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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 31–40 of 753 papers

Title	Date	Tasks	Status	Hype
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1
ILMART: Interpretable Ranking with Constrained LambdaMART	Jun 1, 2022	Learning-To-Rank	CodeCode Available	1
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity	Apr 22, 2022	Learning-To-Rank	CodeCode Available	1
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression	Apr 1, 2022	Head Pose EstimationLearning-To-Rank	CodeCode Available	1
Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference	Feb 12, 2022	Entity TypingLearning-To-Rank	CodeCode Available	1
Decision-Focused Learning: Through the Lens of Learning to Rank	Dec 7, 2021	Combinatorial OptimizationDecision Making	CodeCode Available	1
Pairwise Learning for Neural Link Prediction	Dec 6, 2021	Learning-To-RankLink Prediction	CodeCode Available	1
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention	May 20, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics	May 3, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1

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