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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 451–460 of 753 papers

TitleStatusHype
An Attention-Based Deep Net for Learning to Rank—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Towards Two-Stage Counterfactual Learning to Rank—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—0
Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
Addressing Community Question Answering in English and Arabic—0
Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm—0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems—0
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