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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 311–320 of 753 papers

TitleStatusHype
Improving Neural Ranking via Lossless Knowledge Distillation—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Addressing Community Question Answering in English and Arabic—0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems—0
Enhancing LambdaMART Using Oblivious Trees—0
Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings—0
End-to-end Learning for Fair Ranking Systems—0
Boosting API Recommendation with Implicit Feedback—0
A Network Framework for Noisy Label Aggregation in Social Media—0
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