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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 211–220 of 753 papers

TitleStatusHype
Selective Query Processing: a Risk-Sensitive Selection of System Configurations—0
Efficient and Effective Tree-based and Neural Learning to Rank—0
Position Bias Estimation with Item Embedding for Sparse Dataset—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
On the Impact of Outlier Bias on User ClicksCode0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk MinimizationCode0
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training—0
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