  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.
