Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. Thus, the objective we would like to optimize in such scenarios is typically a globaln on-decomposable one such as the area under the precision-recall curve, the $F_\beta$ score, precision at fixed recall, etc. In practice, due to the scalability limitations of existing approaches for optimizing such objectives, large-scale systems are trained to maximize classification accuracy, in the hope that performance as measured via the true objective will also be favorable. In this work we present a unified framework that, using straightforward building block bounds, allows for highly scalable optimization of a wide range of ranking-based objectives. We demonstrate the advantage of our approach on several real-life retrieval problems that are significantly larger than those considered in the literature, while achieving substantial improvement in performance over the accuracy-objective baseline.

本研究提出了一种统一的框架，使用简单的构建块限制，允许对各种基于排名的目标进行高度可扩展的优化，并在多个实际检索问题上展示了我们方法的优势，同时在性能和精度方面显著改进了基线。

可扩展的学习非可分解目标