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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 561–570 of 753 papers

TitleStatusHype
Learning a Deep Listwise Context Model for Ranking RefinementCode0
Unbiased Learning to Rank with Unbiased Propensity EstimationCode0
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
Local Descriptors Optimized for Average Precision—0
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal PatternsCode0
Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets—0
Leveraging Unlabeled Data for Crowd Counting by Learning to RankCode0
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and ApplicationCode0
Deep Neural Network for Learning to Rank Query-Text Pairs—0
Direct Learning to Rank and Rerank—0
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