Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
arXiv (Cornell University)(2023)
摘要
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.
更多查看译文
关键词
accurate sequential recommendation,cascade-guided
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要