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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 281–290 of 753 papers

TitleStatusHype
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus—0
Individually Fair Rankings—0
Information Ranking Using Optimum-Path Forest—0
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank—0
Interpretable Learning-to-Rank with Generalized Additive Models—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking—0
CRST: a Claim Retrieval System in Twitter—0
Invited Talk: Learning from Rational Behavior—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
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