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.
翻译:排名模型是信息检索系统的主要组成部分。 几种排序方法基于传统机器学习算法,使用一套手工制作的特征。 最近,研究人员在信息检索中利用了深层学习模型。 这些模型经过培训,从原始数据中提取用于排序任务的特征,以克服手工制作的特征的局限性。 提出了各种深层学习模型,每个模型都提供一套神经网络组件,以提取用于排序的特征。 在本文中,我们将文献中的拟议模型与不同层面进行比较,以了解每种模型的主要贡献和局限性。 在对文献的讨论中,我们分析了有前途的神经元组成部分,并提出了未来的研究方向。 我们还展示了文件检索与其他检索任务的类比,其中要排列的项目是结构化的文件、答案、图像和视频。