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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 671–680 of 753 papers

TitleStatusHype
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval—0
Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
Correcting for Selection Bias in Learning-to-rank Systems—0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance—0
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking—0
A General Framework for Counterfactual Learning-to-Rank—0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion—0
Word-Entity Duet Representations for Document Ranking—0
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