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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 601–610 of 753 papers

TitleStatusHype
Reinforcement Learning to Rank with Coarse-grained Labels—0
Universalizing Weak Supervision—0
RelationListwise for Query-Focused Multi-Document Summarization—0
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank—0
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain—0
Reqo: A Robust and Explainable Query Optimization Cost Model—0
Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition—0
Resolving Entity Morphs in Censored Data—0
Responding E-commerce Product Questions via Exploiting QA Collections and Reviews—0
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs—0
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