»The FUN project will bring the power of Large Language Models into Web crawling to assess the quality of the Web content.«
Nicola Tonellotto, PhD
Associate Professor, Department of Information Engineering, University of Pisa, Italy
Project FUN
Low-quality content in a data corpus can affect search engine performance. “Quality-Focused Neural Crawling” (FUN) plans to develop new neural methods for identifying low-quality content and integrate this technology in the Open Web Search and Analysis Infrastructure in order to improve search engine performance.
Sean MacAvaney is a Lecturer in Machine Learning at the University of Glasgow and a member of the Terrier Team. His research primarily focuses on effective and efficient neural retrieval. He completed his PhD at Georgetown University in 2021, where he was an ARCS Endowed Scholar. He was a co-recipient of the SIGIR 2023 Best Paper Award, the ECIR 2023 Best Short Paper Award and the ACM SIGIR 2024 Best Paper Runner-up. He has published over 30 papers in top IR and NLP venues.
Nicola Tonellotto is an Associate Professor at the Department of Information Engineering of the University of Pisa. His research interests focus on cloud computing and information retrieval. He received a PhD from the University of Pisa and the Technical University of Pisa in 2008. He was a co-recipient of the ACM SIGIR 2015 Best Paper Award, the ACM SIGIR 2023 Best Paper Award Honourable Mention and the ACM SIGIR 2024 Best Paper Runner-up. He is a Distinguished Member of the ACM. He has published over 100 papers in IR and HPC venues.
