Research Grant
[Cite as https://purl.org/au-research/grants/arc/DP190101985]Researchers: Professor Hongzhi Yin (Chief Investigator) , Prof Bin Cui (Partner Investigator)
Brief description Challenging big data for scalable, robust and real-time recommendations. With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data: big volume, low veracity, high velocity and high variety. This project aims to develop a new generation of cost-effective techniques for scalable, robust and real-time recommendations utilising big data. This project aims to address these challenges to achieve scalable, robust and real-time recommendations. This project will devise a series of cost-effective machine learning methods and schemes to deliver an end-to-end recommender framework. This project has the potential to significantly reduce the energy consumption of large-scale recommender systems as well as facilitating an increase in the use of recommendation applications for big data.
Funding Amount $350,000
Funding Scheme Discovery Projects
- PURL : https://purl.org/au-research/grants/arc/DP190101985
- ARC : DP190101985