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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 521–530 of 753 papers

TitleStatusHype
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Learning to Select: Problem, Solution, and Applications—0
Learning to Temporally Order Medical Events in Clinical Text—0
Learning to Weight Translations using Ordinal Linear Regression and Query-generated Training Data for Ad-hoc Retrieval with Long Queries—0
Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval—0
Learning Visual Features from Snapshots for Web Search—0
Learning what matters - Sampling interesting patterns—0
Leveraging semantically similar queries for ranking via combining representations—0
Leveraging User Behavior History for Personalized Email Search—0
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs—0
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