Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information. To address these vulnerabilities, we propose Cascade-guided Adversarial training, a new adversarial training procedure that is specifically designed for sequential recommendation models. Our approach harnesses the intrinsic cascade effects present in sequential modeling to produce strategic adversarial perturbations to item embeddings during training. Experiments on training state-of-the-art sequential models on four public datasets from different domains show that our training approach produces superior model ranking accuracy and superior model robustness to real item replacement perturbations when compared to both standard model training and generic adversarial training.

翻译：序列推荐模型通过从用户与物品的时间顺序交互中学习，在多种场景下优于传统推荐模型。尽管取得了成功，但序列推荐模型的鲁棒性近期受到质疑。序列推荐模型固有的两个特性可能削弱其鲁棒性：训练过程中产生的级联效应，以及模型过度依赖时间信息的倾向。为解决这些脆弱性问题，我们提出级联引导的对抗训练（Cascade-guided Adversarial Training）——一种专为序列推荐模型设计的新型对抗训练流程。该方法利用序列建模中内在的级联效应，在训练期间对物品嵌入生成具有战略性的对抗扰动。在来自四个不同领域的公开数据集上，对当前最优序列模型进行训练实验表明：相较于标准训练与通用对抗训练，本训练方法不仅显著提升了模型排序精度，还大幅增强了模型对真实物品替换扰动的鲁棒性。