考虑用户兴趣分析的差分隐私方案推荐  

Recommendation of differential privacy scheme considering user interest analysis

作　　者：耿秀丽[1] 王著鑫 Geng Xiuli;Wang Zhuxin(Business School,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《计算机应用研究》2022年第2期474-478,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(71301104);国家教育部人文社会科学研究规划基金资助项目(19YJA630021);高等学校博士学科点专项科研基金资助项目(20133120120002)。

摘　　要：现有差分隐私推荐算法在计算相似度时,直接根据用户—方案数据进行计算,而忽略了方案属性对用户偏好的影响,没有反映用户的真实偏好,不能进行准确推荐。针对此问题,提出考虑用户兴趣分析的差分隐私推荐方法。该方法首先收集用户对方案属性的兴趣评分,其次使用K-means++对用户—方案属性评分数据进行聚类,然后采用差分隐私算法选择近邻用户,并为目标用户推荐适合的方案。最后,以养老院方案推荐为例予以验证。实验结果显示:与KDPC、DPCF、PNCF相比,所提算法在相同隐私预算下,平均绝对误差下降约19.0%、34.0%、37.7%;在相同近邻集合尺寸下,平均绝对误差下降约10.4%、20.3%、21.4%。因此,该算法在保护了用户隐私的基础上,进一步提高了推荐精度。When calculating similarity,the existing differential privacy recommendation algorithms directly calculate based on user-scheme data,but ignore the influence of scheme attributes on user preferences,fail to reflect the real preferences of users,and cannot make accurate recommendations.To solve this problem,this paper proposed differential privacy recommendation method considering user interest analysis.In this method,it collected firstly users’interest ratings for scheme attributes,and secondly clustered the user-scheme attributes rating data by K-means++.Then,it used the differential privacy algorithm to select the nearest users,and recommended suitable schemes for the target user.Finally,taking the recommendation of nursing home schemes as an example,the experimental results show that compared with KDPC,DPCF and PNCF,the proposed algorithm can reduce the mean absolute error by about 19.0%,34.0%and 37.7%under the same privacy budget.The mean absolute error decreases by 10.4%,20.3%and 21.4%for the same size of the nearest neighbor set.Therefore,based on protecting the privacy of users,the algorithm further improves the recommendation accuracy.

关 键 词：差分隐私 用户兴趣 方案属性 K-means++ 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]