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Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
[article]
2021
arXiv
pre-print
Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks difficult to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of ways to generate the adversarial samples drawn from different distribution as the
arXiv:2112.00973v1
fatcat:nqjcqphuujfrhhyfu6gj754sii