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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 431–440 of 753 papers

Title	Date	Tasks	Status
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online	Mar 25, 2020	Learning-To-RankReinforcement Learning	—Unverified
Identifying Notable News Stories	Mar 16, 2020	Learning-To-Rank	—Unverified
StochasticRank: Global Optimization of Scale-Free Discrete Functions	Mar 4, 2020	global-optimizationLearning-To-Rank	—Unverified
Handling Position Bias for Unbiased Learning to Rank in Hotels Search	Feb 28, 2020	Learning-To-RankPosition	—Unverified
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data	Feb 18, 2020	Learning-To-Rank	—Unverified
Learning to rank for uplift modeling	Feb 14, 2020	Learning-To-RankMarketing	—Unverified
Listwise Learning to Rank with Deep Q-Networks	Feb 13, 2020	Decision MakingLearning-To-Rank	—Unverified
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment	Feb 9, 2020	DiversityEnsemble Learning	—Unverified
Eliminating Search Intent Bias in Learning to Rank	Feb 8, 2020	Learning-To-RankRetrieval	—Unverified
JPLink: On Linking Jobs to Vocational Interest Types	Feb 6, 2020	Learning-To-Rank	—Unverified

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