Research
I have always been fascinated by web search engines and the human element they involve. I am interested in how individuals translate their information needs into different queries and how this influences the relevance and quality of the information they encounter. I examine the extent to which these often-neglected factors affect retrieval performance, with the goal of guiding the development of more realistic, user-centered methodologies and test collections.
đź“‘ Publications
LLMs can be Fooled into Labelling a Document as Relevant
🏆 Published in SIGIR-AP, 2024. Download: [ Preprint | Poster ]
Generative Information Retrieval Evaluation
Published in Information Access in the Era of Generative AI, 2024. Download: [ Preprint ]
ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality
🏆 Published in UbiComp, 2023. Download: [ Preprint | Github ]
Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study
🏆 Published in SIGIR, 2023. Download: [ Preprint | Poster | Github ]
Where Do Queries Come From?
Published in SIGIR, 2022. Download: [ Preprint ]
User-centered Non-factoid Answer Retrieval
Published in SIGIR, 2022. Download: [ Preprint ]
RMIT CIDDA IR at the TREC 2022 Fair Ranking Track
Published in TREC, 2022. Download: [ Preprint ]
Personalisation of Generic Library Search Results Using Student Enrolment Information
Published in Journal of Educational Data Mining, 2015. Download: [ Preprint ]
📜 Minor Thesis
Inspired by the progress made in web search engines, my minor thesis investigated the potential of using personalization technology in educational library settings as part of my minor thesis at Monash University. I proposed, developed and evaluated an approach that creates student models which capture their academic interests and respond to their queries with search results re-ranked according to their individual academic interests - I had fun working with users and got interesting results :)