  The core of information retrieval (IR) is to identify relevant information
from large-scale resources and return it as a ranked list to respond to the
user's information need. In recent years, the resurgence of deep learning has
greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural
information retrieval), especially the paradigm of pre-training methods (PTMs).
Owing to sophisticated pre-training objectives and huge model size, pre-trained
models can learn universal language representations from massive textual data,
which are beneficial to the ranking task of IR. Recently, a large number of
works, which are dedicated to the application of PTMs in IR, have been
introduced to promote the retrieval performance. Considering the rapid progress
of this direction, this survey aims to provide a systematic review of
pre-training methods in IR. To be specific, we present an overview of PTMs
applied in different components of an IR system, including the retrieval
component, the re-ranking component, and other components. In addition, we also
introduce PTMs specifically designed for IR, and summarize available datasets
as well as benchmark leaderboards. Moreover, we discuss some open challenges
and highlight several promising directions, with the hope of inspiring and
facilitating more works on these topics for future research.
