The robustness of trust-based recommender algorithm under random attack

Collaborative Filtering(CF) is considered a powerful technique for generating personalized recommendations. However, The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. The random attack is considered to be the easiest attack. 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 random 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.