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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 111–120 of 753 papers

Title	Date	Tasks	Status	Score
A General Framework for Pairwise Unbiased Learning to Rank	Jul 18, 2022	Learning-To-RankPosition	CodeCode Available	5
ImitAL: Learned Active Learning Strategy on Synthetic Data	Aug 24, 2022	Active LearningInformativeness	CodeCode Available	5
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM	Feb 16, 2025	Learning-To-RankRetrieval	CodeCode Available	5
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank	Mar 8, 2018	Crowd CountingImage Retrieval	CodeCode Available	5
A Recurrent Model for Collective Entity Linking with Adaptive Features	Apr 3, 2020	Deep LearningEntity Disambiguation	CodeCode Available	5
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain	Aug 6, 2021	Learning-To-RankNeural Architecture Search	CodeCode Available	5
BEER 1.1: ILLC UvA submission to metrics and tuning task	Sep 1, 2015	Learning-To-RankMachine Translation	CodeCode Available	5
Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics	Jul 24, 2024	AttributeFairness	CodeCode Available	5
HAPI: A Model for Learning Robot Facial Expressions from Human Preferences	Mar 21, 2025	Bayesian OptimizationFacial expression generation	CodeCode Available	5
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available	5

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