New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy

Zhengzheng Xian,Lei Li,Gai Li,Qiliang Li

doi:10.1155/2017/1975719

Abstract

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.

Highlights

The Internet has been widely used since the birth of Web 2.0, and the human lifestyle has been greatly changed
The Singular Value Decomposition (SVD) model [1] is a kind of common collaborative filtering method to provide personalized recommendation services, and the predictive accuracy can be improved by considering the user and item bias information
In order to protect the private information of the original data on the basis of ensuring the predictive accuracy, we proposed three new methods that apply differential privacy to SVD++ through gradient perturbation, objective-function perturbation, and output perturbation

Summary

Introduction

The Internet has been widely used since the birth of Web 2.0, and the human lifestyle has been greatly changed. When a user opens a shopping website or a mobile terminal application, a very enthusiastic recommender system will list some commodities in which he or she may be interested based on the purchase history record, browser footprint, evaluation information, and so forth. There are numerous intelligent applications such as those. If the value of implicit feedback information such as historical browsing data, historical rating data, and the evaluation timestamp can be fully exploited, the predictive accuracy could be improved further. The Singular Value Decomposition (SVD) model [1] is a kind of common collaborative filtering method to provide personalized recommendation services, and the predictive accuracy can be improved by considering the user and item bias information. As a derivative model of SVD, the SVD++ model [2,3,4] achieves better recommendation accuracy by adding implicit feedback information, such as movies that a user has evaluated, and the specific value of the score does not matter for this kind of information

Objectives

Methods

Results

Conclusion