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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 231–240 of 753 papers

TitleStatusHype
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews—0
Forest Reranking through Subtree Ranking—0
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems—0
Click-aware purchase prediction with push at the top—0
Generalization error bounds for learning to rank: Does the length of document lists matter?—0
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities—0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
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