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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 301–310 of 753 papers

TitleStatusHype
Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces—0
Deep Domain Specialisation for single-model multi-domain learning to rank—0
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style—0
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation—0
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System—0
Deep Multi-view Learning to Rank—0
Deep Neural Network for Learning to Rank Query-Text Pairs—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning Optimal Card Ranking from Query Reformulation—0
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