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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 201–210 of 753 papers

Title	Date	Tasks	Status	Hype
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank	Dec 24, 2022	Decision MakingInformation Retrieval	—Unverified	0
Coarse-to-Fine Contrastive Learning on Graphs	Dec 13, 2022	Contrastive LearningLearning-To-Rank	—Unverified	0
Multi-Task Off-Policy Learning from Bandit Feedback	Dec 9, 2022	Learning-To-RankRecommendation Systems	—Unverified	0
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems	Dec 6, 2022	FairnessLearning-To-Rank	—Unverified	0
RankDNN: Learning to Rank for Few-shot Learning	Nov 28, 2022	Few-Shot Learningimage-classification	CodeCode Available	1
RankCSE: Unsupervised Representation Learning via Learning to Rank	Nov 14, 2022	Contrastive LearningLearning-To-Rank	CodeCode Available	1
Learning to Rank Graph-based Application Objects on Heterogeneous Memories	Nov 4, 2022	Learning-To-Rank	—Unverified	0
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance	Nov 2, 2022	Learning-To-Rankregression	—Unverified	0
Whole Page Unbiased Learning to Rank	Oct 19, 2022	Causal DiscoveryInformation Retrieval	—Unverified	0
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning	Oct 17, 2022	Learning-To-RankMulti-agent Reinforcement Learning	—Unverified	0

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