57 citations · 180 across the 49 of their papers we have counts for
8 papers · 1 filter
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
Zhaohan Meng, Zaiqiao Meng, Siwei Liu +3
Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multiple turns of conversation. Existing mu…
Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank
Zeyan Liang, Graham McDonald, Iadh Ounis
Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., us…
RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation
Zeyuan Meng, Zixuan Yi, Iadh Ounis
A recent Large language model (LLM)-based recommendation model, called RecRanker, has demonstrated a superior performance in the top-k recommendation task compared to other models.…
KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation
Zeyuan Meng, Zixuan Yi, Iadh Ounis
Large Language Models (LLMs) have shown strong potential in recommender systems due to their contextual learning and generalisation capabilities. Existing LLM-based recommendation…
Quantifying Query Fairness Under Unawareness
Thomas Jaenich, Alejandro Moreo, Alessandro Fabris +4
Traditional ranking algorithms are designed to retrieve the most relevant items for a user's query, but they often inherit biases from data that can unfairly disadvantage vulnerabl…
Lost in Transliteration: Bridging the Script Gap in Neural IR
Andreas Chari, Iadh Ounis, Sean MacAvaney
Most human languages use scripts other than the Latin alphabet. Search users in these languages often formulate their information needs in a transliterated -- usually Latinized --…