  Neural ranking models for information retrieval (IR) use shallow or deep
neural networks to rank search results in response to a query. Traditional
learning to rank models employ machine learning techniques over hand-crafted IR
features. By contrast, neural models learn representations of language from raw
text that can bridge the gap between query and document vocabulary. Unlike
classical IR models, these new machine learning based approaches are
data-hungry, requiring large scale training data before they can be deployed.
This tutorial introduces basic concepts and intuitions behind neural IR models,
and places them in the context of traditional retrieval models. We begin by
introducing fundamental concepts of IR and different neural and non-neural
approaches to learning vector representations of text. We then review shallow
neural IR methods that employ pre-trained neural term embeddings without
learning the IR task end-to-end. We introduce deep neural networks next,
discussing popular deep architectures. Finally, we review the current DNN
models for information retrieval. We conclude with a discussion on potential
future directions for neural IR.
