In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative sampling, as these false negatives can provide incorrect information and mislead the model learning. To date, only a small number of studies have been committed to solve the false negative problem, primarily focusing on designing sophisticated sampling algorithms to filter false negatives. In contrast, this paper shifts its focus to refining the loss function. We find that the original Bayesian Personalized Ranking (BPR), initially designed for uniform negative sampling, is inadequate in adapting to hard sampling scenarios. Hence, we introduce an enhanced Bayesian Personalized Ranking objective, named as Hard-BPR, which is specifically crafted for dynamic hard negative sampling to mitigate the influence of false negatives. This method is simple yet efficient for real-world deployment. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness and robustness of our approach, along with the enhanced ability to distinguish false negatives.

翻译：在隐式协同过滤中，困难负样本挖掘技术被开发用于加速和提升推荐模型的学习效果。然而，困难负采样中假阴性样本的无意选择仍是一个主要问题，因为这些假阴性会提供错误信息并误导模型学习。迄今为止，仅有少量研究致力于解决假阴性问题，主要集中于设计复杂的采样算法来过滤假阴性。相比之下，本文创新性地将焦点转向损失函数的改进。我们发现最初为均匀负采样设计的原始贝叶斯个性化排序(BPR)无法适应困难采样场景。为此，我们提出一种增强型贝叶斯个性化排序目标函数——Hard-BPR，该函数专为动态困难负采样设计以减轻假阴性影响。该方法简洁高效，便于实际部署。在三个真实数据集上开展的大量实验证明了我们方法的有效性和鲁棒性，以及增强的假阴性区分能力。