SERank：使用挤压和激励网络优化顺序学习排序
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network
摘要：
学习排序（LTR）是一组有监督的机器学习算法，旨在生成一系列项目的最佳排序顺序。在过去的几十年里，人们对许多排序模型进行了研究。大多数算法在训练和推理过程中对每个查询文档对进行独立处理。近年来，人们提出了一些基于多变量评分函数或上下文嵌入的方法，这些方法主要是通过挖掘候选列表中的信息来进行改进。然而，这些方法通常会大大增加在线推理过程中的计算成本，特别是当在现实世界的Web搜索系统中的大规模的候选人。更重要的是，很少有研究关注模型结构的新颖设计，以利用信息对候选人进行排名。在这项工作中，我们提出了一个有效的和高效的方法命名为SERank，这是一个顺序排序模型，利用挤压和激励网络，以利用跨文档的信息。此外，我们研究我们提出的方法在几个公共基准数据集，以及从商业问答搜索引擎，知乎收集的点击日志。此外，我们还在知乎搜索引擎上进行了在线A/B测试，以进一步验证所提出的方法。离线数据集和在线A/B测试的结果表明，我们的方法有助于显着改善。
英文摘要：
Learning-to-rank (LTR) is a set of supervised machine learning algorithms that aim at generating optimal ranking order over a list of items. A lot of ranking models have been studied during the past decades. And most of them treat each query document pair independently during training and inference. Recently, there are a few methods have been proposed which focused on mining information across ranking candidates list for further improvements, such as learning multivariant scoring function or learning contextual embedding. However, these methods usually greatly increase computational cost during online inference, especially when with large candidates size in real-world web search systems. What's more, there are few studies that focus on novel design of model structure for leveraging information across ranking candidates. In this work, we propose an effective and efficient method named as SERank which is a Sequencewise Ranking model by using Squeeze-and-Excitation network to take advantage of cross-document information. Moreover, we examine our proposed methods on several public benchmark datasets, as well as click logs collected from a commercial Question Answering search engine, Zhihu. In addition, we also conduct online A/B testing at Zhihu search engine to further verify the proposed approach. Results on both offline datasets and online A/B testing demonstrate that our method contributes to a significant improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
