Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias -- the tendency to over-recommend popular items -- resulting in less personalization, unfair exposure and lower recommendation diversity. Current solutions address popularity bias through different stages of the recommendation pipeline, including pre-processing methods that may distort data distributions, in-processing approaches which can complicate optimization, and post-processing techniques that are limited in correcting bias already embedded in the learned representations. To address these limitations, we propose PBiLoss, a novel regularization-based loss function designed to explicitly counteract popularity bias in graph-based recommenders. PBiLoss augments traditional training objectives by penalizing the model's inclination toward popular items, thereby encouraging the recommendation of less popular but potentially more personalized content. We introduce two sampling strategies -- Popular Positive (PopPos) and Popular Negative (PopNeg) -- and explore two methods to distinguish popular items -- one based on a fixed popularity threshold and another without any threshold -- making the approach flexible and adaptive. Our proposed method is model-agnostic and can be seamlessly integrated into state-of-the-art graph-based frameworks such as LightGCN and its variants. Extensive experiments carried out on datasets including Epinions, iFashion, and MovieLens highlight the advantages of the PBiLoss for enhancing fairness in recommendations, decreasing PRU and PRI by up to 10\%, compared to other baseline models, while maintaining accuracy and other standard metrics intact in the process.

翻译：基于图神经网络（GNN）的推荐系统已被证明在用户-物品交互任务中表现良好。然而，这些系统普遍存在流行度偏差——倾向于过度推荐流行物品——导致个性化程度降低、曝光不公平以及推荐多样性下降。现有解决方案通过推荐管道的不同阶段应对流行度偏差，包括可能扭曲数据分布的预处理方法、可能使优化复杂化的处理中方法，以及因无法修正学习表示中已嵌入偏差而效果有限的后处理方法。为克服这些局限，我们提出PBiLoss，一种新型基于正则化的损失函数，旨在显式抵消图推荐系统中的流行度偏差。PBiLoss通过惩罚模型对流行物品的倾向来增强传统训练目标，从而鼓励推荐较少流行但可能更具个性化的内容。我们引入两种采样策略——流行正样本（PopPos）与流行负样本（PopNeg）——并探索两种区分流行物品的方法：一种基于固定流行度阈值，另一种无阈值，使该方法灵活且自适应。所提方法不依赖特定模型，可无缝集成至LightGCN及其变体等前沿图推荐框架中。在Epinions、iFashion和MovieLens等数据集上开展的广泛实验表明，与基线模型相比，PBiLoss在保持准确性及其他标准指标不变的前提下，可将PRU与PRI指标最多降低10%，凸显其在提升推荐公平性方面的优势。