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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

TitleStatusHype
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Deep Metric Learning to RankCode0
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
DCM Bandits: Learning to Rank with Multiple ClicksCode0
An Offline Metric for the Debiasedness of Click ModelsCode0
New Insights into Metric Optimization for Ranking-based RecommendationCode0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Learning to Rank Context for Named Entity Recognition Using a Synthetic DatasetCode0
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple DocumentsCode0
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce ProductsCode0
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