%0 Journal Article
%T Semantic clustering-based attack detection model on CF-based recommender systems<br>基于语义聚类的协作推荐攻击检测模型
%A CHEN Jian
%A OU Qing-yong
%A ZHENG Yu-xin
%A LI Dong
%A <br>陈健
%A 区庆勇
%A 郑宇欣
%A 李东
%J 计算机应用
%D 2009
%I 
%X Collaborative recommender systems have been widely used in E-commerce environment. Because this recommendation technology is very sensitive to user's profile, an attacker can affect the prediction by injecting a lot of biased users' profiles. Therefore, the author proposed a semantic clustering-based attack detection model on CF-based recommender systems, which mined the potential interest combination by analyzing the semantics of items in the transaction database. The proposed model judged the truth of a user's profile by detecting the randomness in a user's data. Extensive experiments demonstrate that the proposed model can effectively detect the "profile injection" attacks in CF-based recommender system, which can significantly improve the robustness and reliability of the whole system.
%K collaborative filtering
%K recommender system
%K attack model
%K semantic clustering<br>协作过滤
%K 推荐系统
%K 攻击模型
%K 语义聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=CE5ED38A7DBD2185C7BEC3FA28ACCD96&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=2A0592C45C936A61&eid=7E01AF4ED17ED9B3&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=3