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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
Learning to Rank from Samples of Variable Quality	Jun 21, 2018	Document RankingLearning-To-Rank	—Unverified
Learning to Rank Graph-based Application Objects on Heterogeneous Memories	Nov 4, 2022	Learning-To-Rank	—Unverified
Learning to rank in person re-identification with metric ensembles	Mar 5, 2015	Learning-To-RankPerson Re-Identification	—Unverified
Learning to Rank Intents in Voice Assistants	Apr 30, 2020	DenoisingLearning-To-Rank	—Unverified
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking	Nov 1, 2021	Document RankingLearning-To-Rank	—Unverified
Learning to Rank in the Position Based Model with Bandit Feedback	Apr 27, 2020	Learning-To-RankMulti-Armed Bandits	—Unverified
Learning to Rank Learning Curves	Jun 5, 2020	Learning-To-RankNeural Architecture Search	—Unverified
Learning to Rank Lexical Substitutions	Oct 1, 2013	Learning-To-Rank	—Unverified
Learning to rank music tracks using triplet loss	May 18, 2020	Learning-To-RankTriplet	—Unverified
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation	Jan 25, 2023	Learning-To-RankRecommendation Systems	—Unverified

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