AsarRec learns adaptive sequence augmentations via transformation matrices and Semi-Sinkhorn projection to improve robustness of self-supervised sequential recommenders under noise.
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MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.
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AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
AsarRec learns adaptive sequence augmentations via transformation matrices and Semi-Sinkhorn projection to improve robustness of self-supervised sequential recommenders under noise.
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MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender
MVIGER integrates complementary knowledge from diverse prompts and indices in generative recommenders via a variational model with learnable prior over latent sources, showing superior performance on three datasets.