The interaction data used for training recommender systems often exhibit a long-tail distribution. Such highly imbalanced data distribution results in an unfair learning process among items. Contrastive learning alleviates the above issue by data augmentation. However, it lacks consideration of the significant disparity in popularity between items and may even introduce false negatives during the data augmentation, misleading user preference prediction. To address this issue, we combine contrastive learning with a weighted model for negative validation. By penalizing identified false negatives during training, we limit their potential harm within the training process. Meanwhile, to tackle the scarcity of supervision signals for unpopular items, we design Popularity Associated Modeling to mine the correlation among items. Then we guide unpopular items to learn hidden features favored by specific users from their associated popular items, which provides effective supplementary information for their representation modeling. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms state-of-the-art baselines in recommendation performance, with Recall@20 improvements of 4.2%, 2.4% and 3.6% across the datasets, but also shows significant effectiveness in relieving popularity bias.

用于训练推荐系统的交互数据通常表现出长尾分布。这种高度不平衡的数据分布会导致项目之间的学习过程不公平。对比学习通过数据增强缓解了上述问题。然而，它缺乏对项目之间受欢迎程度的显著差异的考虑，甚至可能在数据增强过程中引入假阴性，误导用户偏好预测。为了解决这个问题，我们将对比学习与加权模型相结合进行否定验证。通过在训练期间惩罚已识别的假阴性，我们限制了它们在训练过程中的潜在危害。同时，为了解决不受欢迎商品监管信号的稀缺问题，我们设计了 Popularity Associated Modeling 来挖掘商品之间的相关性。然后，引导不受欢迎的项目从其关联的热门项目中学习特定用户喜欢的隐藏特征，为他们的表示建模提供有效的补充信息。在三个真实数据集上的广泛实验表明，我们提出的模型在推荐性能方面优于最先进的基线，整个数据集的Recall@20提高了 4.2%、2.4% 和 3.6%，但也显示出在缓解流行偏差方面的显着有效性。