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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 111–120 of 753 papers

TitleStatusHype
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Choice by Elimination via Deep Neural Networks—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
Click-aware purchase prediction with push at the top—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search—0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
Communication-Efficient Algorithms for Statistical Optimization—0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
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