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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 331–340 of 753 papers

TitleStatusHype
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Learning to Rank by Optimizing NDCG Measure—0
Learning to Extract Folktale Keywords—0
Learning to Focus when Ranking Answers—0
Learning to Order Natural Language Texts—0
Learning to Personalize for Web Search Sessions—0
LearningToQuestion at SemEval 2017 Task 3: Ranking Similar Questions by Learning to Rank Using Rich Features—0
Learning to Rank Academic Experts in the DBLP Dataset—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Position Bias Estimation with Item Embedding for Sparse Dataset—0
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