Uplift Modeling for Target User Attacks on Recommender Systems
Wenjie Wang,
Changsheng Wang,
Fuli Feng
et al.
Abstract:Recommender systems are vulnerable to injective attacks, which inject limited fake users into the platforms to manipulate the exposure of target items to all users. In this work, we identify that conventional injective attackers overlook the fact that each item has its unique potential audience, and meanwhile, the attack difficulty across different users varies. Blindly attacking all users will result in a waste of fake user budgets and inferior attack performance. To address these issues, we focus on an under… Show more
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