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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 371–380 of 753 papers

TitleStatusHype
Story Disambiguation: Tracking Evolving News Stories across News and Social Streams—0
Structured ranking learning using cumulative distribution networks—0
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions—0
TermPicker: Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud - An Extended Technical Report—0
The DipInfoUniTo Realizer at SRST'19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
Time-Aware Evidence Ranking for Fact-Checking—0
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