The following is an non-exhaustive list of research projects I am actively involved in.
Responsible AI
Fair AI Systems
Debiased Machine Learning
Explainable AI
Recommender Systems
(Fair) Multi-party Recommender Systems
Simulating Realistic Users in Recommender Systems
Agent-based Recommender Systems
Social Computing (Analysing social media and networks)
Blockmodelling:
Graph clustering involves grouping vertices together to find common communities or the underlying structure in graphs. These can be used for marketing, abstraction to better understand large complex systems/organisation, protein functionality discovery etc. A well known clustering approach is community detection, where the groups have to many connections among themselves and few between groups. These groups can represent friendship groups and protein groups. However, community structure is not the only interesting structure in graphs. An example is the core-periphery structure, where a central, but small core of vertices are well connected to all other vertices and the other groups have fewer connections among themselves and to the other groups. This structure exists in most real graphs.
To find this type of structure requires a more general definition of a graph cluster. Blockmodelling is the technqiue to find these more general structures. In this research, we have devised a number of approaches to find more interpretable and overlapping memberrhip blockmodels.
Itinerary Recommendation:
Planning a travel itinerary can be time consuming, frustrating and difficult to do well. Have to find and choose interesting places to visit, appropriate accommodation, plan and schedule transportation between locations, figuring out where to eat, and all constrained by time and monetary budgets. In this project, we look propose new recommendation based approaches that advances the ultimate aim of recommending personalised travel itineraries.
Role Discovery:
Users in online forums and other social media can be considered to take different roles. Using forums as an example, users can play the expert, enthusiast or the newbie roles (or a mixture of them) in a computer overclocking forum. In this research, we proposed a number of forum based features to group users into common roles. In addition, blcokmodelling (see above) can be used to find common user groups/roles in networks.