SNR as a Supervisory Coordinate: Coarse-to-Fine Learning for Diffusion-based Recommendation

Diffusion models have emerged as a powerful paradigm for sequential recommendation (SR) due to their ability to model user preferences as complex, multi-modal distributions. However, existing frameworks typically rely on static, point-wise supervision throughout the denoising trajectory. Such constant-resolution objectives are misaligned with the time-varying information that the noisy state retains about the target. Specifically, enforcing a precise point target in high-noise regimes leads to supervisory over-specification, forcing the model to resolve distinctions that exceed the recoverable information. To address this, we propose CloudDiff, a diffusion framework for SR that adaptively modulates its supervisory granularity according to the signal-to-noise ratio (SNR). CloudDiff introduces two SNR-coupled mechanisms: (1) a semantic cloud loss that aligns the denoiser's intermediate representations with set-valued targets of semantic neighbors, with their influence gated by the noise level; and (2) a log-SNR-coupled temperature schedule that dynamically adjusts the sharpness of the contrastive distribution. We provide an information-theoretic foundation showing that both axes of CloudDiff are governed by a single coordinate, log-SNR, jointly motivating the proposed coarse-to-fine scheme. Extensive experiments on multiple benchmarks demonstrate that CloudDiff consistently outperforms state-of-the-art baselines, with detailed analyses revealing how SNR-adaptive supervision stabilizes the refinement trajectory.