Spammer Groups and Target Item Groups Detection in Time Series Data
Baoxing Wang, Shu Lei Wu, Liang Wang, Guiquan Liu · International Conference on Multimedia Information Networking and Security · 2013
Recommender systems become popular in recent years. It has been widely used in many areas such as e-commerce and social networks. Many recommender algorithms have been proposed and the most famous one is collaborative filtering algorithm. As it is vulnerable to inject profile attacks, some people try to attack the system due to profit purpose. They promote or demote target items by injecting some fake user profiles into the system. Existing works are mainly focused on detecting individual spammers and few of them are proposed to detect groups of spammers while these groups have stronger influence than individuals on recommender systems. But none of them can detect items which are under attack at the same time. In this paper we propose a method which can not only detect groups of spammers but also detect correlated groups of items. The proposed method first find some candidate groups of attackers and candidate groups of target items. Then we derive three features from the collusion attack phenomenon to detect groups of spammers and groups of target items. Finally we utilize rank aggregation technique to get the most possible group of spammers and correlated group of target items. Experimental results demonstrate the effectiveness of the proposed method.