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.

本文提出了 Cascade-guided Adversarial training，一种针对顺序推荐模型的新对抗性训练过程，以解决顺序推荐模型的健壮性和过度依赖时间信息的问题。实验结果表明，我们的训练方法在模型排名准确性和模型健壮性方面都优于标准模型训练和通用对抗性训练。