Training sequential recommenders such as SASRec with uniform sample weights achieves good overall performance but can fall short on specific user groups. One such example is popularity bias, where mainstream users receive better recommendations than niche content viewers. To improve recommendation quality across diverse user groups, we explore three Distributionally Robust Optimization(DRO) methods: Group DRO, Streaming DRO, and Conditional Value at Risk (CVaR) DRO. While Group and Streaming DRO rely on group annotations and struggle with users belonging to multiple groups, CVaR does not require such annotations and can naturally handle overlapping groups. In experiments on two real-world datasets, we show that the DRO methods outperform standard training, with CVaR delivering the best results. Additionally, we find that Group and Streaming DRO are sensitive to the choice of group used for loss computation. Our contributions include (i) a novel application of CVaR to recommenders, (ii) showing that the DRO methods improve group metrics as well as overall performance, and (iii) demonstrating CVaR's effectiveness in the practical scenario of intersecting user groups.

翻译：使用均匀样本权重训练SASRec等序列推荐模型虽能获得良好的整体性能，但在特定用户群体上可能表现不足。一个典型例子是流行度偏差，即主流用户获得的推荐质量优于小众内容观看者。为提升跨多样化用户群体的推荐质量，我们探索了三种分布鲁棒优化方法：群体DRO、流式DRO以及条件风险价值DRO。虽然群体DRO与流式DRO依赖群体标注且难以处理多归属用户，CVaR方法无需此类标注并能自然处理重叠群体。在两个真实数据集上的实验表明，DRO方法均优于标准训练，其中CVaR取得最佳效果。此外，我们发现群体DRO与流式DRO对损失计算所选取的群体类别具有敏感性。本文贡献包括：（i）首次将CVaR应用于推荐系统，（ii）证明DRO方法在提升群体指标的同时改善整体性能，（iii）在用户群体交叉的实际场景中验证CVaR的有效性。