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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 721–730 of 753 papers

Title	Date	Tasks	Status
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective	Jun 13, 2023	Learning-To-RankOffline RL	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
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance	Sep 6, 2019	Learning-To-Rank	CodeCode Available
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting	Jun 9, 2021	Learning-To-RankRetrieval	CodeCode Available
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset	Apr 3, 2024	Learning-To-Rank	CodeCode Available
A General Framework for Pairwise Unbiased Learning to Rank	Jul 18, 2022	Learning-To-RankPosition	CodeCode Available
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision	May 6, 2025	Learning-To-RankSelf-Supervised Learning	CodeCode Available
PASSerRank: Prediction of Allosteric Sites with Learning to Rank	Feb 2, 2023	Drug DiscoveryLearning-To-Rank	CodeCode Available
Patch Ranking: Efficient CLIP by Learning to Rank Local Patches	Sep 22, 2024	Learning-To-Rank	CodeCode Available
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery	Jun 10, 2025	Causal DiscoveryCausal Inference	CodeCode Available

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