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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 526–550 of 753 papers

TitleStatusHype
Weak Supervision for Improved Precision in Search Systems—0
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
Perceptron like Algorithms for Online Learning to Rank—0
Personalized Context-Aware Multi-Modal Transportation Recommendation—0
Personalized Context-Aware Point of Interest Recommendation—0
Personalized Execution Time Optimization for the Scheduled Jobs—0
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data—0
Pessimistic Off-Policy Optimization for Learning to Rank—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
Plackett-Luce model for learning-to-rank task—0
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework—0
Unbiased Offline Evaluation for Learning to Rank with Business Rules—0
Web-Scale Responsive Visual Search at Bing—0
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data—0
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank—0
A Simple yet Effective Framework for Active Learning to Rank—0
Practical User Feedback-driven Internal Search Using Online Learning to Rank—0
A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services—0
Predtron: A Family of Online Algorithms for General Prediction Problems—0
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity—0
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)—0
Differentially Private Link Prediction With Protected Connections—0
"What Are You Trying to Do?" Semantic Typing of Event Processes—0
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank—0
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