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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

Showing 1120 of 753 papers

TitleStatusHype
Beyond Pairwise Learning-To-Rank At Airbnb0
A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao0
Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-SupervisionCode0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement0
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
Long Context Modeling with Ranked Memory-Augmented Retrieval0
Weak Supervision for Improved Precision in Search Systems0
Entailment-Preserving First-order Logic Representations in Natural Language Entailment0
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and RelevanceCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
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