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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 31–40 of 753 papers

Title	Date	Tasks	Status	Hype
Dual-Branch Network for Portrait Image Quality Assessment	May 14, 2024	Face Image Quality AssessmentImage Quality Assessment	CodeCode Available	1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision	Jun 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	Aug 19, 2020	Click-Through Rate PredictionLearning-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
Context-Aware Learning to Rank with Self-Attention	May 20, 2020	Learning-To-Rank	CodeCode Available	1
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1
Hierarchical Entity Typing via Multi-level Learning to Rank	Apr 5, 2020	DecoderEntity Typing	CodeCode Available	1
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks	Nov 11, 2018	Learning-To-Rank	CodeCode Available	1
L2R2: Leveraging Ranking for Abductive Reasoning	May 22, 2020	Language ModellingLearning-To-Rank	CodeCode Available	1

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