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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 551–560 of 753 papers

TitleStatusHype
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Cross-Lingual Learning-to-Rank with Shared Representations—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Ranking for Relevance and Display Preferences in Complex Presentation LayoutsCode0
Weakly-supervised Contextualization of Knowledge Graph Facts—0
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution—0
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)—0
A General Framework for Counterfactual Learning-to-Rank—0
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