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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 661–670 of 753 papers

Title	Date	Tasks	Status
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs	Jan 22, 2016	Anomaly DetectionComputational Efficiency	—Unverified
TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud - An Extended Technical Report	Jan 11, 2016	Learning-To-Rank	—Unverified
Rank Pooling for Action Recognition	Dec 6, 2015	Action RecognitionGesture Recognition	CodeCode Available
Activity Auto-Completion: Predicting Human Activities From Partial Videos	Dec 1, 2015	Activity PredictionInformation Retrieval	—Unverified
Learning to Rank Based on Subsequences	Dec 1, 2015	Learning-To-Rank	—Unverified
Predtron: A Family of Online Algorithms for General Prediction Problems	Dec 1, 2015	Binary ClassificationClassification	—Unverified
MidRank: Learning to rank based on subsequences	Nov 29, 2015	Learning-To-Rank	—Unverified
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank	Nov 20, 2015	Learning-To-Rank	—Unverified
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques	Nov 13, 2015	Binary ClassificationLearning-To-Rank	—Unverified
Factorizing LambdaMART for cold start recommendations	Nov 4, 2015	Learning-To-RankMatrix Completion	—Unverified

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