Challenging Big Data for Scalable, Robust and Real-time Recommendations (2019–2022)

With the advent of big data era, recommender systems are facing unprecedented challenges with respect to the four dimensions of big data including big volume, low veracity, high velocity and high variety. This project aims to systematically address these challenges to achieve scalable, robust and real-time recommendations. This project expects to devise a series of cost-effective learning methods and schemes to deliver an end-to-end recommender framework by addressing the specific challenges of big data in the four dimensions. The research results will significantly reduce the energy consumption of large-scale recommender systems as well as benefit both society and economy by creating a number of recommendation applications for big data.
Grant type:
ARC Discovery Projects
  • Senior Lecturer in Data Science
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
Funded by:
Australian Research Council