Trustworthy Recommendation under Limited Data

The School of EECS is hosting the following PhD progress review 1 seminar

Trustworthy Recommendation under Limited Data

Speaker: Chenhao Zhang
Host: A/Prof Hongzhi Yin

Abstract: Recommendation systems are vital in the digital age, assisting users in finding their interests within vast data collections. Traditional collaborative filtering methods encounter issues such as the cold-start problem and data scarcity. Deep learning-based systems offer enhanced capabilities but demand substantial data. Limited data exacerbates challenges, including noise and imbalance, creating new obstacles for recommendation systems. This PhD research explores three key aspects: 1) addressing overlooked interesting items, 2) mitigating the impact of observed uninteresting items, and 3) managing data imbalance. This research aims to improve recommendation systems in low-data scenarios, benefiting small and medium-sized companies and responding to public emergencies.

Bio: Mr Chenhao Zhang holds a master's degree in computer science from the University of Queensland, Australia (2022) and is currently pursuing a PhD in computer science at the School of EECS under the supervision of Dr Miao Xu and Dr Weitong Tony Chen. His research interests encompass deep learning and recommendation systems.