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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 491–500 of 753 papers

Title	Date	Tasks	Status
ULTRA: An Unbiased Learning To Rank Algorithm Toolbox	Aug 11, 2021	Learning-To-Rank	—Unverified
On Learning to Rank Long Sequences with Contextual Bandits	Jun 7, 2021	Learning-To-RankMulti-Armed Bandits	—Unverified
Online Diverse Learning to Rank from Partial-Click Feedback	Nov 1, 2018	Learning-To-RankRecommendation Systems	—Unverified
Online Learning of Optimally Diverse Rankings	Sep 13, 2021	Learning-To-Rank	—Unverified
Online Learning to Rank in Stochastic Click Models	Mar 7, 2017	Information RetrievalLearning-To-Rank	—Unverified
Online Learning to Rank with Features	Oct 5, 2018	Learning-To-Rank	—Unverified
Online Learning to Rank with Feedback at the Top	Mar 6, 2016	Learning-To-Rank	—Unverified
Online Learning to Rank with Top-k Feedback	Aug 23, 2016	Learning-To-Rank	—Unverified
On Lipschitz Continuity and Smoothness of Loss Functions in Learning to Rank	May 3, 2014	Binary ClassificationLearning-To-Rank	—Unverified
A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom	Aug 29, 2023	Learning-To-RankRe-Ranking	—Unverified

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