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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

Showing 676700 of 753 papers

TitleStatusHype
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance0
Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking0
A General Framework for Counterfactual Learning-to-Rank0
Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion0
Word-Entity Duet Representations for Document Ranking0
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network0
Cross-Lingual Learning-to-Rank with Shared Representations0
Cross-lingual Subjectivity Detection for Resource Lean Languages0
CRST: a Claim Retrieval System in Twitter0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer0
Self-Supervised Ranking for Representation Learning0
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations0
De-Biased Modelling of Search Click Behavior with Reinforcement Learning0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity0
Deep Domain Specialisation for single-model multi-domain learning to rank0
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images0
Deep Multi-view Learning to Rank0
Deep Neural Network for Learning to Rank Query-Text Pairs0
Deep Pairwise Learning To Rank For Search Autocomplete0
Deep Ranking Ensembles for Hyperparameter Optimization0
Deep Ranking for Person Re-identification via Joint Representation Learning0
Detect2Rank : Combining Object Detectors Using Learning to Rank0
Semantic Relatedness of Wikipedia Concepts -- Benchmark Data and a Working Solution0
Dialog Generation Using Multi-Turn Reasoning Neural Networks0
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