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Allocating latent refinement steps based on information gain can significantly boost generative recommendation accuracy while optimizing computational resources.
LLMs can denoise sequential recommendations by disagreeing with the recommendation model itself, leading to more robust performance against noisy user data.
Forget generic image-text embeddings – teaching models to generate and reason about product *attributes* unlocks SOTA e-commerce retrieval.
Forget external text corpora – this new method unlocks surprisingly effective sequential recommendations by cleverly routing and filtering token embeddings from multiple LLMs.
