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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 491–500 of 753 papers

Title	Date	Tasks	Status
Learning to Rank for Plausible Plausibility	Jun 5, 2019	Learning-To-RankNatural Language Understanding	—Unverified
A Passage-Based Approach to Learning to Rank Documents	Jun 5, 2019	Document RankingLearning-To-Rank	—Unverified
A Study of Latent Structured Prediction Approaches to Passage Reranking	Jun 1, 2019	Learning-To-RankPassage Reranking	—Unverified
Cross-lingual Subjectivity Detection for Resource Lean Languages	Jun 1, 2019	Language ModelingLanguage Modelling	—Unverified
Deep Metric Learning to Rank	Jun 1, 2019	Image RetrievalLearning-To-Rank	CodeCode Available
Uncoupled Regression from Pairwise Comparison Data	May 31, 2019	Learning-To-Rankregression	CodeCode Available
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model	May 29, 2019	Learning-To-Rank	—Unverified
Spectrum-enhanced Pairwise Learning to Rank	May 2, 2019	Learning-To-RankMissing Values	—Unverified
Block-distributed Gradient Boosted Trees	Apr 23, 2019	Click-Through Rate PredictionLearning-To-Rank	—Unverified
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs	Apr 13, 2019	Blind Image Quality AssessmentImage Quality Assessment	—Unverified

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