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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 371–380 of 753 papers

Title	Date	Tasks	Status
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
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation	Apr 16, 2018	Learning-To-Rank	—Unverified
A Passage-Based Approach to Learning to Rank Documents	Jun 5, 2019	Document RankingLearning-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
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition	Oct 7, 2023	Learning-To-RankMicro Expression Recognition	—Unverified
VSoLSCSum: Building a Vietnamese Sentence-Comment Dataset for Social Context Summarization	Dec 1, 2016	Learning-To-RankSentence	—Unverified
Learning to Rank Personalized Search Results in Professional Networks	May 16, 2016	Learning-To-Rank	—Unverified

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