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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 451–460 of 753 papers

Title	Date	Tasks	Status
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions	Nov 1, 2019	AI2 Reasoning ChallengeARC	—Unverified
The DipInfoUniTo Realizer at SRST'19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization	Nov 1, 2019	BIG-bench Machine LearningLearning-To-Rank	—Unverified
BanditRank: Learning to Rank Using Contextual Bandits	Oct 23, 2019	Information RetrievalLearning-To-Rank	—Unverified
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking	Oct 21, 2019	Information RetrievalLearning-To-Rank	—Unverified
Universal Text Representation from BERT: An Empirical Study	Oct 17, 2019	Learning-To-RankNatural Language Inference	—Unverified
Personalized Context-Aware Multi-Modal Transportation Recommendation	Oct 13, 2019	feature selectionLearning-To-Rank	—Unverified
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)	Oct 10, 2019	Learning-To-RankText Generation	CodeCode Available
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet	Oct 5, 2019	Learning-To-RankNER	—Unverified
Learning to Rank Proposals for Object Detection	Oct 1, 2019	Learning-To-RankObject	—Unverified
Learning Effective Exploration Strategies For Contextual Bandits	Sep 25, 2019	Imitation LearningLearning-To-Rank	—Unverified

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