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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 391–400 of 753 papers

Title	Date	Tasks	Status	Hype
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network	Jun 7, 2020	Learning-To-RankQuestion Answering	CodeCode Available	1
Learning to Rank Learning Curves	Jun 5, 2020	Learning-To-RankNeural Architecture Search	—Unverified	0
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
Ranking-Incentivized Quality Preserving Content Modification	May 26, 2020	Learning-To-RankRetrieval	CodeCode Available	0
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank	May 25, 2020	counterfactualLearning-To-Rank	—Unverified	0
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
Distance-based Positive and Unlabeled Learning for Ranking	May 20, 2020	Learning-To-RankSpecificity	CodeCode Available	0
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1

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