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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 651–660 of 753 papers

Title	Date	Tasks	Status
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?	Jun 1, 2016	Community Question AnsweringLearning-To-Rank	—Unverified
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking	Jun 1, 2016	Feature EngineeringLearning-To-Rank	—Unverified
Learning to Rank Personalized Search Results in Professional Networks	May 16, 2016	Learning-To-Rank	—Unverified
Off-policy evaluation for slate recommendation	May 16, 2016	Learning-To-RankOff-policy evaluation	CodeCode Available
Machine Comprehension Based on Learning to Rank	May 11, 2016	BIG-bench Machine LearningLearning-To-Rank	—Unverified
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality	May 1, 2016	Language ModelingLanguage Modelling	—Unverified
Generalization error bounds for learning to rank: Does the length of document lists matter?	Mar 6, 2016	Learning-To-Rank	—Unverified
Online Learning to Rank with Feedback at the Top	Mar 6, 2016	Learning-To-Rank	—Unverified
Choice by Elimination via Deep Neural Networks	Feb 17, 2016	Learning-To-Rank	—Unverified
DCM Bandits: Learning to Rank with Multiple Clicks	Feb 9, 2016	Learning-To-Rank	CodeCode Available

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