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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 21–30 of 753 papers

Title	Date	Tasks	Status	Hype	Score
Hierarchical Entity Typing via Multi-level Learning to Rank	Apr 5, 2020	DecoderEntity Typing	CodeCode Available	1	5
ILMART: Interpretable Ranking with Constrained LambdaMART	Jun 1, 2022	Learning-To-Rank	CodeCode Available	1	5
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1	5
Controlling Fairness and Bias in Dynamic Learning-to-Rank	May 29, 2020	ArticlesFairness	CodeCode Available	1	5
Learning to Blindly Assess Image Quality in the Laboratory and Wild	Jul 1, 2019	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1	5
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1	5
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1	5
Learning to Rank in Generative Retrieval	Jun 27, 2023	Learning-To-RankPassage Ranking	CodeCode Available	1	5
Learning to Rank Microphones for Distant Speech Recognition	Apr 6, 2021	channel selectionDecoder	CodeCode Available	1	5
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1	5

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