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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 681–690 of 753 papers

TitleStatusHype
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network—0
Cross-Lingual Learning-to-Rank with Shared Representations—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
CRST: a Claim Retrieval System in Twitter—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
Self-Supervised Ranking for Representation Learning—0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations—0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
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