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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 641–650 of 753 papers

Title	Date	Tasks	Status
PRUNE: Preserving Proximity and Global Ranking for Network Embedding	Dec 1, 2017	Community DetectionGeneral Classification	CodeCode Available
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels	Aug 18, 2018	Learning-To-Rank	CodeCode Available
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization	Apr 26, 2023	counterfactualLearning-To-Rank	CodeCode Available
Differentiable Unbiased Online Learning to Rank	Sep 22, 2018	Learning-To-Rank	CodeCode Available
Improving Pairwise Ranking for Multi-label Image Classification	Apr 11, 2017	ClassificationGeneral Classification	CodeCode Available
Safe Exploration for Optimizing Contextual Bandits	Feb 2, 2020	counterfactualInformation Retrieval	CodeCode Available
More Accurate Question Answering on Freebase	Oct 1, 2015	Learning-To-RankQuestion Answering	CodeCode Available
Learning to Explain Entity Relationships in Knowledge Graphs	Jul 1, 2015	Knowledge GraphsLearning-To-Rank	CodeCode Available
ImitAL: Learning Active Learning Strategies from Synthetic Data	Aug 17, 2021	Active LearningImitation Learning	CodeCode Available
Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study	Jan 5, 2017	Image CroppingLearning-To-Rank	CodeCode Available

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