Attacking item-based recommender systems with power items
Carlos E. Seminario, David C. Wilson · 2014
Recommender Systems (RS) are vulnerable to attack by malicious users who intend to bias the recommendations for their own benefit. Research in this area has developed attack models, detection methods, and mitigation schemes to understand and protect against such attacks. For Collaborative Filtering RSs, model-based approaches such as item-based and matrix-factorization were found to be more robust to many types of attack. Advice in designing for system robustness has thus been to employ model-based approaches. Our recent work with the Power User Attack (PUA), however, determined that attackers disguised as influential users can successfully attack (from the attacker's viewpoint) SVD-based recommenders, as well as user-based. But item-based systems remained robust to the PUA. In this paper we investigate a new, complementary attack model, the Power Item Attack (PIA), that uses influential items to successfully attack RSs. We show that the PIA is able to impact not only user-based and SVD-based recommenders but also the heretofore highly robust item-based approach, using a novel multi-target attack vector.