176 citations · 605 across the 32 of their papers we have counts for
4 papers · 1 filter
Comparing Fair Ranking Metrics
Amifa Raj, Michael D. Ekstrand
Ranked lists are frequently used by information retrieval (IR) systems to present results believed to be relevant to the users information need. Fairness is a relatively new but im…
Evaluating Stochastic Rankings with Expected Exposure
Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand +2
We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for…
Overview of the TREC 2019 Fair Ranking Track
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand +1
The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers in addition to classic notio…
Estimating Error and Bias in Offline Evaluation Results
Mucun Tian, Michael D. Ekstrand
Offline evaluations of recommender systems attempt to estimate users' satisfaction with recommendations using static data from prior user interactions. These evaluations provide re…