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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 441–450 of 753 papers

Title	Date	Tasks	Status
Boosting API Recommendation with Implicit Feedback	Feb 4, 2020	Active LearningLearning-To-Rank	—Unverified
Safe Exploration for Optimizing Contextual Bandits	Feb 2, 2020	counterfactualInformation Retrieval	CodeCode Available
Correcting for Selection Bias in Learning-to-rank Systems	Jan 29, 2020	counterfactualLearning-To-Rank	—Unverified
TopRank+: A Refinement of TopRank Algorithm	Jan 21, 2020	Learning-To-Rank	—Unverified
Influence Diagram Bandits	Jan 1, 2020	Learning-To-RankPosition	—Unverified
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance	Dec 17, 2019	Learning-To-RankPrediction	—Unverified
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval	Dec 12, 2019	Information RetrievalLearning-To-Rank	CodeCode Available
Duet at TREC 2019 Deep Learning Track	Dec 10, 2019	Deep LearningLearning-To-Rank	CodeCode Available
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity	Dec 1, 2019	DiversityLearning-To-Rank	—Unverified
An Alternative Cross Entropy Loss for Learning-to-Rank	Nov 22, 2019	Information RetrievalLearning-To-Rank	—Unverified

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