title: Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
abstract: 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 vu
journal: arXiv (Cornell University)