Reverse Bandwagon Profile Inject Attack against Recommender Systems

Collaborative filtering algorithms are successfully used in personalized recommender systems for their simplicity and high recommending quality. However, significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Malicious users can inject a large number of biased profiles into such a system in order to make recommendations that favor or disfavor given items. The reverse bandwagon attack is considered to need low knowledge cost. In this paper, we examine the robustness of our topic-level trust-based recommendation algorithm that incorporate topic-level trust model into classic collaborative filtering algorithm under the reverse bandwagon attack. The results of our experiments show that topic-level trust based Collaborative Filtering algorithm offers significant improvements in stability over the standard k-nearest neighbor approach when attacked.