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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 181–190 of 753 papers

TitleStatusHype
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to RankCode0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom—0
TRIVEA: Transparent Ranking Interpretation using Visual Explanation of Black-Box Algorithmic Rankers—0
Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post FairnessCode0
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank—0
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking—0
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity—0
Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for ExplainabilityCode0
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