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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 721–730 of 753 papers

Title	Date	Tasks	Status
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank	May 3, 2014	Generalization BoundsLearning-To-Rank	—Unverified
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data	Feb 11, 2014	Binary ClassificationGeneral Classification	—Unverified
Support vector comparison machines	Jan 30, 2014	Learning-To-Rank	CodeCode Available
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings	Oct 1, 2013	Information RetrievalLearning-To-Rank	—Unverified
A Hierarchical Semantics-Aware Distributional Similarity Scheme	Oct 1, 2013	Learning-To-RankSemantic Textual Similarity	—Unverified
Learning to Rank Lexical Substitutions	Oct 1, 2013	Learning-To-Rank	—Unverified
Automated Essay Scoring by Maximizing Human-Machine Agreement	Oct 1, 2013	Automated Essay ScoringLearning-To-Rank	—Unverified
Efficient Collective Entity Linking with Stacking	Oct 1, 2013	Entity LinkingLearning-To-Rank	—Unverified
Learning to Rank for Blind Image Quality Assessment	Sep 1, 2013	Blind Image Quality AssessmentImage Quality Assessment	—Unverified
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking	Aug 24, 2013	ClusteringInformation Retrieval	—Unverified

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