Implicit feedback is the dominant data source for recommender systems, but behavioral logs are often contaminated by false-positive interactions caused by mis-clicks, biased exposure, and interface effects. Denoising recommendation methods improve robustness by down-weighting or filtering interactions suspected to be noisy, often relying on the small-loss heuristic. We revisit this heuristic through the lens of popularity bias. Tail-item positives can be harder to fit because they are sparsely observed, and thus may receive larger losses even when they reflect genuine user preference. Under such popularity-dependent loss patterns, monotone loss-based reweighting can suppress clean-but-hard tail signals and increase the head-tail imbalance in effective supervision. We formalize this interaction through the effective head-tail signal ratio induced by denoising weights and derive a conditional reallocation result: when the loss distribution of tail positives is right-shifted relative to that of head positives, small-loss reweighting increases the effective head-tail signal ratio compared with ERM. Motivated by this analysis, we propose Popularity-Aware Denoising (PAD), a lightweight plug-in framework that modulates denoising strength by item popularity. PAD applies stronger denoising to highly exposed items while being more conservative on tail items, preserving more clean-but-hard long-tail signals. Experiments on three datasets and three backbones show that PAD generally improves over representative denoising baselines and provides favorable accuracy-diversity tradeoffs, especially on MF-style recommenders.

翻译：隐式反馈是推荐系统的主要数据来源，但行为日志常因误点击、暴露偏差和界面效应而掺杂伪正交互。推荐去噪方法通过基于小损失启发式策略降低或过滤疑似噪声交互的权重来提升鲁棒性。我们从流行度偏差视角重新审视该启发式策略。尾部正样本因观测稀疏而更难拟合，即使反映真实用户偏好也可能获得较大损失。在这种依赖流行度的损失分布模式下，基于单调损失的加权会抑制干净但困难的尾部信号，加剧有效监督中的头部-尾部不平衡。我们通过去噪权重导出的有效头尾信号比形式化这种交互，并推导出条件重分配结论：当尾部正样本的损失分布相对头部正样本右移时，小损失加权相比经验风险最小化会增加有效头尾信号比。基于此分析，我们提出流行度感知去噪（PAD）——一种轻量级即插即用框架，能根据物品流行度调节去噪强度。PAD对高曝光物品施加更强去噪，同时保持对尾部物品的保守处理，从而保留更多干净但困难的长尾信号。在三个数据集和三种骨干网络上的实验表明，PAD能普遍优于代表性去噪基线，并在准确性-多样性权衡方面展现优势，尤其在基于矩阵分解的推荐器上。