Online learning to rank (OL2R) optimizes the utility of returned search results based on implicit feedback gathered directly from users. To improve the estimates, OL2R algorithms examine one or more exploratory gradient directions and update the current ranker if a proposed one is preferred by users via an interleaved test. In this paper, we accelerate the online learning process by efficient exploration in the gradient space. Our algorithm, named as Null Space Gradient Descent, reduces the exploration space to only the \emph{null space} of recent poorly performing gradients. This prevents the algorithm from repeatedly exploring directions that have been discouraged by the most recent interactions with users. To improve sensitivity of the resulting interleaved test, we selectively construct candidate rankers to maximize the chance that they can be differentiated by candidate ranking documents in the current query; and we use historically difficult queries to identify the best ranker when tie occurs in comparing the rankers. Extensive experimental comparisons with the state-of-the-art OL2R algorithms on several public benchmarks confirmed the effectiveness of our proposal algorithm, especially in its fast learning convergence and promising ranking quality at an early stage.

本文介绍了一种名为Null Space Gradient Descent的算法，该算法通过高效地在梯度空间中进行探索，加快了在线学习排序（OL2R）算法的学习过程，并改进了交错测试的敏感性。 在公共基准测试中，与现有 OL2R 算法进行了广泛的实验比较，证明了该算法的有效性，尤其是其快速学习收敛和良好的排名质量。

在线学习排序的梯度空间有效探索