PROJECT SUMMARY
Considerate and Accurate Multi-party Recommender Systems for Constrained Resources
Focus Areas: News and Media, Transport and Mobility, Health, and Social Services
Research Program: Machines
Status: Active
This project will create a next generation recommender system that enables equitable allocation of constrained resources. The project will produce novel hybrid socio-technical methods and resources to create a Considerate and Accurate REcommender System (CARES), evaluated with social science and behavioural economics lenses.
CARES will transform the sharing economy by delivering systems and methods that improve user and non-user experiences, business efficiency, and corporate social responsibility.
PARTICIPATE
Participate in an online user study on multi-party fair recommendations
We are looking for users of the Spotify music application to complete a brief online study. In the study, you are expected to browse music recommendations and answer a set of questions.
The study is expected to take less than 15 minutes, and you will receive a AU$10 gift card as a thank you.
You will need to have an active Spotify account with at least 6 months of listening history to take part.
To verify your eligibility and participate in the study, please fill out this form.
PUBLICATIONS
Are footpaths encroached by shared e-scooters? Spatio-temporal analysis of Micro-mobility services, 2023
Kegalle, H., Hettiachchi, D., et al.
Capacity-aware fair POI recommendation combining Transformer Neural Networks and Resource allocation Policy. Submitted to journal Knowledge Based Systems, 2023
Chan, J.
More is Less: When do Recommender Systems Underperform for Data-rich Users? 2023
Xuan, Y., Sanderson, M., et al.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies, 2023
Small, E., Chan, J., et al.
RESEARCHERS
PARTNERS
