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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	Score
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available	5
Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation	Jul 19, 2020	counterfactualHeterogeneous Treatment Effect Estimation	CodeCode Available	5
HAPI: A Model for Learning Robot Facial Expressions from Human Preferences	Mar 21, 2025	Bayesian OptimizationFacial expression generation	CodeCode Available	5
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank	Mar 8, 2018	Crowd CountingImage Retrieval	CodeCode Available	5
A General Framework for Pairwise Unbiased Learning to Rank	Jul 18, 2022	Learning-To-RankPosition	CodeCode Available	5
LTRR: Learning To Rank Retrievers for LLMs	Jun 16, 2025	Learning-To-RankRAG	CodeCode Available	5
How to Forget Clients in Federated Online Learning to Rank?	Jan 24, 2024	Learning-To-Rank	CodeCode Available	5
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision	Dec 29, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	5
Fitting Sentence Level Translation Evaluation with Many Dense Features	Oct 1, 2014	Learning-To-RankMachine Translation	CodeCode Available	5
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce Products	Jul 24, 2019	counterfactualLearning-To-Rank	CodeCode Available	5

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