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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 461–470 of 753 papers

Title	Date	Tasks	Status
Universal Text Representation from BERT: An Empirical Study	Oct 17, 2019	Learning-To-RankNatural Language Inference	—Unverified
Personalized Context-Aware Multi-Modal Transportation Recommendation	Oct 13, 2019	feature selectionLearning-To-Rank	—Unverified
Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)	Oct 10, 2019	Learning-To-RankText Generation	CodeCode Available
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet	Oct 5, 2019	Learning-To-RankNER	—Unverified
Learning to Rank Proposals for Object Detection	Oct 1, 2019	Learning-To-RankObject	—Unverified
Learning Effective Exploration Strategies For Contextual Bandits	Sep 25, 2019	Imitation LearningLearning-To-Rank	—Unverified
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking	Sep 19, 2019	Learning-To-RankRe-Ranking	—Unverified
MarlRank: Multi-agent Reinforced Learning to Rank	Sep 15, 2019	Document RankingLearning-To-Rank	—Unverified
Plackett-Luce model for learning-to-rank task	Sep 15, 2019	Learning-To-Rankmodel	—Unverified
Analysis of Regression Tree Fitting Algorithms in Learning to Rank	Sep 12, 2019	Learning-To-Rankregression	—Unverified

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