  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.
