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, Terrier.org, 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.

Topics

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

Applications

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

Evaluation

  • 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

Projects

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 http://terrier.org 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

Search Among Sensitive Content

Group: Information Retrieval (IR)
Speaker: Mahmoud Sayed, Microsoft
Date: 30 May, 2022
Time: 15:00 - 16:00
Location: Zoom link will be provided after registration

Abstract

Current search engines are designed to find what we want. But many collections can not be made available for search engines because they contain sensitive content that needs to be protected. Before release, such content needs to be examined through a sensitivity review process, which can be difficult and time-consuming. To address this challenge, search technology should be capable of providing access to relevant content while protecting sensitive content. First, in this talk, I will present an approach that leverages evaluation-driven information retrieval (IR) techniques. These techniques optimize an objective function that balances the value of finding relevant content with the imperative to protect sensitive content. This requires evaluation measures that balance between relevance and sensitivity. Baselines are introduced for addressing the problem, and a proposed approach that is based on building a listwise learning to rank model is described. The model is trained with a modified loss function to optimize for the evaluation measure. Second, I will describe a new test collection for the sensitivity-aware retrieval task, which is based on the Avocado Research Email Collection. Finally, I will describe a new public test collection with annotations for one class of exempt material subject to the deliberative process privilege, which is used for studying the ability of text classification techniques to identify those materials that are exempt from release under that privilege.

Bio

Mahmoud Sayed is a Data Scientist at Microsoft. He received his Ph.D. from the University of Maryland, College Park, under the supervision of Prof. Doug Oard. His research interests are Information Retrieval and Machine Learning. In particular, he is interested in multi-criteria learning to rank

The event will be held in a hybrid format.

For in-person participation, please register on Eventbrite:
https://www.eventbrite.co.uk/e/search-among-sensitive-content-tickets-349072283597

For online participation, please register in zoom

Responsible Information Access Beyond Fairness

Group: Information Retrieval (IR)
Speaker: Asia Biega, Max Planck Institute for Security and Privacy
Date: 06 June, 2022
Time: 15:00 - 16:00
Location: Zoom link will be provided after registration

Abstract

To be fully responsible, information access systems should account for a variety of social, legal, and ethical principles. These principles apply not only to the underlying algorithms, but also other system facets, including data and user interfaces. In this talk, I will discuss a few responsibilty concepts which are currently driving our research, including data minimization, nudging, and digital well-being.

 

Bio

Asia J. Biega is a tenure-track faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) leading the Responsible Computing group. Her research centers around developing, examining and computationally operationalizing principles of responsible computing, data governance & ethics, and digital well-being. 

Before joining MPI-SP, Asia worked at Microsoft Research Montréal in the Fairness, Accountability, Transparency, and Ethics in AI (FATEGroup. She completed her PhD in Computer Science at the MPI for Informatics and the MPI for Software Systemswinning the DBIS Dissertation Award of the German Informatics Society.

In her work, Asia engages in interdisciplinary collaborations while drawing from her traditional CS education and her industry experience, including consulting and engineering stints at Microsoft, Google and in e-commerce.

 

The event will be held in a hybrid format.

For in-person participation, please register on Eventbrite:
https://www.eventbrite.co.uk/e/responsible-information-access-beyond-fairness-tickets-334664218687 

For online participation, please register in zoom