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Allocating latent refinement steps based on information gain can significantly boost generative recommendation accuracy while optimizing computational resources.
By disentangling shared account behavior in the frequency domain, DisenReason dynamically infers latent users, boosting recommendation accuracy by up to 12.56% compared to methods assuming a fixed number of users.
