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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 701–750 of 753 papers

TitleStatusHype
Ranking via Robust Binary Classification—0
A Representation Theory for Ranking Functions—0
Learning to Rank Binary Codes—0
Multivariate Spearman's rho for aggregating ranks using copulas—0
Fitting Sentence Level Translation Evaluation with Many Dense FeaturesCode0
Invited Talk: Learning from Rational Behavior—0
Learning to Differentiate Better from Worse Translations—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
Identifying Important Features for Graph Retrieval—0
Learning the Peculiar Value of Actions—0
Learning Rank Functionals: An Empirical Study—0
RankMerging: A supervised learning-to-rank framework to predict links in large social network—0
Automated Disease Normalization with Low Rank Approximations—0
Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—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
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank—0
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank—0
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data—0
Support vector comparison machinesCode0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
Learning to Rank Lexical Substitutions—0
Efficient Collective Entity Linking with Stacking—0
Automated Essay Scoring by Maximizing Human-Machine Agreement—0
Learning to Rank for Blind Image Quality Assessment—0
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
Learning to Extract Folktale Keywords—0
Learning to Order Natural Language Texts—0
Resolving Entity Morphs in Censored Data—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Introducing LETOR 4.0 DatasetsCode1
Learning to Rank for Expert Search in Digital Libraries of Academic Publications—0
Visualization on Financial Terms via Risk Ranking from Financial Reports—0
Expected Divergence Based Feature Selection for Learning to Rank—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking—0
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models—0
Communication-Efficient Algorithms for Statistical Optimization—0
On the Consistency of AUC Pairwise Optimization—0
Forest Reranking through Subtree Ranking—0
Learning to Temporally Order Medical Events in Clinical Text—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
Learning to Rank by Optimizing NDCG Measure—0
Ranking Measures and Loss Functions in Learning to Rank—0
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