Analysis of Segment Shilling Attack Against Trust Based Recommender Systems

Recent research has focus on examining the security of collaborative filtering (CF) recommender system. Segment attack concentrates on a targeted set of users with similar tastes. In this paper, we examine the effectiveness of segment attack against our topic-level trust based recommendation algorithm that incorporate topic-level trust model into traditional collaborative filtering algorithm. The results of our experiments conducted on well-known dataset show that segment attack is more effective against topic-level trust based recommendation algorithm than against classical user-based CF algorithm.