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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 161–170 of 753 papers

Title	Date	Tasks	Status	Score
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available	5
Hashing as Tie-Aware Learning to Rank	May 23, 2017	Image RetrievalLearning-To-Rank	CodeCode Available	5
Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation	Jul 19, 2020	counterfactualHeterogeneous Treatment Effect Estimation	CodeCode Available	5
Distilled Neural Networks for Efficient Learning to Rank	Feb 22, 2022	CPUInformation Retrieval	CodeCode Available	5
Distractor Generation for Multiple Choice Questions Using Learning to Rank	Jun 1, 2018	BIG-bench Machine LearningDistractor Generation	CodeCode Available	5
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval	Nov 26, 2017	Information RetrievalLearning-To-Rank	CodeCode Available	5
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics	Jul 24, 2024	AttributeFairness	CodeCode Available	5
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion	May 20, 2023	DiversityGraph Learning	CodeCode Available	5
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank	Mar 31, 2022	counterfactualGeneral Reinforcement Learning	CodeCode Available	5
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems	Feb 20, 2023	Learning-To-RankReinforcement Learning (RL)	CodeCode Available	5

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