A differentially private nonnegative matrix factorization for recommender system

doi:10.1016/j.ins.2022.01.050

IR > 理学院

	A differentially private nonnegative matrix factorization for recommender system
	Ran, Xun 1; Wang, Yong 1; Zhang, Leo Yu 2; Ma, Jun3
	2022-05
发表期刊	Information Sciences
ISSN	0020-0255
卷号	592 页码:21-35
摘要	Nonnegative matrix factorization (NMF)-based models have been proven to be highly effective and scalable in addressing collaborative filtering (CF) problems in the recommender system (RS). Since RS requires tremendous user data to provide personalized information services, the issue of data privacy has gained prominence. Although the differential privacy (DP) technique has been widely applied to RS, the requirement of nonnegativity makes it difficult to successfully incorporate DP into NMF. In this paper, a differentially private NMF (DPNMF) method is proposed by perturbing the coefficients of the polynomial expression of the objective function, which achieves a good trade-off between privacy protection and recommendation quality. Moreover, to alleviate the influence of the noises added by DP on the items with sparse ratings, an imputation-based DPNMF (IDPNMF) method is proposed. Theoretic analyses and experimental results on several benchmark datasets show that the proposed schemes have good performance and can achieve better recommendation quality on large-scale datasets. Therefore, our schemes have high potential to implement privacy-preserving RS based on big data. © 2022 Elsevier Inc.
关键词	Benchmarking Collaborative filtering Economic and social effects Information services Large dataset Matrix algebra Matrix factorization Privacy-preserving techniques Differential privacies Factorization methods Filtering problems Imputation Non-negativity Nonnegative matrix factorization Objective functions Personalized information services Polynomial expression User data
DOI	10.1016/j.ins.2022.01.050
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000796947000002
出版者	Elsevier Inc.
EI入藏号	20220611588938
EI主题词	Recommender systems
EI分类号	716 Telecommunication ; Radar, Radio and Television ; 718 Telephone Systems and Related Technologies ; Line Communications ; 723.2 Data Processing and Image Processing ; 723.5 Computer Applications ; 903.1 Information Sources and Analysis ; 903.4 Information Services ; 921 Mathematics ; 921.1 Algebra ; 971 Social Sciences
来源库	WOS
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/157951
专题	理学院
通讯作者	Wang, Yong
作者单位	1.Chongqing Univ Posts & Telecommun, Key Lab Elect Commerce & Logist Chongqing, Chongqing 400065, Peoples R China; 2.Deakin Univ, Sch Informat Technol, Waurn Ponds, Vic 3216, Australia; 3.Lanzhou Univ Technol, Dept Phys, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Ran, Xun,Wang, Yong,Zhang, Leo Yu,et al. A differentially private nonnegative matrix factorization for recommender system[J]. Information Sciences,2022,592:21-35.
APA	Ran, Xun,Wang, Yong,Zhang, Leo Yu,&Ma, Jun.(2022).A differentially private nonnegative matrix factorization for recommender system.Information Sciences,592,21-35.
MLA	Ran, Xun,et al."A differentially private nonnegative matrix factorization for recommender system".Information Sciences 592(2022):21-35.