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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 321–330 of 753 papers

Title	Date	Tasks	Status	Hype
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search	May 21, 2021	Learning-To-Rankreinforcement-learning	—Unverified	0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning	May 21, 2021	Learning-To-Rankreinforcement-learning	—Unverified	0
Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention	May 20, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1
Federated Unbiased Learning to Rank	May 11, 2021	Federated LearningLearning-To-Rank	—Unverified	0
Scalable Personalised Item Ranking through Parametric Density Estimation	May 11, 2021	Density EstimationLearning-To-Rank	—Unverified	0
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics	May 3, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1
Ranking Structured Objects with Graph Neural Networks	Apr 18, 2021	Graph RankingGraph Regression	CodeCode Available	0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context	Apr 17, 2021	Learning-To-RankRe-Ranking	—Unverified	0
Learning to Rank Microphones for Distant Speech Recognition	Apr 6, 2021	channel selectionDecoder	CodeCode Available	1
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading	Apr 1, 2021	Learning-To-Rank	—Unverified	0

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