Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

排名模型是信息检索系统的主要组成部分。几种排名方法基于使用一组手工制作的特征的传统机器学习算法。最近，研究人员在信息检索中利用了深度学习模型。这些模型经过端到端训练，可以从原始数据中提取特征用于排序任务，从而克服手工制作特征的局限性。已经提出了多种深度学习模型，每个模型都提供了一组神经网络组件来提取用于排名的特征。在本文中，我们从不同维度比较了文献中提出的模型，以了解每个模型的主要贡献和局限性。在我们对文献的讨论中，我们分析了有前途的神经组件，并提出未来的研究方向。我们还展示了文档检索和其他检索任务之间的类比，其中要排序的项目是结构化文档、答案、图像和视频。