The Glasgow Information Retrieval Group within the School of Computing Science at the University of Glasgow was founded 32 years ago in 1986 by Professor C. J. ‘Keith’ van Rijsbergen, often considered one of the founders of modern Information Retrieval (IR). From its outset, the Glasgow IR group has focused on improving the effectiveness of IR systems, inventing new logic & probabilistic retrieval models in the 90's and early 2000's, followed by the development of adaptive query expansion techniques, interactive multimedia models, the Divergence From Randomness framework, as well as leading research into quantum, expertise search and search result diversification models in the late 2000's. Since then, the Information Retrieval group embraced emerging machine learning and deep learning technologies for very large corpora and data streams, and have been at the forefront of research, development and application of those technologies for search and recommendation use-cases in a manner that ensures both effectiveness and efficiency.

The Glasgow IR Group has a strong research track record. Indeed, the ACM Digital Library shows that the group is ranked first by number of papers (429) at the SIGIR conference (the top CORE A* conference in the IR field). Meanwhile, a recent study by Microsoft Research of the 40 years of SIGIR showed the University of Glasgow as the 5th most cited university at the conference and the 1st in Europe. The group is also renowned for developing the popular open source IR platform,, which has been downloaded over 60,000 since its first release in 2004 and is cited by over 3500 research papers. Furthermore, the group has a long history of engagement with the public and industry sectors from small SMEs to multinational corporations.

The Informer magazine of BCS's Information Retrieval Specialist Group carried a recent profile on the Glasgow Information Retrieval Group.


As the most active Information Retrieval group by publications in Europe and one of the longest running, our research covers the full-spectrum of topics that are relevant to the development of IR systems:

IR & Recommender Systems Models

  • Theoretical modelling of IR systems
  • Machine learning and deep learning for information retrieval and recommender systems
  • Interactive information retrieval (personalised IR, emotion based search, user modelling for IR, gestural IR)
  • User modelling and personal information access
  • Topic modeling; Entity search; Natural language processing for IR
  • Recommender systems; Context-aware venue suggestion

Large-scale IR & Efficient IR

  • Web information retrieval; Big data and information retrieval
  • Efficient architecture for large-scale IR systems; Data stream processing architectures

Data Streams & IR

  • Real-time information retrieval
  • Search in social and sensor networks

Artificial Intelligence & IR

  • Conversational information seeking and dialogue systems
  • Information credibility, transparency, explainability and verification in IR systems
  • Fairness in information retrieval & recommender systems

Natural Language Processing & IR

  • Information extraction including entity and relation extraction
  • Automatic knowledge graph construction
  • Multi-task models, joint models and summarization


  • Multimedia information retrieval
  • Domain-specific information retrieval: smart cities; health; news; eDiscovery; sensitivity review
  • Emergency management and crisis informatics
  • Politics and Media


  • Test collections and evaluation metrics
  • Evaluation of IR systems and crowdsourcing for IR
  • Online and Offline Evaluation of IR and Recommender Systems
  • Eye-tracking and physiological approaches, such as fMRI


Active Projects:


Recent Projects:

Current staff and students

Academic Staff:

Current Research Assistants and Research Students:


Recent Graduates

  • Jarana Manotumruksa (2019), University College London, Researcher
  • Anjie Fang (2019), Amazon, Applied Scientist
  • Jorge David Gonzalez Paule  (2019), Jobandtalent Espana, Data Scientist
  • Colin Wilkie (2019), Siemens, Data Engineer
  • David Maxwell (2019), University of Deft, Data Engineer
  • Graham McDonald (2019), University of Glasgow, Lecturer
  • James McMinn (2018), ScoopAnalytics, Co-Founder
  • Stuart Mackie (2018), BiP Solutions/Strathclyde Uni, Data Scientist
  • Horatiu Bota (2018), Prodsight, Data Scientist
  • Jesus Alberto Rodriquez Perez (2018), University of Glasgow, Postdoctoral Researcher
  • Fajie Yuan (2018), Tencent, Senior Researcher


Notable Alumni

  • Ryen White (Research Manager, Microsoft Research AI)
  • Mark Sanderson (Professor, Royal Melbourne Institute of Technology)
  • Mounia Lalmas (Head of Tech Research, Spotify)
  • Ian Ruthven (Professor, Strathclyde University)
  • Fabio Crestani (Professor, University of Lugano)
  • Vassilis Plachouras (Software Engineering, Facebook)
  • Leif Azzopardi (Chancellor's Fellow, Strathclyde University)
  • Rodrygo Santos (Assistant Professor, Federal University of Minas Gerais)
  • Eugene Kharitonov (Research Engineer, Facebook)
  • Saul Vargas (Senior Machine Learning Scientist, ASOS)
  • Dyaa Albakour (Lead Data Scientist, Signal Media)
  • Nut Limsopatham (Senior Researcher, Microsoft AI)
  • Amir Jadidinejad (AI Engineer, Glaxo Smith Kline)
  • Zaiqiao Meng (Researcher, Cambridge University)

Terrier IR platform

Terrier is a highly flexible, efficient, and effective open source search engine, readily deployable on large-scale collections of documents developed by the IR group. Terrier implements state-of-the-art indexing and retrieval functionalities, and provides an ideal platform for the rapid development and evaluation of large-scale retrieval applications. Indeed, Terrier is used internationally, with over 60,000 downloads since its first release in 2004. Terrier is is used widely by the research community, with over 3700 citations in research papers according to Google Scholar.

Visit the website at to learn more and download Terrier for free.

Popular resources

For those new to the Information Retrieval field, the group maintains a useful set of common resources for researchers and practitioners:

  • Information Retrieval Test Collections: On this page are a list of publically available IR test collections. Some are held locally and some are pointers to remote sites.
  • Collections of text and corpora: What's the difference between a test collection and a text collection? Well a test collection has to have associated queries and relevance judgements. The things in here are simply document collections.
  • Language reference works: This page contains links to online language reference works, such as dictionaries, thesauri etc.
  • IR systems: A list of links to some sites that have information about IR systems.
  • Linguistic utilities: Bits of IR language related utilities like stemmers, stop words lists, morphological taggers, etc.
  • IR Journals: Various table of contents and abstracts of the papers in a number of well known IR journals.
  • IR Organisations: Various IR groups and more formal organisations.
  • Books: Supplements of books or whole books online.

Upcoming events

Contextualized Neural Retrieval Models: From Effective to Efficiency

Group: Information Retrieval (IR)
Speaker: Ben He, University of Chinese Academy of Sciences
Date: 17 May, 2021
Time: 11:00 - 12:00

Recent contextualized language models such as BERT have shown promising results in improving retrieval performance on various public benchmarks. However,  how to balance between effectiveness and efficiency remains a major issue in deploying BERT-based rankers in practice. This talk presents a series of efforts in tackling bottlenecks of BERT-based rankers from both effectiveness and efficiency perspectives. 1) BERT-QE, a query expansion approach is proposed to utilize BERT's ability in identifying highly relevant text pieces from given documents. 2) Co-BERT, a groupwise end-to-end BERT ranker is proposed to incorporate context information, together with a lightweight PRF calibrator to boost the ranking effectiveness. 3) Inspired by recent advances in transformer-based query generation, we propose to trade offline relevance weighting for online retrieval efficiency by utilizing the powerful BERT ranker to weight the neighbour documents for each document based on the generated pseudo-queries. Analysis and limitations are also discussed.
Dr. Ben He received his B.S. degree in 2001 from Beihang University and his Ph.D. degree in 2007 from the University of Glasgow, both in Computer Science. He was then a research assistant at University of Glasgow from 2007 to 2009, and a postdoctoral fellow at York University from 2009 to 2010. He joined University of Chinese Academy of Sciences (UCAS) in August 2010 where he is currently a professor at the school of Computer Science and Technology. He is also a visiting professor at Institute of Software, CAS. Dr. He's research interests encompass the fields of information retrieval and natural language processing. His work focuses on developing scalable neural retrieval models by addressing various grand challenges facing IR society.