176 citations · 605 across the 32 of their papers we have counts for
7 papers · 1 filter
Candidate Set Sampling for Evaluating Top-N Recommendation
Ngozi Ihemelandu, Michael D. Ekstrand
The strategy for selecting candidate sets -- the set of items that the recommendation system is expected to rank for each user -- is an important decision in carrying out an offlin…
Distributionally-Informed Recommender System Evaluation
Michael D. Ekstrand, Ben Carterette, Fernando Diaz
Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additio…
Inference at Scale Significance Testing for Large Search and Recommendation Experiments
Ngozi Ihemelandu, Michael D. Ekstrand
A number of information retrieval studies have been done to assess which statistical techniques are appropriate for comparing systems. However, these studies are focused on TREC-st…
Patterns of gender-specializing query reformulation
Amifa Raj, Bhaskar Mitra, Nick Craswell +1
Users of search systems often reformulate their queries by adding query terms to reflect their evolving information need or to more precisely express their information need when th…
Overview of the TREC 2021 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…
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…