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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
GLEN: Generative Retrieval via Lexical Index Learning	Nov 6, 2023	Learning-To-RankRetrieval	CodeCode Available	1
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1
L2R2: Leveraging Ranking for Abductive Reasoning	May 22, 2020	Language ModellingLearning-To-Rank	CodeCode Available	1
Learning Latent Vector Spaces for Product Search	Aug 25, 2016	Learning-To-Rankparameter estimation	CodeCode Available	1
Learning to Blindly Assess Image Quality in the Laboratory and Wild	Jul 1, 2019	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1
Learning to Rank in Generative Retrieval	Jun 27, 2023	Learning-To-RankPassage Ranking	CodeCode Available	1
Learning to Rank Microphones for Distant Speech Recognition	Apr 6, 2021	channel selectionDecoder	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

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