Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic  

作　　者：ZHAO Feng XIONG Yan LIANG Xiao GONG Xudong LU Qiwei 

机构地区：[1]School of Computer Science and Technology, University of Science and Technology of China [2]Dispatching and Control Center, Anhui Electric Power Corporation

出　　处：《Chinese Journal of Electronics》2016年第1期20-25,共6页电子学报（英文版）

基　　金：supported by National Natural Science Foundation of China(No.61202404,No.61170233,No.61232018,No.61272472,No.61272317,No.61300170);the Fundamental Research Funds for the Central Universities(No.WK0110000036);University Provincial Natural Science Foundation of Anhui Province(No.KJ2013A040)

摘　　要：Recommendation has become increasingly important because of the information overload. Collaborative filtering(CF) technique, as the most popular recommendation method, utilizes the historical preferences of users to predict their future interests on other items.However, CF technique requires collecting users' rating information, which may lead to the disclosure of privacy. We propose a new randomized perturbation approach Time-drifting privacy-preserving collaborative filtering(TPPCF) to well balance privacy of users and accuracy of recommendation. Since users' recent ratings can better represent their interests and preferences, we incorporate a varying weight into the approach. Specifically, we assign higher weights to more recent ratings both when computing user similarity and perturbing users' ratings. To further improve the efficiency, we cluster the users into several groups to reduce computation cost. We demonstrate the effectiveness and efficiency of our method through experiments on Movie Lens dataset, which shows TPPCF can achieve higher privacy while generating more accurate recommendation.Recommendation has become increasingly important because of the information overload. Collaborative filtering（CF） technique, as the most popular recommendation method, utilizes the historical preferences of users to predict their future interests on other items.However, CF technique requires collecting users＇ rating information, which may lead to the disclosure of privacy. We propose a new randomized perturbation approach Time-drifting privacy-preserving collaborative filtering（TPPCF） to well balance privacy of users and accuracy of recommendation. Since users＇ recent ratings can better represent their interests and preferences, we incorporate a varying weight into the approach. Specifically, we assign higher weights to more recent ratings both when computing user similarity and perturbing users＇ ratings. To further improve the efficiency, we cluster the users into several groups to reduce computation cost. We demonstrate the effectiveness and efficiency of our method through experiments on Movie Lens dataset, which shows TPPCF can achieve higher privacy while generating more accurate recommendation.

关 键 词：Collaborative filtering （CF） Random-ization PRIVACY Varying Weight Time-drifting character-istic. 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]