This repository contains the author version of the survey on Domain-Specific Information Retrieval, which has been peer reviewed and accepted by Foundations and Trends in Information Retrieval.

To cite the survey, please use the following bib item:

@article{verberne2026domainspecific,  
 authors = {Verberne, Suzan and Piroi, Florina and Hanbury, Allan},  
 title = {{Domain-specific Information Retrieval}},  
 journal = {{Foundations and Trends in Information Retrieval (FnTIR)}},
 issue = {{FTINR-09-2025-0088}},
 year = {2026},  
}

Abstract

Information Retrieval (IR) is much more than ad hoc web search: a large portion of the information seeking activities takes place in specific domains, often addressing work-related tasks. Examples are lawyers finding relevant prior cases for a legal case and academic researchers studying the background material for a paper. In Chapter 2 we outline the most common domain-specific tasks, datasets and applications. Domain-specific IR has a number of challenges that make set it apart from simple web search tasks: they often deal with long documents, long queries and long sessions with specific user needs. We discuss these in Chapter 3. We also address issues related to bias and fairness of models. In Chapter 4, we describe the models and methods developed for domain-specific tasks and data, addressing the specific challenges, including user aspects related to domain-specific IR tasks, dealing with queries, user models, and user interactions. We show that many of the methodological advances in domain-specific IR are transferable to other domains and even to open-domain web search. We conclude with the main takeaways and propose research directions for the near future.