Recommender systems often suffer from popularity bias, where frequently interacted items are overrepresented in recommendations. This bias stems from propensity factors influencing training data, leading to imbalanced exposure. In this paper, we introduce a Fair Sampling (FS) approach to address this issue by ensuring that both users and items are selected with equal probability as positive and negative instances. Unlike traditional inverse propensity score (IPS) methods, FS does not require propensity estimation, eliminating errors associated with inaccurate calculations. Our theoretical analysis demonstrates that FS effectively neutralizes the influence of propensity factors, achieving unbiased learning. Experimental results validate that FS outperforms state-of-the-art methods in both point-wise and pair-wise recommendation tasks, enhancing recommendation fairness without sacrificing accuracy. The implementation is available at https://anonymous.4open.science/r/Fair-Sampling.

本研究解决了推荐系统中常见的流行偏见问题，该偏见导致频繁互动项目在推荐中过度代表。提出了一种公平取样方法，确保用户和物品以相等概率被选为正负实例，避免了传统方法中对倾向估计的依赖。实验结果表明，该方法在增强推荐公平性方面超过了现有最先进的技术，同时保持了推荐的准确性。

通过公平取样减轻协同过滤中的流行偏见