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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 171–180 of 753 papers

Title	Date	Tasks	Status	Hype
Ranking & Reweighting Improves Group Distributional Robustness	May 9, 2023	Information RetrievalLearning-To-Rank	—Unverified	0
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank	May 4, 2023	FairnessLearning-To-Rank	—Unverified	0
Exploration of Unranked Items in Safe Online Learning to Re-Rank	May 2, 2023	Learning-To-RankSafe Exploration	—Unverified	0
On the Impact of Outlier Bias on User Clicks	May 1, 2023	counterfactualLearning-To-Rank	CodeCode Available	0
Learning to Re-rank with Constrained Meta-Optimal Transport	Apr 29, 2023	FairnessLearning-To-Rank	—Unverified	0
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization	Apr 26, 2023	counterfactualLearning-To-Rank	CodeCode Available	0
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank	Apr 25, 2023	Learning-To-Rank	CodeCode Available	1
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training	Apr 20, 2023	Learning-To-Rank	—Unverified	0
An Offline Metric for the Debiasedness of Click Models	Apr 19, 2023	counterfactualLearning-To-Rank	CodeCode Available	0
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval	Apr 18, 2023	Argument RetrievalLearning-To-Rank	—Unverified	0

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