A differentially private nonnegative matrix factorization for recommender system

doi:10.1016/j.ins.2022.01.050

CORC > 兰州理工大学 > 兰州理工大学

	A differentially private nonnegative matrix factorization for recommender system
	Ran, Xun 2; Wang, Yong 2; Zhang, Leo Yu 1; Ma, Jun 3
刊名	Information Sciences
	2022-05-01
卷号	592 页码:21-35
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
ISSN号	0020-0255
DOI	10.1016/j.ins.2022.01.050
英文摘要	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.
WOS研究方向	Computer Science
语种	英语
出版者	Elsevier Inc.
WOS记录号	WOS:000796947000002
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/157951]
专题	兰州理工大学
作者单位	1.School of Information Technology, Deakin University, Waurn Ponds; VIC; 3216, Australia; 2.Key Laboratory of Electronic Commerce and Logistics of Chongqing, Chongqing University of Posts and Telecommunications, Chongqing; 400065, China; 3.Department of Physics, Lanzhou University of Technology, Lanzhou; 730050, 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.