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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	Score
An Efficient Approach for Cross-Silo Federated Learning to Rank	Jun 21, 2021	Federated LearningLearning-To-Rank	CodeCode Available	1	5
Introducing LETOR 4.0 Datasets	Jun 9, 2013	Learning-To-Rank	CodeCode Available	1	5
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	5
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention	May 20, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1	5
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1	5
Hierarchical Entity Typing via Multi-level Learning to Rank	Apr 5, 2020	DecoderEntity Typing	CodeCode Available	1	5
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing	Jul 8, 2025	Learning-To-RankRecommendation Systems	CodeCode Available	1	5
L2R2: Leveraging Ranking for Abductive Reasoning	May 22, 2020	Language ModellingLearning-To-Rank	CodeCode Available	1	5
Controlling Fairness and Bias in Dynamic Learning-to-Rank	May 29, 2020	ArticlesFairness	CodeCode Available	1	5
GLEN: Generative Retrieval via Lexical Index Learning	Nov 6, 2023	Learning-To-RankRetrieval	CodeCode Available	1	5

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