommendation is implemented. The experimental results show that the proposed algorithm can effectively improve the security of the recommended system with the good quality of the recommendation.
作者
蒋宗礼
乔向梅
JIANG Zong-Li;QIAO Xiang-Mei(Faculty of Information Technology,B eijing University of Technology,Beijing 100124,China)
出处
《计算机系统应用》
2018年第10期189-195,共7页
Computer Systems & Applications
关键词
协同过滤
模糊C均值聚类
差分隐私
collaborative filtering
fuzzy C-means clustering
differential privacy