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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 271–280 of 753 papers

TitleStatusHype
Attention-based neural re-ranking approach for next city in trip recommendations—0
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models—0
Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Cross-Lingual Learning-to-Rank with Shared Representations—0
A Survey on E-Commerce Learning to Rank—0
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network—0
Individually Fair Rankings—0
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
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