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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 291–300 of 753 papers

Title	Date	Tasks	Status
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion	Apr 22, 2022	counterfactualInformation Retrieval	—Unverified
Is Non-IID Data a Threat in Federated Online Learning to Rank?	Apr 20, 2022	Federated LearningInformation Retrieval	CodeCode Available
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank	Apr 6, 2022	Decision MakingLearning-To-Rank	—Unverified
Which Tricks Are Important for Learning to Rank?	Apr 4, 2022	Learning-To-Rank	—Unverified
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition	Apr 2, 2022	Learning-To-RankPosition	CodeCode Available
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank	Mar 31, 2022	counterfactualGeneral Reinforcement Learning	CodeCode Available
Minimax Regret for Cascading Bandits	Mar 23, 2022	Learning-To-Rank	—Unverified
Personalized Execution Time Optimization for the Scheduled Jobs	Mar 11, 2022	Learning-To-RankRecommendation Systems	—Unverified
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths	Mar 4, 2022	Feature ImportanceLearning-To-Rank	—Unverified

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