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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 421–430 of 753 papers

Title	Date	Tasks	Status
Variance Reduction in Gradient Exploration for Online Learning to Rank	Jun 10, 2019	Learning-To-Rank	—Unverified
Visualization on Financial Terms via Risk Ranking from Financial Reports	Dec 1, 2012	Learning-To-Rank	—Unverified
ViTOR: Learning to Rank Webpages Based on Visual Features	Mar 7, 2019	General Classificationimage-classification	—Unverified
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization	Dec 1, 2016	Learning-To-RankSentence	—Unverified
Weakly-supervised Contextualization of Knowledge Graph Facts	May 7, 2018	Knowledge GraphsLearning-To-Rank	—Unverified
Weak Supervision for Improved Precision in Search Systems	Mar 10, 2025	Learning-To-Rank	—Unverified
Web-Scale Responsive Visual Search at Bing	Feb 14, 2018	Learning-To-Rank	—Unverified
"What Are You Trying to Do?" Semantic Typing of Event Processes	Oct 13, 2020	Learning-To-RankObject	—Unverified
What Are You Trying to Do? Semantic Typing of Event Processes	Nov 1, 2020	Learning-To-RankVocal Bursts Type Prediction	—Unverified
What makes you change your mind? An empirical investigation in online group decision-making conversations	Jul 25, 2022	Change Point DetectionDecision Making	—Unverified

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