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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 711–720 of 753 papers

TitleStatusHype
Identifying Important Features for Graph Retrieval—0
Learning Rank Functionals: An Empirical Study—0
RankMerging: A supervised learning-to-rank framework to predict links in large social network—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles—0
Automated Disease Normalization with Low Rank Approximations—0
A Learning-to-Rank Approach for Image Color Enhancement—0
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval—0
Identification of functionally related enzymes by learning-to-rank methods—0
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank—0
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