New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy
Joint Authors
Li, Gai
Xian, Zhengzheng
Li, Qiliang
Li, Lei
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era.
However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse.
Singular Value Decomposition (SVD) is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction.
The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information.
Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge.
In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation.
Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy.
In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.
American Psychological Association (APA)
Xian, Zhengzheng& Li, Qiliang& Li, Gai& Li, Lei. 2017. New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189718
Modern Language Association (MLA)
Xian, Zhengzheng…[et al.]. New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy. Mathematical Problems in Engineering No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1189718
American Medical Association (AMA)
Xian, Zhengzheng& Li, Qiliang& Li, Gai& Li, Lei. New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1189718
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189718