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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 61–70 of 753 papers

Title	Date	Tasks	Status	Hype
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild	May 28, 2020	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1
Unifying Online and Counterfactual Learning to Rank	Dec 8, 2020	counterfactualLearning-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
L2R2: Leveraging Ranking for Abductive Reasoning	May 22, 2020	Language ModellingLearning-To-Rank	CodeCode Available	1
Learning to Rank Microphones for Distant Speech Recognition	Apr 6, 2021	channel selectionDecoder	CodeCode Available	1
Dual-Branch Network for Portrait Image Quality Assessment	May 14, 2024	Face Image Quality AssessmentImage Quality Assessment	CodeCode Available	1
Pairwise Learning for Neural Link Prediction	Dec 6, 2021	Learning-To-RankLink Prediction	CodeCode Available	1
GLEN: Generative Retrieval via Lexical Index Learning	Nov 6, 2023	Learning-To-RankRetrieval	CodeCode Available	1
A scale invariant ranking function for learning-to-rank: a real-world use case	Oct 21, 2021	Learning-To-RankRecommendation Systems	—Unverified	0
ARSM Gradient Estimator for Supervised Learning to Rank	Nov 1, 2019	Learning-To-Rank	—Unverified	0

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