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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 201–210 of 753 papers

TitleStatusHype
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Explicit and Implicit Semantic Ranking Framework—0
Building Cross-Sectional Systematic Strategies By Learning to Rank—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Explore Entity Embedding Effectiveness in Entity Retrieval—0
Influence of Neighborhood on the Preference of an Item in eCommerce Search—0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking—0
Extractive Headline Generation Based on Learning to Rank for Community Question Answering—0
Extreme Learning to Rank via Low Rank Assumption—0
Fairness Through Regularization for Learning to Rank—0
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