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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 551–575 of 753 papers

TitleStatusHype
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Cross-Lingual Learning-to-Rank with Shared Representations—0
Dialog Generation Using Multi-Turn Reasoning Neural Networks—0
Efficient Exploration of Gradient Space for Online Learning to Rank—0
Ranking for Relevance and Display Preferences in Complex Presentation LayoutsCode0
Weakly-supervised Contextualization of Knowledge Graph Facts—0
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution—0
Semi-Automatic Construction of Word-Formation Networks (for Polish and Spanish)—0
A General Framework for Counterfactual Learning-to-Rank—0
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
Web-Scale Responsive Visual Search at Bing—0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search—0
Deep Multi-view Learning to Rank—0
Assertion-based QA with Question-Aware Open Information Extraction—0
Drug Selection via Joint Push and Learning to Rank—0
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