Scalable and Lightweight On-Device Recommender Systems

This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage.

Academic entry requirements

You have to prove you are prepared for PhD study. You do this by showing you:

have completed some research experience
have completed an approved university degree and
can meet the English language requirements.

Number awarded - May vary