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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 11–20 of 753 papers

Title	Date	Tasks	Status	Hype
Accelerated Convergence for Counterfactual Learning to Rank	May 21, 2020	counterfactualLearning-To-Rank	CodeCode Available	1
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1
ILMART: Interpretable Ranking with Constrained LambdaMART	Jun 1, 2022	Learning-To-Rank	CodeCode Available	1
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing	Jul 8, 2025	Learning-To-RankRecommendation Systems	CodeCode Available	1
Dual-Branch Network for Portrait Image Quality Assessment	May 14, 2024	Face Image Quality AssessmentImage Quality Assessment	CodeCode Available	1
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision	Jun 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Controlling Fairness and Bias in Dynamic Learning-to-Rank	May 29, 2020	ArticlesFairness	CodeCode Available	1
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1

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