Silencing the Poison: An Unsupervised Granular Ball Defense Approach in Local Smoothing Context for Recommender Systems

Abstract

In many advanced recommender systems (e.g., GNN-based), local smoothing mechanisms would distill collaborative signals but could inadvertently amplify targeted data poisoning threats. Existing defenses predominantly relied on rigid binary filtration strategies. However, such structural deletion of suspicious nodes often compromises graph connectivity, leading to a severe robustness-fidelity dilemma. To fill this gap, we propose SiPo (Silencing the Poison), a novel framework that shifts the paradigm from node removal to gradient silencing. SiPo leverages unsupervised granular ball computing to adaptively capture adversarial topological micro-clusters within the feature space. Subsequently, it geometrically collapses high-risk nodes towards a non-informative singularity via a global gravity gating mechanism. This operation blocks the backpropagation path of malicious gradients, effectively suppressing attacks while preserving the collaborative signals of normal users. Theoretical analysis confirms that SiPo enforces a compression of the local Lipschitz constant. Furthermore, extensive experiments on four public datasets demonstrate that SiPo not only significantly reduces attack success rates but also consistently improves recommendation performance. The implementation code is available at: https://github.com/KREML0/SiPo.