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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 691–700 of 753 papers

Title	Date	Tasks	Status
Deep Ranking for Person Re-identification via Joint Representation Learning	May 26, 2015	Feature EngineeringLearning-To-Rank	—Unverified
Fast and Accurate Preordering for SMT using Neural Networks	May 1, 2015	Language ModelingLanguage Modelling	—Unverified
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval	May 1, 2015	Cross-Lingual Information RetrievalInformation Retrieval	—Unverified
Learning to rank in person re-identification with metric ensembles	Mar 5, 2015	Learning-To-RankPerson Re-Identification	—Unverified
Contextual Semibandits via Supervised Learning Oracles	Feb 20, 2015	Decision MakingLearning-To-Rank	CodeCode Available
Cascading Bandits: Learning to Rank in the Cascade Model	Feb 10, 2015	Learning-To-Rank	—Unverified
Learning Efficient Anomaly Detectors from K-NN Graphs	Feb 6, 2015	Anomaly DetectionComputational Efficiency	—Unverified
Regression and Learning to Rank Aggregation for User Engagement Evaluation	Jan 29, 2015	Learning-To-RankRecommendation Systems	—Unverified
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels	Jan 25, 2015	AttributeLearning-To-Rank	—Unverified
Learning to Rank Academic Experts in the DBLP Dataset	Jan 21, 2015	Information RetrievalLearning-To-Rank	—Unverified

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