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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	Score
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild	May 28, 2020	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1	5
Unifying Online and Counterfactual Learning to Rank	Dec 8, 2020	counterfactualLearning-To-Rank	CodeCode Available	1	5
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1	5
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing	Jul 8, 2025	Learning-To-RankRecommendation Systems	CodeCode Available	1	5
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks	Nov 11, 2018	Learning-To-Rank	CodeCode Available	1	5
Learning Latent Vector Spaces for Product Search	Aug 25, 2016	Learning-To-Rankparameter estimation	CodeCode Available	1	5
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	5
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1	5
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM	Feb 16, 2025	Learning-To-RankRetrieval	CodeCode Available	0	5
Improving Pairwise Ranking for Multi-label Image Classification	Apr 11, 2017	ClassificationGeneral Classification	CodeCode Available	0	5

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