Imalsha Ubayawickrema

Imalsha’s PhD research explores how Large Language Models (LLMs) can support more interactive, accurate, and explainable recommender systems. Her work investigates how knowledge graphs and user behaviour data can be combined to help AI systems better understand preferences, reduce incorrect or unsupported recommendations, and provide clearer reasons for their suggestions. The research also develops methods for evaluating whether AI-generated explanations genuinely reflect the evidence and decision-making behind a recommendation, with the aim of creating more transparent and trustworthy personalised recommendation systems.

Imalsha Ubayawickrema is a scholarship recipient of the ARC Centre for Automated Decision-Making and Society (ADM+S) and is supervised by Prof. Jeffrey Chan and Dr. Danula Hettiachchi.