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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 51–60 of 753 papers

Title	Date	Tasks	Status	Hype
Controlling Fairness and Bias in Dynamic Learning-to-Rank	May 29, 2020	ArticlesFairness	CodeCode Available	1
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild	May 28, 2020	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1
L2R2: Leveraging Ranking for Abductive Reasoning	May 22, 2020	Language ModellingLearning-To-Rank	CodeCode Available	1
Accelerated Convergence for Counterfactual Learning to Rank	May 21, 2020	counterfactualLearning-To-Rank	CodeCode Available	1
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1
That is a Known Lie: Detecting Previously Fact-Checked Claims	May 12, 2020	Fact CheckingLearning-To-Rank	CodeCode Available	1
RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering	May 1, 2020	Collaborative FilteringLearning-To-Rank	CodeCode Available	1
Hierarchical Entity Typing via Multi-level Learning to Rank	Apr 5, 2020	DecoderEntity Typing	CodeCode Available	1
TREC CAsT 2019: The Conversational Assistance Track Overview	Mar 30, 2020	Conversational SearchLearning-To-Rank	CodeCode Available	1
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1

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