DENOTER: dual cascadEd iNformatiOn filTERing for robust sequential recommendation

Sequential recommendation systems aim to predict users’ next interactions by analyzing historical sequences of their behaviors. However, these systems are vulnerable to adversarial attacks that disrupt input sequences through the injection of noisy or irrelevant interactions. Accordingly, this paper introduces the Dual cascadEd iNformatiOn filTERing (DENOTER) algorithm designed to mitigate the impact of adversarial attacks. Unlike existing recommendation methods, which often overlook the varying importance of different components (i.e., items and their associated features) within behavioral sequences, DENOTER employs a two-tier adaptive filtering mechanism. Specifically, at the feature level, a dimension-adjustable, forward-only stochastic model is introduced to dynamically select and preserve essential features, thereby effectively filtering out malicious perturbations. At the item level, a Gumbel-driven sampling strategy is leveraged to selectively retain salient items. Through multi-layer refinement with alternating feature- and item-level filtering, DENOTER progressively reduces the influence of adversarially perturbed items and features within input sequences, enabling robust recommendation without specialized data augmentation or model retraining. Extensive empirical evaluations on five datasets and four encoders demonstrate that DENOTER consistently outperforms existing baselines under diverse adversarial conditions, achieving superior robustness and accuracy even in low-resource and out-of-domain settings.