Optimization metrics are crucial for building recommendation systems at scale. However, an effective and efficient metric for practical use remains elusive. While Top-K ranking metrics are the gold standard for optimization, they suffer from significant computational overhead. Alternatively, the more efficient accuracy and AUC metrics often fall short of capturing the true targets of recommendation tasks, leading to suboptimal performance. To overcome this dilemma, we propose a new optimization metric, Lower-Left Partial AUC (LLPAUC), which is computationally efficient like AUC but strongly correlates with Top-K ranking metrics. Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback. We further design an efficient point-wise recommendation loss to maximize LLPAUC and evaluate it on three datasets, validating its effectiveness and robustness.

翻译：优化指标对于大规模构建推荐系统至关重要。然而，在实际应用中，一种既有效又高效的指标仍难以实现。尽管Top-K排序指标是优化的黄金标准，但其计算开销显著。相比之下，更高效的准确率和AUC指标往往无法捕捉推荐任务的真实目标，导致性能次优。为克服这一困境，我们提出了一种新的优化指标——左下角部分AUC（LLPAUC），该指标既具备AUC的计算高效性，又与Top-K排序指标高度相关。与AUC相比，LLPAUC仅考虑ROC曲线左下角的部分区域，从而将优化焦点集中于Top-K项。我们从理论上验证了LLPAUC与Top-K排序指标之间的相关性，并证明了其对用户噪声反馈的鲁棒性。我们进一步设计了一种高效的点式推荐损失以最大化LLPAUC，并在三个数据集上评估了其有效性和鲁棒性。