1 citations · 1 across the 7 of their papers we have counts for
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Explaining When PRF Fails: Participatory Auditing for Selective Query Expansion
Zeyan Liang, Graham McDonald, Iadh Ounis
Pseudo-Relevance Feedback (PRF) improves retrieval effectiveness on average, but harms a substantial fraction of queries through query drift, an asymmetry hidden by aggregate offli…
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…
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…
Query Exposure Prediction for Groups of Documents in Rankings
Thomas Jaenich, Graham McDonald, Iadh Ounis
The main objective of an Information Retrieval system is to provide a user with the most relevant documents to the user's query. To do this, modern IR systems typically deploy a re…
Overview of the TREC 2022 Fair Ranking Track
Michael D. Ekstrand, Graham McDonald, Amifa Raj +1
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demograp…