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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 611–620 of 753 papers

Title	Date	Tasks	Status
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources	Nov 6, 2017	Context Aware Product RecommendationLearning-To-Rank	CodeCode Available
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns	Mar 20, 2018	Incremental LearningLearning-To-Rank	CodeCode Available
Maximizing Marginal Fairness for Dynamic Learning to Rank	Feb 18, 2021	FairnessLearning-To-Rank	CodeCode Available
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?	May 13, 2024	feature selectionInterpretable Machine Learning	CodeCode Available
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval	Nov 26, 2017	Information RetrievalLearning-To-Rank	CodeCode Available
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study	Apr 4, 2024	counterfactualLearning-To-Rank	CodeCode Available
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision	Dec 29, 2020	Information RetrievalLearning-To-Rank	CodeCode Available
Support vector comparison machines	Jan 30, 2014	Learning-To-Rank	CodeCode Available
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation	Jul 30, 2019	Decision MakingLearning-To-Rank	CodeCode Available
LaSER: Language-Specific Event Recommendation	Feb 24, 2023	Information RetrievalLearning-To-Rank	CodeCode Available

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