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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 661–670 of 753 papers

TitleStatusHype
Community-based Cyberreading for Information Understanding—0
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain—0
Computational and Statistical Tradeoffs in Learning to Rank—0
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank—0
Constrained Multi-Task Learning for Automated Essay Scoring—0
Content-Based Features to Rank Influential Hidden Services of the Tor Darknet—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events—0
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank—0
Selective Query Processing: a Risk-Sensitive Selection of System Configurations—0
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