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，即从时序用户-物品交互中学习的模型，在许多场景下优于传统推荐模型。尽管序列推荐模型取得了成功，但其鲁棒性近期受到质疑。序列推荐模型特有的两个特性可能削弱其鲁棒性——训练过程中引发的级联效应以及模型过度依赖时序信息的倾向。为应对这些脆弱性，我们提出级联引导的对抗训练，这是一种专为序列推荐模型设计的全新对抗训练流程。我们的方法利用序列建模中固有的级联效应，在训练过程中对物品嵌入生成策略性的对抗扰动。在四个不同领域的公开数据集上对前沿序列模型进行训练实验表明，相较于标准模型训练和通用对抗训练，我们的训练方法能产生更优的模型排序准确率，并对真实物品替换扰动展现出更强的模型鲁棒性。