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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 726–750 of 753 papers

TitleStatusHype
Learning to Rank Lexical Substitutions—0
Automated Essay Scoring by Maximizing Human-Machine Agreement—0
Efficient Collective Entity Linking with Stacking—0
Learning to Rank for Blind Image Quality Assessment—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
Learning to Order Natural Language Texts—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Resolving Entity Morphs in Censored Data—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Learning to Extract Folktale Keywords—0
Learning to Rank for Expert Search in Digital Libraries of Academic Publications—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models—0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking—0
Visualization on Financial Terms via Risk Ranking from Financial Reports—0
Communication-Efficient Algorithms for Statistical Optimization—0
Expected Divergence Based Feature Selection for Learning to Rank—0
On the Consistency of AUC Pairwise Optimization—0
Learning to Temporally Order Medical Events in Clinical Text—0
Forest Reranking through Subtree Ranking—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—0
Two-Layer Generalization Analysis for Ranking Using Rademacher Average—0
Ranking Measures and Loss Functions in Learning to Rank—0
Learning to Rank by Optimizing NDCG Measure—0
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