In information retrieval, learning to rank was originally proposed for ranking retrieved documents according to their relevance by machine learning techniques. In general, it can effectively improve the performance of critical ranking tasks, where the listwise approach is one of the most popular approaches adopted for ranking. However, considering that documents with more relevance receive greater attention, current ranking function based listwise approaches discriminate correctly ranked sequences with incorrect sequences; however, they are incapable of recognizing which incorrect sequences are relatively more satisfied. In this study, we propose a listwise approach based on an extreme order sensitive constraint to overcome the aforementioned drawback. We first define this constraint and then design a novel listwise loss function, ListXOS, based on the constraint via cross-correntropy to improve the performance of ranking tasks. Experimental results on three public datasets present improved performance of learning to rank by 6% compared with conventional methods, which demonstrate the superiority of the proposed approach over related state-of-the-art approaches.

中文翻译：

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Listwise 学习通过交叉相关熵对极端顺序敏感约束进行排序

在信息检索中，学习排序最初是为了通过机器学习技术根据检索到的文档的相关性对它们进行排序。总的来说，它可以有效地提高关键排序任务的性能，其中 listwise 方法是最流行的排序方法之一。然而，考虑到具有更高相关性的文档受到更多关注，当前基于排序函数的列表方法将正确排序的序列与不正确的序列区分开来；然而，他们无法识别哪些不正确的序列相对更令人满意。在这项研究中，我们提出了一种基于极端顺序敏感约束的列表方法来克服上述缺点。我们首先定义这个约束，然后设计一个新的列表损失函数，ListXOS，基于通过交叉相关熵的约束来提高排序任务的性能。在三个公共数据集上的实验结果表明，与传统方法相比，学习排名的性能提高了 6%，这证明了所提出的方法优于相关的最新方法。