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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 631–640 of 753 papers

Title	Date	Tasks	Status
Intersection of Parallels as an Early Stopping Criterion	Aug 19, 2022	counterfactualLearning-To-Rank	CodeCode Available
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs	Feb 19, 2024	Document RankingLearning-To-Rank	CodeCode Available
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM	Feb 16, 2025	Learning-To-RankRetrieval	CodeCode Available
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking	Apr 28, 2022	FairnessLearning-To-Rank	CodeCode Available
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits	Aug 8, 2024	Exposure FairnessFairness	CodeCode Available
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank	Aug 19, 2021	counterfactualLearning-To-Rank	CodeCode Available
Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint	Jul 24, 2020	ClusteringFeature Importance	CodeCode Available
Model-based Unbiased Learning to Rank	Jul 24, 2022	Information RetrievalLearning-To-Rank	CodeCode Available
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank	Nov 30, 2018	Document SummarizationLearning-To-Rank	CodeCode Available
Modeling Label Ambiguity for Neural List-Wise Learning to Rank	Jul 24, 2017	Learning-To-Rank	CodeCode Available

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