The work of the Information Retrieval (IR) group involves developing effective web-based technologies that support people with accessing, managing and using information. We approach this from different perspectives, including the study of the interactions between people, information and technologies; and the development of computational methods that support information access and use. Our multidisciplinary team draws together skills from computer science, information science and human computer and information interaction. We have collaborated in our research with external academic and non-academic partners, funded by national and international funding bodies. Through our research we aim to enrich the user’s search experience and further our understanding of how people access, interact with, use and re-use information.
Key research areas
The research we undertake is generally built upon consideration of the user, the system and context of use. Current research in the IR group is informed by four core areas of activity:
- The study of human computer and information interaction (e.g., in the iLab) to understand user cognition and behaviour with respect to the interactivity involved in information access, use and re-use.
- The development of novel solutions to information access problems, ranging from the development of specific algorithms to the design of entire prototype systems, with a particular focus on web-scale systems and algorithmic bias.
- The study and design of methods and techniques for evaluating information access systems for a variety of applications and search scenarios.
- The development of novel methodologies to study dynamics of social interactions on social media platforms.
Specific areas of research include: human computation and crowdsourcing, information visualisation, web science, information retrieval, data mining, big data, geo-spatial search, artificial intelligence, semantic search, multimedia retrieval, digital cultural heritage, NLP, data streams, search log analysis, task-based information interaction, lifelogging, exploratory search, human machine interaction, recommender systems, algorithmic bias, user interface design and evaluation.