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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 91–100 of 753 papers

TitleStatusHype
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Learning to rank quantum circuits for hardware-optimized performance enhancement—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search DatasetCode0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Patches for Unbiased Image Redundancy ReductionCode0
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking ModelsCode0
Metasql: A Generate-then-Rank Framework for Natural Language to SQL TranslationCode1
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