Information retrieval (IR) is a fundamental task in many real-world applications such as Web search, question answering systems, and digital libraries. The core of IR is to identify information resources relevant to user's information need. Since there might be more than one relevant resource, the returned result is often organized as a ranked list of documents according to their relevance degree against the information need. The ranking property of IR makes it different from other tasks, and researchers have devoted substantial efforts to develop a variety of ranking models in IR. In recent years, the resurgence of deep learning has greatly advanced this field and led to a hot topic named NeuIR (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 that are beneficial to the ranking task of IR. Considering the rapid progress of this direction, this survey provides a systematic review of PTMs in IR. The authors present an overview of PTMs applied in different components of an IR system, including the retrieval component and the re-ranking component. In addition, they introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Lastly, they discuss some open challenges and highlight several promising directions with the hope of inspiring and facilitating more works on these topics for future research.

This book constitutes the refereed proceedings of the 24th China Conference on Information Retrieval, CCIR 2018, held in Guilin, China, in September 2018. The 22 full papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections: Information retrieval, collaborative and social computing, natural language processing.