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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 141–150 of 753 papers

TitleStatusHype
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Learning to Rank in Generative RetrievalCode1
Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal ClassificationCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive LearningCode1
Learning to Rank when Grades Matter—0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
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