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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 81–90 of 753 papers

Title	Date	Tasks	Status	Score
Unbiased Learning to Rank with Unbiased Propensity Estimation	Apr 16, 2018	Learning-To-Rankparameter estimation	CodeCode Available	5
An Offline Metric for the Debiasedness of Click Models	Apr 19, 2023	counterfactualLearning-To-Rank	CodeCode Available	5
LaSER: Language-Specific Event Recommendation	Feb 24, 2023	Information RetrievalLearning-To-Rank	CodeCode Available	5
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources	Nov 6, 2017	Context Aware Product RecommendationLearning-To-Rank	CodeCode Available	5
Joint Optimization of Cascade Ranking Models	Feb 11, 2019	Ad-Hoc Information RetrievalDocument Ranking	CodeCode Available	5
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?	May 13, 2024	feature selectionInterpretable Machine Learning	CodeCode Available	5
Is Non-IID Data a Threat in Federated Online Learning to Rank?	Apr 20, 2022	Federated LearningInformation Retrieval	CodeCode Available	5
ImitAL: Learning Active Learning Strategies from Synthetic Data	Aug 17, 2021	Active LearningImitation Learning	CodeCode Available	5
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation	Dec 24, 2021	Combinatorial OptimizationDeep Reinforcement Learning	CodeCode Available	5
Improving Pairwise Ranking for Multi-label Image Classification	Apr 11, 2017	ClassificationGeneral Classification	CodeCode Available	5

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