  Recommender systems learn from historical user-item interactions to identify
preferred items for target users. These observed interactions are usually
unbalanced following a long-tailed distribution. Such long-tailed data lead to
popularity bias to recommend popular but not personalized items to users. We
present a gradient perspective to understand two negative impacts of popularity
bias in recommendation model optimization: (i) the gradient direction of
popular item embeddings is closer to that of positive interactions, and (ii)
the magnitude of positive gradient for popular items are much greater than that
of unpopular items. To address these issues, we propose a simple yet efficient
framework to mitigate popularity bias from a gradient perspective.
Specifically, we first normalize each user embedding and record accumulated
gradients of users and items via popularity bias measures in model training. To
address the popularity bias issues, we develop a gradient-based embedding
adjustment approach used in model testing. This strategy is generic,
model-agnostic, and can be seamlessly integrated into most existing recommender
systems. Our extensive experiments on two classic recommendation models and
four real-world datasets demonstrate the effectiveness of our method over
state-of-the-art debiasing baselines.
