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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 181–190 of 753 papers

Title	Date	Tasks	Status	Score
Fitting Sentence Level Translation Evaluation with Many Dense Features	Oct 1, 2014	Learning-To-RankMachine Translation	CodeCode Available	5
Model-based Unbiased Learning to Rank	Jul 24, 2022	Information RetrievalLearning-To-Rank	CodeCode Available	5
An Efficient Combinatorial Optimization Model Using Learning-to-Rank Distillation	Dec 24, 2021	Combinatorial OptimizationDeep Reinforcement Learning	CodeCode Available	5
Hashing as Tie-Aware Learning to Rank	May 23, 2017	Image RetrievalLearning-To-Rank	CodeCode Available	5
ImitAL: Learning Active Learning Strategies from Synthetic Data	Aug 17, 2021	Active LearningImitation Learning	CodeCode Available	5
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval	Jan 11, 2023	Conversational SearchInformation Retrieval	CodeCode Available	5
Policy-Gradient Training of Fair and Unbiased Ranking Functions	Nov 19, 2019	counterfactualDecision Making	CodeCode Available	5
End-to-End Neural Ad-hoc Ranking with Kernel Pooling	Jun 20, 2017	Ad-Hoc Information RetrievalDocument Ranking	CodeCode Available	5
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds	Aug 8, 2019	Learning-To-Rank	CodeCode Available	5
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs	Feb 19, 2024	Document RankingLearning-To-Rank	CodeCode Available	5

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