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Grant Award View - GA214450
Robust meta learning for risk-aware recommender systems
GA ID:
GA214450
Agency:
Australian Research Council
Approval Date:
14-Jan-2022
Publish Date:
20-Jan-2022
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
3-Oct-2022 to 2-Oct-2025
Original: 14-Jan-2022 to 31-Dec-2024
Value (AUD):
$494,500.00
(GST inclusive where applicable)
Variations:
- GA214450-V1 - Variation to Grant (5-Apr-2022 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 21/22 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Robust meta learning for risk-aware recommender systems
Purpose:
Recommender systems are the core of many online services but they are highly vulnerable to risks like shilling attacks, privacy leaks, and unexpected change. This project aims to develop new adversarial Bayesian-based, privacy-preserved and self-adaptive fuzzy meta learning methods and meta recommender systems that are robust to these risky, uncertain and dynamic environments. The anticipated outcomes should significantly improve the reliability of recommender systems with particular benefits for online personalised service systems, e.g., e-government, e-business and e-Learning. The outcomes will also advance machine learning knowledge with a new robust meta learning schema for general data analytics and applications.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2022
Internal Reference ID:
DP22 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
University of Technology Sydney
Recipient ABN:
77 257 686 961
Grant Recipient Location
Suburb:
ULTIMO
Town/City:
ULTIMO
Postcode:
2007
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2007
Country:
AUSTRALIA