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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 81–90 of 753 papers

TitleStatusHype
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
Long Context Modeling with Ranked Memory-Augmented Retrieval—0
Weak Supervision for Improved Precision in Search Systems—0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment—0
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and RelevanceCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Cascading Bandits Robust to Adversarial Corruptions—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Reqo: A Robust and Explainable Query Optimization Cost Model—0
RAMQA: A Unified Framework for Retrieval-Augmented Multi-Modal Question AnsweringCode0
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