Learning from implicit feedback has become the standard paradigm for modern recommender systems. However, this setting is fraught with the persistent challenge of false negatives, where unobserved user-item interactions are not necessarily indicative of negative preference. To address this issue, this paper introduces a novel and principled loss function, named Corrected and Weighted (CW) loss, that systematically corrects for the impact of false negatives within the training objective. Our approach integrates two key techniques. First, inspired by Positive-Unlabeled learning, we debias the negative sampling process by re-calibrating the assumed negative distribution. By theoretically approximating the true negative distribution (p-) using the observable general data distribution (p) and the positive interaction distribution (p^+), our method provides a more accurate estimate of the likelihood that a sampled unlabeled item is truly negative. Second, we introduce a dynamic re-weighting mechanism that modulates the importance of each negative instance based on the model's current prediction. This scheme encourages the model to enforce a larger ranking margin between positive items and confidently predicted (i.e., easy) negative items, while simultaneously down-weighting the penalty on uncertain negatives that have a higher probability of being false negatives. A key advantage of our approach is its elegance and efficiency; it requires no complex modifications to the data sampling process or significant computational overhead, making it readily applicable to a wide array of existing recommendation models. Extensive experiments conducted on four large-scale, sparse benchmark datasets demonstrate the superiority of our proposed loss. The results show that our method consistently and significantly outperforms a suite of state-of-the-art loss functions across multiple ranking-oriented metrics.

翻译：从隐式反馈中学习已成为现代推荐系统的标准范式。然而，这一设定始终面临着假阴性问题的持续挑战，即未观测到的用户-物品交互并不一定代表负向偏好。为解决此问题，本文提出了一种新颖且具有理论依据的损失函数，称为校正加权损失，它系统性地在训练目标中校正假阴性的影响。我们的方法整合了两项关键技术。首先，受正例-未标记学习启发，我们通过重新校准假定的负例分布来消除负采样过程的偏差。通过使用可观测的总体数据分布与正交互分布从理论上近似真实的负例分布，我们的方法为采样的未标记物品是真正负例的可能性提供了更准确的估计。其次，我们引入了一种动态重加权机制，该机制根据模型当前预测来调节每个负例的重要性。此方案鼓励模型在正例与高置信度预测的负例之间强制更大的排序间隔，同时降低对那些更可能是假阴性的不确定负例的惩罚权重。我们方法的一个关键优势在于其简洁性与高效性；它无需对数据采样过程进行复杂修改或引入显著的计算开销，使其易于应用于广泛的现有推荐模型。在四个大规模稀疏基准数据集上进行的广泛实验证明了我们所提损失函数的优越性。结果表明，在多个面向排序的指标上，我们的方法始终显著优于一系列最先进的损失函数。