Efficient Exploration of Gradient Space for Online Learning to Rank
Efficient Exploration of Gradient Space for Online Learning to Rank
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DOI：
10.1145/3209978.3210045
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发表时间：
2018-05
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影响因子：
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通讯作者：
Huazheng Wang-;Ramsey Langley;Sonwoo Kim;Eric McCord-Snook;Hongning Wang
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作者：
Huazheng Wang-;Ramsey Langley;Sonwoo Kim;Eric McCord-Snook;Hongning Wang
在线学习排序（OL2R）基于直接从用户那里收集的隐式反馈优化了返回的搜索结果的效用。在本文中，我们通过对梯度空间的有效探索来加速在线学习过程。我们的算法被命名为零空间梯度下降，将探索空间减少到只有最近表现不佳的梯度的零空间。这可以防止算法反复探索最近与用户交互所阻碍的方向。为了提高结果交错测试的灵敏度，我们有选择地构建候选排序器，以最大限度地提高候选排序器在当前查询中被候选排序文档区分的机会；在比较排名时，我们使用历史困难查询来识别最佳排名。在几个公共基准上与最先进的OL2R算法进行了广泛的实验比较，证实了我们的提议算法的有效性，特别是在其快速学习收敛和早期有希望的排名质量方面。
Online learning to rank (OL2R) optimizes the utility of returned search results based on implicit feedback gathered directly from users. 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 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.