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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 261–270 of 753 papers

TitleStatusHype
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Automated Disease Normalization with Low Rank Approximations—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning—0
autoBagging: Learning to Rank Bagging Workflows with Metalearning—0
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom—0
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
CRST: a Claim Retrieval System in Twitter—0
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