Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences. However, most current debiasing strategies are prone to playing a trade-off game between head and tail performance, thus inevitably degrading the overall recommendation accuracy. To reduce the negative impact of popularity bias on CF models, we incorporate Bias-aware margins into Contrastive loss and propose a simple yet effective BC Loss, where the margin tailors quantitatively to the bias degree of each user-item interaction. We investigate the geometric interpretation of BC loss, then further visualize and theoretically prove that it simultaneously learns better head and tail representations by encouraging the compactness of similar users/items and enlarging the dispersion of dissimilar users/items. Over eight benchmark datasets, we use BC loss to optimize two high-performing CF models. On various evaluation settings (i.e., imbalanced/balanced, temporal split, fully-observed unbiased, tail/head test evaluations), BC loss outperforms the state-of-the-art debiasing and non-debiasing methods with remarkable improvements. Considering the theoretical guarantee and empirical success of BC loss, we advocate using it not just as a debiasing strategy, but also as a standard loss in recommender models.

翻译：协同过滤（CF）模型易受流行度偏差影响，导致推荐偏离用户真实偏好。然而，当前多数去偏策略需在头部与尾部性能间进行权衡，不可避免地降低了整体推荐精度。为减轻流行度偏差对CF模型的负面影响，我们将偏差感知边界融入对比损失，提出一种简洁高效的BC损失函数，其中边界根据每个用户-物品交互的偏差程度定量调整。我们研究了BC损失的几何解释，并通过可视化和理论证明，该方法通过增强相似用户/物品的紧凑性并扩大相异用户/物品的离散性，同时优化了头部与尾部的表征学习。在八个基准数据集上，我们将BC损失应用于两个高性能CF模型的优化。在多种评估设置（即不平衡/平衡、时间切分、全观测无偏、尾部/头部测试评估）下，BC损失均以显著提升优于当前最先进的去偏与非去偏方法。鉴于BC损失的理论保障与实验成功，我们主张不仅将其作为去偏策略，更应作为推荐模型中的标准损失函数。