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20222024
most citedDistributionally-Informed Recommender System Evaluation

18 citations · 65 across the 11 of their papers we have counts for

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10 papers · 1 filter

cs.IR20232 cited

Unified Browsing Models for Linear and Grid Layouts

Amifa Raj, Michael Ekstrand

Many information access systems operationalize their results in terms of rankings, which are then displayed to users in various ranking layouts such as linear lists or grids. User…

cs.IR20235 cited

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…

cs.IR2023

Towards Measuring Fairness in Grid Layout in Recommender Systems

Amifa Raj, Michael D. Ekstrand

There has been significant research in the last five years on ensuring the providers of items in a recommender system are treated fairly, particularly in terms of the exposure the…

cs.IR202318 cited

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…

cs.IR20233 cited

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…

cs.IR20235 cited

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…