Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial is to give an introduction to this research direction. Specifically, the existing learning-to-rank algorithms are reviewed and categorized into three approaches: the pointwise, pairwise, and listwise approaches. The advantages and disadvantages with each approach are analyzed, and the relationships between the loss functions used in these approaches and IR evaluation measures are discussed. Then the empirical evaluations on typical learning-to-rank methods are shown, with the LETOR collection as a benchmark dataset, which seems to suggest that the listwise approach be the most effective one among all the approaches. After that, a statistical ranking theory is introduced, which can describe different learning-to-rank algorithms, and be used to analyze their query-level generalization abilities. At the end of the tutorial, we provide a summary and discuss potential future work on learning to rank.

学习信息检索（IR）的排名是一项使用训练数据自动构建排名模型的任务，以便该模型可以根据新对象的相关程度，偏好或重要性对其进行排序。本质上，许多IR问题都是排名问题，并且可以通过使用学习排名技术来潜在地增强许多IR技术。本教程的目的是介绍此研究方向。具体而言，对现有的学习排名算法进行了回顾并归类为三种方法：逐点，成对和列表方法。分析了每种方法的优缺点，并讨论了这些方法中使用的损失函数与IR评估方法之间的关系。然后，以LETOR集合为基准数据集，显示了对典型学习排名方法的经验评估，这似乎表明按列表列出的方法是所有方法中最有效的一种。之后，介绍了一种统计排名理论，该理论可以描述不同的学习排名算法，并可以用来分析其查询级别的泛化能力。在本教程的最后，我们提供了摘要，并讨论了未来学习排名的潜在工作。并用于分析其查询级别的泛化能力。在本教程的最后，我们提供了摘要，并讨论了未来学习排名的潜在工作。并用于分析其查询级别的泛化能力。在本教程的最后，我们提供了摘要，并讨论了未来学习排名的潜在工作。