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Shifting from fixed-length tokens to semantic subwords can dramatically enhance the efficiency of attention mechanisms in generative recommenders.
Forget complex model architectures for cross-domain recommendation: Taesar shows that cleverly transforming your data can unlock better performance with standard models.
CoT reasoning can hurt recommender performance by drowning out important ID signals – unless you compress reasoning chains and use bias-subtracted contrastive decoding to realign the inference subspace.
