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cs.IR2025
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…
cs.IR2025
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…