基于奇异值分解模型的在线实时推荐的隐私保护  

Privacy Preservation of Online Real-Time Recommendation Based on the SVD Scheme

作　　者：路应金[1] 杜素娟[1] LU Ying-jin;DU Su-juan(University of Electronic Science and Technology of China　Chengdu　611731　China)

机构地区：[1]电子科技大学,成都611731

出　　处：《电子科技大学学报（社科版）》2017年第2期74-81,共8页Journal of University of Electronic Science and Technology of China(Social Sciences Edition)

基　　金：国家自然科学基金(71372140)

摘　　要：利用缩减的奇异值分解更新算法和随机技术提出了一个基于奇异值分解模型的在线推荐的隐私保护方法,将新数据混合到原始数据中保护消费者在线购物的隐私数据。实验结果表明,我们提供的模型可以保证数据高效性和更低概率的隐私泄露,并且预测的精度仍然很高,对于实现消费者网上隐私保护有重要的指导意义。The most personalized recommendation method research of online shopping faces challenges about how to ensure the validity of data during the data sharing process and protect users'personal privacy.In this paper,we propose a privacy preserving scheme of online recommendation based on the SVD algorithm by the truncated SVD update algorithms and randomization techniques.It turns out that the proposed scheme can conduct data efficiently and protect data privacy effectively.It is of important guiding significance for privacy preserving of online consumers.

关 键 词：奇异值分解 隐私保护 实时推荐 协同过滤 

分 类 号：G206.2[文化科学—传播学]