基于属性优化矩阵补全的抗托攻击推荐算法

托攻击是当前推荐系统面临的严峻挑战之一。由于推荐系统的开放性，恶意用户可轻易对其注入精心设计的评分从而影响推荐结果，降低用户体验。基于属性优化结构化噪声矩阵补全技术，提出一种鲁棒的抗托攻击个性化推荐（SATPR）算法，将攻击评分视为评分矩阵中的结构化行噪声并采用L2,1范数进行噪声建模，同时引入用户与物品的属性特征以提高托攻击检测精度。实验表明，SATPR算法在托攻击下可取得比传统推荐算法更精确的个性化评分预测效果。

Shilling-attack-tolerant recommendation algorithm based on attribute facilitated matrix completion

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 article 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 L2,1-norm. This article also introduced attributive characters of users and items to improve the accuracy of detection of shilling-attack. Experimental results showed that SATPR algorithm achieved more accurate results of personalized rating prediction than traditional recommendation algorithms under shilling attacks.