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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 741–750 of 753 papers

Title	Date	Tasks	Status
Communication-Efficient Algorithms for Statistical Optimization	Dec 1, 2012	Learning-To-Rank	—Unverified
Expected Divergence Based Feature Selection for Learning to Rank	Dec 1, 2012	feature selectionLearning-To-Rank	—Unverified
On the Consistency of AUC Pairwise Optimization	Aug 3, 2012	Learning-To-Rank	—Unverified
Learning to Temporally Order Medical Events in Clinical Text	Jul 1, 2012	Document SummarizationInformation Retrieval	—Unverified
Forest Reranking through Subtree Ranking	Jul 1, 2012	Dependency ParsingLearning-To-Rank	—Unverified
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity	Dec 1, 2011	Active LearningLearning-To-Rank	—Unverified
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces	May 26, 2011	Learning-To-RankMulti-Task Learning	—Unverified
Two-Layer Generalization Analysis for Ranking Using Rademacher Average	Dec 1, 2010	Generalization BoundsInformation Retrieval	—Unverified
Ranking Measures and Loss Functions in Learning to Rank	Dec 1, 2009	General ClassificationLearning-To-Rank	—Unverified
Learning to Rank by Optimizing NDCG Measure	Dec 1, 2009	Information RetrievalLearning-To-Rank	—Unverified

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