Recommendation systems often rely on implicit feedback, where only positive user-item interactions can be observed. Negative sampling is therefore crucial to provide proper negative training signals. However, existing methods tend to mislabel potentially positive but unobserved items as negatives and lack precise control over negative sample selection. We aim to address these by generating controllable negative samples, rather than sampling from the existing item pool. In this context, we propose Adaptive Diffusion-based Augmentation for Recommendation (ADAR), a novel and model-agnostic module that leverages diffusion to synthesize informative negatives. Inspired by the progressive corruption process in diffusion, ADAR simulates a continuous transition from positive to negative, allowing for fine-grained control over sample hardness. To mine suitable negative samples, we theoretically identify the transition point at which a positive sample turns negative and derive a score-aware function to adaptively determine the optimal sampling timestep. By identifying this transition point, ADAR generates challenging negative samples that effectively refine the model's decision boundary. Experiments confirm that ADAR is broadly compatible and boosts the performance of existing recommendation models substantially, including collaborative filtering and sequential recommendation, without architectural modifications.

翻译：推荐系统通常依赖于隐式反馈，其中仅能观测到正向的用户-物品交互。因此，负采样对于提供恰当的负向训练信号至关重要。然而，现有方法倾向于将潜在正向但未被观测到的物品误标为负样本，且缺乏对负样本选择的精确控制。我们旨在通过生成可控的负样本来解决这些问题，而非从现有物品池中采样。在此背景下，我们提出基于扩散的自适应增强推荐方法（ADAR），这是一种新颖且与模型无关的模块，利用扩散模型合成信息丰富的负样本。受扩散过程中渐进式破坏过程的启发，ADAR模拟了从正向到负向的连续过渡，从而实现对样本难度的细粒度控制。为挖掘合适的负样本，我们从理论上识别了正向样本转变为负向的过渡点，并推导出一个基于分数的函数来自适应地确定最优采样时间步。通过识别该过渡点，ADAR生成具有挑战性的负样本，从而有效优化模型的决策边界。实验证实，ADAR具有广泛的兼容性，无需修改架构即可显著提升现有推荐模型的性能，包括协同过滤和序列推荐模型。