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School of Big Data and Software Engineering, Chongqing University
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LLMs can denoise sequential recommendations by disagreeing with the recommendation model itself, leading to more robust performance against noisy user data.
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.
