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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 121–130 of 753 papers

TitleStatusHype
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
Deep Domain Specialisation for single-model multi-domain learning to rank—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
Boosting API Recommendation with Implicit Feedback—0
A Network Framework for Noisy Label Aggregation in Social Media—0
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