User historical interaction data is the primary signal for learning user preferences in collaborative filtering (CF). However, the training data often exhibits a long-tailed distribution, where only a few items have the majority of interactions. CF models trained directly on such imbalanced data are prone to learning popularity bias, which reduces personalization and leads to suboptimal recommendation quality. Graph Neural Networks (GNNs), while effective for CF due to their message passing mechanism, can further propagate and amplify popularity bias through their aggregation process. Existing approaches typically address popularity bias by modifying training objectives but fail to directly counteract the bias propagated during GNN's neighborhood aggregation. Applying weights to interactions during aggregation can help alleviate this problem, yet it risks distorting model learning due to unstable node representations in the early stages of training. In this paper, we propose a Post-hoc Popularity Debiasing (PPD) method that corrects for popularity bias in GNN-based CF and operates directly on pre-trained embeddings without requiring retraining. By estimating interaction-level popularity and removing popularity components from node representations via a popularity direction vector, PPD reduces bias while preserving user preferences. Experimental results show that our method outperforms state-of-the-art approaches for popularity bias correction in GNN-based CF.

翻译：用户历史交互数据是协同过滤中学习用户偏好的主要信号。然而，训练数据通常呈现长尾分布，即少数项目占据了绝大多数交互。直接在此类不平衡数据上训练的协同过滤模型容易学习到流行度偏差，这会降低个性化程度并导致次优的推荐质量。图神经网络虽因其消息传递机制在协同过滤中表现有效，但会通过其聚合过程进一步传播并放大流行度偏差。现有方法通常通过修改训练目标来解决流行度偏差，但未能直接抵消图神经网络邻域聚合过程中传播的偏差。在聚合过程中对交互施加权重有助于缓解此问题，但由于训练早期节点表示不稳定，这种方法可能扭曲模型学习。本文提出一种后置流行度去偏方法，该方法可直接在预训练嵌入上操作而无需重新训练，从而校正基于图神经网络的协同过滤中的流行度偏差。通过估计交互级流行度并借助流行度方向向量从节点表示中移除流行度分量，该方法能在保留用户偏好的同时减少偏差。实验结果表明，本方法在基于图神经网络的协同过滤的流行度偏差校正任务上优于现有最优方法。