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.

翻译：流行度偏差是推荐系统中长期存在的问题，对公平性和效率均构成挑战。现有文献普遍认为，减少流行度偏差往往需要牺牲推荐准确度。本文对这一普遍认知提出质疑。基于通用偏差-方差分解框架的分析表明，在一定条件下减少偏差实际上可以提升模型性能。为实现这一双赢目标，我们提出通过负采样干预模型训练，进而修正模型预测结果。具体而言，我们提供了一种最优负采样规则，在最大化部分AUC以保持任意给定模型准确度的同时，通过修正样本信息与先验信息，以灵活且系统的方式减少流行度偏差。在真实数据集上的实验结果表明，本方法在提升推荐性能与降低流行度偏差方面均具有显著优势。