A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator  

作　　者：YI Huawei ZHANG Fuzhi LAN Jie 

机构地区：[1]School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei Province, E R.China [2]The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, P. R. China [3]Liaoning University of Technology, Jinzhou 121001, Liaoning Province, P. R. China

出　　处：《China Communications》2014年第9期112-123,共12页中国通信（英文版）

基　　金：National Natural Science Foundation of China under Grant No.61379116，Natural Science Foundation of Hebei Province under Grant No.F2015203046 and No.F2013203124，Key Program of Research on Science and Technology of Higher Education Institutions of Hebei Province under Grant No.ZH2012028

摘　　要：The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.The existing collaborative recommendation algorithms have lower robustness against shilling attacks. With this problem in mind, in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator. Firstly, we propose a k-distance- based method to compute user suspicion degree （USD）. The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model. The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users. Then, Tukey M-estimator is introduced to construct robust matrix factorization model, which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix. Finally, a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model. Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关 键 词：shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] O212.1[自动化与计算机技术—计算机科学与技术]