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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 411–420 of 753 papers

Title	Date	Tasks	Status
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries	Dec 1, 2016	Document RankingInformation Retrieval	—Unverified
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval	Jun 1, 2014	Information RetrievalLearning-To-Rank	—Unverified
Learning Visual Features from Snapshots for Web Search	Oct 19, 2017	Learning-To-RankRetrieval	—Unverified
Learning what matters - Sampling interesting patterns	Feb 7, 2017	DiversityLearning-To-Rank	—Unverified
Answering questions by learning to rank -- Learning to rank by answering questions	Sep 2, 2019	ARCLearning-To-Rank	—Unverified
An IPW-based Unbiased Ranking Metric in Two-sided Markets	Jul 14, 2023	Learning-To-RankPosition	—Unverified
Leveraging semantically similar queries for ranking via combining representations	Jun 23, 2021	Learning-To-Rank	—Unverified
Towards Deep and Representation Learning for Talent Search at LinkedIn	Sep 17, 2018	Learning Semantic RepresentationsLearning-To-Rank	—Unverified
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank	Dec 28, 2022	Learning-To-Rank	—Unverified
Leveraging User Behavior History for Personalized Email Search	Feb 15, 2021	Learning-To-Rank	—Unverified

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