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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 311–320 of 753 papers

Title	Date	Tasks	Status	Hype
Ranker-agnostic Contextual Position Bias Estimation	Jul 28, 2021	Learning-To-RankPosition	—Unverified	0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank	Jul 20, 2021	Image Super-ResolutionLearning-To-Rank	—Unverified	0
Leveraging semantically similar queries for ranking via combining representations	Jun 23, 2021	Learning-To-Rank	—Unverified	0
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation	Jun 21, 2021	Answer GenerationData Augmentation	—Unverified	0
RankDetNet: Delving Into Ranking Constraints for Object Detection	Jun 19, 2021	3D Object DetectionClassification	—Unverified	0
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting	Jun 9, 2021	Learning-To-RankRetrieval	CodeCode Available	0
On Learning to Rank Long Sequences with Contextual Bandits	Jun 7, 2021	Learning-To-RankMulti-Armed Bandits	—Unverified	0
New Insights into Metric Optimization for Ranking-based Recommendation	Jun 4, 2021	Learning-To-RankRecommendation Systems	CodeCode Available	0
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction	Jun 1, 2021	FairnessLearning-To-Rank	CodeCode Available	0

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