Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model's unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method's superiority in group fairness enhancement and fairness-utility tradeoff.

翻译：成对学习策略是在隐式反馈数据上优化推荐模型的常用方法，该方法通常通过区分正样本（即用户点击的）和负样本（即通过负采样获得的）来学习用户偏好。然而，不同物品组（由物品属性指定）的规模通常分布不均。我们通过实验发现，成对算法（如BPR）常用的均匀负采样策略会继承这种数据偏差，并对多数物品组进行过度采样作为负实例，严重抵消了物品侧的群体公平性。本文提出了一种公平自适应负采样方法（FairNeg），通过在训练过程中自适应调整组级负采样分布来提升物品群体公平性。具体而言，该方法首先感知每一步模型中不公平状态，然后采用自适应动量更新策略调整组级采样分布，以更好地促进公平性优化。此外，我们提出了一种负采样分布混合机制，该机制能够巧妙融入现有用于挖掘信息负样本的重要性感知采样技术，从而同时实现多个优化目标。在四个公开数据集上的大量实验表明，所提方法在提升群体公平性以及公平性-效用权衡方面具有显著优势。