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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 281–290 of 753 papers

Title	Date	Tasks	Status
Cascading Bandits: Learning to Rank in the Cascade Model	Feb 10, 2015	Learning-To-Rank	—Unverified
Factorization Machines for Data with Implicit Feedback	Dec 19, 2018	Collaborative FilteringLearning-To-Rank	—Unverified
Extreme Learning to Rank via Low Rank Assumption	Jul 1, 2018	Learning-To-RankRecommendation Systems	—Unverified
Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank	May 25, 2020	counterfactualLearning-To-Rank	—Unverified
Extractive Headline Generation Based on Learning to Rank for Community Question Answering	Aug 1, 2018	ArticlesCommunity Question Answering	—Unverified
Extraction of Domain-Specific Bilingual Lexicon from Comparable Corpora: Compositional Translation and Ranking	Dec 1, 2012	Learning-To-RankMachine Translation	—Unverified
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training	Apr 20, 2023	Learning-To-Rank	—Unverified
Answering questions by learning to rank -- Learning to rank by answering questions	Sep 2, 2019	ARCLearning-To-Rank	—Unverified
AIBench: An Industry Standard Internet Service AI Benchmark Suite	Aug 13, 2019	BenchmarkingLearning-To-Rank	—Unverified
Influence of Neighborhood on the Preference of an Item in eCommerce Search	Aug 10, 2019	Learning-To-Rank	—Unverified

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