摘要
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L_(2,1)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 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 L2,1-norm.This paper also introduced attributive characters 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.
作者
周宇轩
陈蕾
张涵峰
Zhou Yuxuan;Chen Lei;Zhang Hanfeng(School of Computer Science,Nanjing University of Posts&Telecommunications,Nanjing 210003,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing University of Posts&Telecommunications,Nanjing 210003,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第3期724-729,780,共7页
Application Research of Computers
基金
江苏省自然科学基金面上项目(BK20161516)
中国博士后科学基金资助项目(2015M581794)
江苏省高校自然科学研究面上项目(15KJB520027)
江苏省博士后科研计划资助项目(1501023C)
关键词
推荐系统
托攻击
L2
1范数正则化
属性特征
recommender system
shilling attack
L2,1-norm regularization
attributive characters