As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.

翻译：学习排序方法主要致力于提升排序质量，其输出分数在设计上未进行尺度校准。这从根本上限制了排序方法在分数敏感型应用中的使用。尽管结合回归目标和排序目标的简单多目标方法能有效学习尺度校准分数，但我们认为这两个目标未必兼容，导致两者间的权衡难以达到理想效果。本文提出一种实用的回归兼容排名方法，该方法能实现更优的权衡，并证明回归与排序两个组件相互对齐。尽管相同思路适用于二值和分级相关性排序场景，但本文主要聚焦二值标签。我们在多个公开学习排序基准上评估所提方法，结果表明其在回归指标和排序指标上持续取得最优或竞争性结果，并在多目标优化中显著改善帕累托前沿。此外，我们在YouTube搜索上评估该方法，发现它不仅提升了生产环境pCTR模型的排序质量，还提高了点击预测准确性。该方案已成功部署于YouTube生产系统。