A group attack detecter for collaborative filtering recommendation
Qingxian Wang, Yan Ren, Nengqiang He, Meng Wan, Guobo Lu · 2014
Collaborative filtering recommender systems are now popular both commercially and in the research community. However, they are vulnerable to manipulation by malicious users, where attackers inject into some fake user profiles in order to bias the recommendation results to their benefits. To solve the problem, a lots of methods have been proposed but mainly focus on identification the attacker at the individual level, i.e., to find the fake user one by one, while do not consider the similarity between attack users. In this paper, we present an algorithm to detect the attackers in group level. It works based on an effective algorithm for detecting individual malicious user and an effective clustering algorithm. More precisely, we cluster all users into group, and then find the group characters of attacked items, finally we find the attack user group. We test the algorithm on a benchmark dataset using four kinds of typical attack models, the results show that our solution is both efficient and effective, particularly in the popular attack model and the segment attack model, and the performance is significant in the segment attack model with large attack size.