  Popularity bias is a persistent issue associated with recommendation systems,
posing challenges to both fairness and efficiency. Existing literature widely
acknowledges that reducing popularity bias often requires sacrificing
recommendation accuracy. In this paper, we challenge this commonly held belief.
Our analysis under general bias-variance decomposition framework shows that
reducing bias can actually lead to improved model performance under certain
conditions. To achieve this win-win situation, we propose to intervene in model
training through negative sampling thereby modifying model predictions.
Specifically, we provide an optimal negative sampling rule that maximizes
partial AUC to preserve the accuracy of any given model, while correcting
sample information and prior information to reduce popularity bias in a
flexible and principled way. Our experimental results on real-world datasets
demonstrate the superiority of our approach in improving recommendation
performance and reducing popularity bias.
