%0 Journal Article
%A 陈一帅
%A 冯婷婷
%A 郭宇春
%A 杨晶晶
%T User Friendly Preferential Private Recommendation
%D 2022
%R 10.19682/j.cnki.1005-8885.2022.1006
%J 中国邮电高校学报（英文）
%P 43-53
%V 29
%N 3
%X <p>
	To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.
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%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2022.1006