Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance is also a vital determinant of success for personalized recommender systems to reduce the risk of losing users with low satisfaction. This study introduces a "safe" collaborative filtering method that prioritizes recommendation quality for less-satisfied users rather than focusing on the average performance. Our approach minimizes the conditional value at risk (CVaR), which represents the average risk over the tails of users' loss. To overcome computational challenges for web-scale recommender systems, we develop a robust yet practical algorithm that extends the most scalable method, implicit alternating least squares (iALS). Empirical evaluation on real-world datasets demonstrates the excellent tail performance of our approach while maintaining competitive computational efficiency.

翻译：卓越的尾部性能对于现代机器学习任务至关重要，例如算法公平性、类别不平衡和风险敏感决策，因为它确保了数据集中困难样本的有效处理。尾部性能也是个性化推荐系统成功的关键决定因素，有助于降低低满意度用户流失的风险。本研究引入了一种“安全”的协同过滤方法，该方法优先提升低满意度用户的推荐质量，而非关注平均性能。我们的方法最小化条件风险价值（CVaR），它表示用户损失尾部的平均风险。为克服网络规模推荐系统的计算挑战，我们开发了一种鲁棒且实用的算法，该算法扩展了最具可扩展性的方法——隐式交替最小二乘法（iALS）。在真实数据集上的实证评估表明，我们的方法在保持竞争性计算效率的同时，实现了卓越的尾部性能。