Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.

排名模型是信息检索（IR）研究的核心。在过去的几十年中，从传统的启发式方法，概率方法到现代的机器学习方法，已提出了各种构建排名模型的技术。近年来，随着深度学习技术的发展，我们看到了将浅层或深度神经网络应用于IR中的排名问题的工作量不断增加，本文将其称为神经排名模型。神经排序模型的能力在于能够从原始文本输入中学习排序问题的能力，从而避免了手工制作功能的许多限制。神经网络具有足够的能力来建模复杂的任务，这是处理排名中相关性估计的复杂性所必需的。既然提出了各种各样的神经排名模型，我们认为是总结当前状态，学习现有方法并为未来发展获得一些见识的恰当时机。与现有评论相反，在本次调查中，我们将从不同维度深入研究神经排名模型，以分析其基本假设，主要设计原则和学习策略。我们通过基准测试任务比较这些模型，以获得对现有技术的全面的经验理解。我们还将讨论当前文献中缺少的内容以及有希望的和期望的未来方向。与现有评论相反，在本次调查中，我们将从不同维度深入研究神经排名模型，以分析其基本假设，主要设计原则和学习策略。我们通过基准测试任务比较这些模型，以获得对现有技术的全面的经验理解。我们还将讨论当前文献中缺少的内容以及有希望和希望的未来方向。与现有评论相反，在本次调查中，我们将从不同维度深入研究神经排名模型，以分析其基本假设，主要设计原则和学习策略。我们通过基准测试任务比较这些模型，以获得对现有技术的全面的经验理解。我们还将讨论当前文献中缺少的内容以及有希望的和期望的未来方向。