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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 251–260 of 753 papers

TitleStatusHype
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques—0
Handling Position Bias for Unbiased Learning to Rank in Hotels Search—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Deep Multi-view Learning to Rank—0
Automated Essay Scoring by Maximizing Human-Machine Agreement—0
A Flexible Recommendation System for Cable TV—0
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank—0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
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