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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 261–270 of 753 papers

TitleStatusHype
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation—0
A Learning-to-Rank Approach for Image Color Enhancement—0
Addressing Purchase-Impression Gap through a Sequential Re-ranker—0
Fast and Accurate Preordering for SMT using Neural Networks—0
FAQ-based Question Answering via Word Alignment—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
A Passage-Based Approach to Learning to Rank Documents—0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
Fairness Through Regularization for Learning to Rank—0
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