Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead learning processes, reducing both recommendation accuracy and platform value. Existing denoising strategies typically overlook the entity-specific nature of noise while introducing high computational costs and complex hyperparameter tuning. To address these challenges, we propose \textbf{EARD} (\textbf{E}ntity-\textbf{A}ware \textbf{R}eliability-\textbf{D}riven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability. Motivated by the empirical observation that training loss correlates with noise, EARD quantifies user and item reliability via their average training losses as a proxy for reputation, and integrates these entity-level factors with interaction-level confidence. The framework is \textbf{model-agnostic}, \textbf{computationally efficient}, and requires \textbf{only two intuitive hyperparameters}. Extensive experiments across multiple datasets and backbone models demonstrate that EARD yields substantial improvements over state-of-the-art baselines (e.g., up to 27.01\% gain in NDCG@50), while incurring negligible additional computational cost. Comprehensive ablation studies and mechanism analyses further confirm EARD's robustness to hyperparameter choices and its practical scalability. These results highlight the importance of entity-aware reliability modeling for denoising implicit feedback and pave the way for more robust recommendation research.

翻译：隐式反馈是现代推荐系统的核心，但其本质上是含噪声的，通常会损害模型训练并降低用户体验。在大规模场景下，此类噪声会误导学习过程，降低推荐准确性和平台价值。现有的去噪策略通常忽略了噪声的实体特异性，同时引入了高昂的计算成本和复杂的超参数调优。为应对这些挑战，我们提出 \textbf{EARD}（\textbf{E}ntity-\textbf{A}ware \textbf{R}eliability-\textbf{D}riven Denoising），一种轻量级框架，将焦点从交互级信号转向实体级可靠性。受训练损失与噪声相关的实证观察启发，EARD 通过用户和项目的平均训练损失来量化其可靠性，以此作为声誉的代理指标，并将这些实体级因素与交互级置信度相融合。该框架具有 \textbf{模型无关性}、\textbf{计算高效性}，且仅需 \textbf{两个直观的超参数}。在多个数据集和骨干模型上的广泛实验表明，EARD 相比最先进的基线方法取得了显著提升（例如，NDCG@50 最高提升 27.01%），同时仅带来可忽略的额外计算成本。全面的消融研究和机制分析进一步证实了 EARD 对超参数选择的鲁棒性及其实际可扩展性。这些结果凸显了实体感知的可靠性建模对于隐式反馈去噪的重要性，并为更鲁棒的推荐研究铺平了道路。