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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 121–130 of 753 papers

Title	Date	Tasks	Status	Score
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics	Jul 24, 2024	AttributeFairness	CodeCode Available	5
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits	Aug 8, 2024	Exposure FairnessFairness	CodeCode Available	5
How to Forget Clients in Federated Online Learning to Rank?	Jan 24, 2024	Learning-To-Rank	CodeCode Available	5
ImitAL: Learned Active Learning Strategy on Synthetic Data	Aug 24, 2022	Active LearningInformativeness	CodeCode Available	5
More Accurate Question Answering on Freebase	Oct 1, 2015	Learning-To-RankQuestion Answering	CodeCode Available	5
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval	Nov 26, 2017	Information RetrievalLearning-To-Rank	CodeCode Available	5
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation	Aug 4, 2024	Learning-To-RankReranking	CodeCode Available	5
HAPI: A Model for Learning Robot Facial Expressions from Human Preferences	Mar 21, 2025	Bayesian OptimizationFacial expression generation	CodeCode Available	5
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank	Aug 20, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	5
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available	5

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