Shilling attack is one of serious challenges which recommender systems are facing. Malicious users can easily insert well-designed ratings into recommender systems to affect recommendation results and decrease user experiences because of the openness of recommender systems. This paper proposed a robust shilling-attack-tolerant personalized recommendation(SATPR) algorithm based on attribution facilitated matrix completion with structural noise technology, regarded the ratings of attack users in the rating matrix as structural row noise and modeled them with L_2,1-norm. This paper also introduced attributive cha-racters of users and items to improve the accuracy of detection of shilling-attack. Experimental results show that SATPR algorithm achieves more accurate results of personalized rating prediction than traditional recommendation algorithms under shilling attacks.

[2019-03-05] Printed Article /doc/2017.09.0906/v2