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

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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.

shilling attacksrobust collaborative recommendationmatrix factorizationk-distanceTukey M-estimator

YI Huawei、ZHANG Fuzhi、LAN Jie

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School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei Province, P.R.China

The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, P.R.China

Liaoning University of Technology, Jinzhou 121001, Liaoning Province, P.R.China

This work was supported by National Natural Science Foundation of ChinaNatural Science Foundation of Hebei ProvinceNatural Science Foundation of Hebei ProvinceKey Program of Research on Science and Technology of Higher Education Institutions of Hebei Province

61379116F2015203046F2013203124ZH2012028

2014

中国通信(英文版)

中国通信(英文版)

CSTPCDCSCDSCI
影响因子：0.463
ISSN：1673-5447
年,卷(期)：2014.11(9)
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