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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
Selective Weak Supervision for Neural Information Retrieval	Jan 28, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	1
Listwise Learning to Rank by Exploring Unique Ratings	Jan 7, 2020	Learning-To-Rank	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
Towards Amortized Ranking-Critical Training for Collaborative Filtering	Jun 10, 2019	Collaborative FilteringLearning-To-Rank	CodeCode Available	1
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks	Nov 11, 2018	Learning-To-Rank	CodeCode Available	1
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering	Oct 10, 2017	Answer SelectionClustering	CodeCode Available	1
Learning Latent Vector Spaces for Product Search	Aug 25, 2016	Learning-To-Rankparameter estimation	CodeCode Available	1
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1
Towards Two-Stage Counterfactual Learning to Rank	Jun 25, 2025	counterfactualLearning-To-Rank	—Unverified	0
Unidentified and Confounded? Understanding Two-Tower Models for Unbiased Learning to Rank	Jun 25, 2025	Learning-To-Rank	CodeCode Available	0

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