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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 571–580 of 753 papers

Title	Date	Tasks	Status
Web-Scale Responsive Visual Search at Bing	Feb 14, 2018	Learning-To-Rank	—Unverified
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search	Feb 2, 2018	Learning-To-Rank	—Unverified
Deep Multi-view Learning to Rank	Jan 31, 2018	Learning-To-RankMULTI-VIEW LEARNING	—Unverified
Assertion-based QA with Question-Aware Open Information Extraction	Jan 23, 2018	Learning-To-RankOpen-Domain Question Answering	—Unverified
Drug Selection via Joint Push and Learning to Rank	Jan 23, 2018	Learning-To-Rank	—Unverified
Learning to Select: Problem, Solution, and Applications	Jan 1, 2018	Learning-To-Rank	—Unverified
PRUNE: Preserving Proximity and Global Ranking for Network Embedding	Dec 1, 2017	Community DetectionGeneral Classification	CodeCode Available
Learning to Rank based on Analogical Reasoning	Nov 28, 2017	Learning-To-RankObject	—Unverified
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval	Nov 26, 2017	Information RetrievalLearning-To-Rank	CodeCode Available
Neural Ranking Models with Multiple Document Fields	Nov 25, 2017	Learning-To-RankRetrieval	—Unverified

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