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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 651–660 of 753 papers

Title	Date	Tasks	Status
Autoregressive Reasoning over Chains of Facts with Transformers	Dec 17, 2020	Learning-To-Rank	CodeCode Available
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments	Nov 28, 2023	counterfactualLearning-To-Rank	CodeCode Available
Uncoupled Regression from Pairwise Comparison Data	May 31, 2019	Learning-To-Rankregression	CodeCode Available
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank	Oct 7, 2020	Learning-To-RankSentence	CodeCode Available
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation	Oct 29, 2023	Federated LearningInformation Retrieval	CodeCode Available
RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question Answering	Jan 23, 2025	DecoderInformation Retrieval	CodeCode Available
Using clarification questions to improve software developers’ Web search	Jul 1, 2022	Learning-To-Rankvalid	CodeCode Available
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank	Jun 3, 2022	Learning-To-Rank	CodeCode Available
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine	Dec 26, 2023	Learning-To-Rank	CodeCode Available
Learning to Rank Aspects and Opinions for Comparative Explanations	Jan 16, 2025	Explainable RecommendationLearning-To-Rank	CodeCode Available

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