Achieving optimal privacy in trust-aware social recommender systems
SocInfo'10 Proceedings of the Second international conference on Social informatics
Defending recommender systems by influence analysis
Information Retrieval
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Collaborative Filtering (CF) is considered a powerful technique for generating personalized recommendation. 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 average attack model is a somewhat more sophisticated attack than the random attack model. 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 average 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 under average attack.