Collaborative Denoising Shilling Attack for Recommendation Systems

Recommendation systems, while enhancing user experiences and increasing platform revenues, are vulnerable to shilling attacks due to their ubiquity and inherent openness. These attacks manipulate recommendations by strategically injecting fake profiles, often capitalizing on noisy data inconsistencies to produce imperceptible, attack-aware profiles. Existing research largely emphasizes heuristic and deep neural network (DNN) methodologies, overlooking this noise-impact aspect. In this context, we present the Collaborative Denoising Shilling Attack (CDSA) model. CDSA integrates the collaborative information bottleneck (CIB) to distill latent features, optimizing mutual information between denoised inputs and imperceptible, attack-aware features. The multi-feature learning component refines these features via multiple objectives, enhancing the information bottleneck representation. These objectives are sequentially optimized, following a hierarchical framework. Our empirical tests affirm CDSA’s superiority over traditional attack strategies and cutting-edge methods, such as AUSH [1] and GSPAttack [2].