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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 21–30 of 753 papers

TitleStatusHype
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
Learning to Rank Aspects and Opinions for Comparative ExplanationsCode0
Efficient support ticket resolution using Knowledge Graphs—0
Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks—0
Effective and secure federated online learning to rank—0
Ranking-aware adapter for text-driven image ordering with CLIPCode1
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
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