通过均衡不同物品的损失，减轻推荐系统中的热门偏差。
Correcting Popularity Bias in Recommender Systems via Item Loss Equalization
- 在训练中引入损失均衡项，降低热门与冷门物品的损失差异。
- 实验显示该方法显著缓解了推荐不公，准确率下降可忽略。
- 适合关注公平性、需提升长尾物品曝光的研究者和工程师。
推荐系统常受热门偏差影响，少数热门物品因交互频繁主导推荐结果，导致小众用户兴趣被忽视，造成用户间不公平及推荐质量差异。本文提出一种训练过程干预方法，借鉴机器学习中的公平经验风险最小化思想，在推荐模型目标函数中增加一项，旨在最小化不同物品组间的损失差异。在两个真实数据集上的大量实验表明，该方法能有效缓解热门偏差带来的不公平，同时仅带来可忽略的推荐准确率损失。
原文摘要 · Abstract (English)
Recommender Systems (RS) often suffer from popularity bias, where a small set of popular items dominate the recommendation results due to their high interaction rates, leaving many less popular items overlooked. This phenomenon disproportionately benefits users with mainstream tastes while neglecting those with niche interests, leading to unfairness among users and exacerbating disparities in recommendation quality across different user groups. In this paper, we propose an in-processing approach to address this issue by intervening in the training process of recommendation models. Drawing inspiration from fair empirical risk minimization in machine learning, we augment the objective function of the recommendation model with an additional term aimed at minimizing the disparity in loss values across different item groups during the training process. Our approach is evaluated through extensive experiments on two real-world datasets and compared against state-of-the-art baselines. The results demonstrate the superior efficacy of our method in mitigating the unfairness of popularity bias while incurring only negligible loss in recommendation accuracy.
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