Segment-Focused Shilling Attacks against Recommendation Algorithms in Binary Ratings-based Recommender Systems

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. However, the open nature of collaborative filtering recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Shilling attacks against numeric ratings-based CF schemes have been extensively studied. To the best of our knowledge, there is rare study about how to attack binary ratings-based recommendation systems. Hence, shilling attacks strategies against binary ratings-based recommendation algorithms need to be further investigated. The empirical results obtained from Movielens dataset show that the segment attack, which is easy to mount, affects strongly against BU-CF algorithm and BI-CF algorithm, and the attack size and the filler size don’t have the same sensitivity in attack effect.