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Found 2 papers, 2 papers with code
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.
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