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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 621–630 of 753 papers

TitleStatusHype
Policy-Gradient Training of Fair and Unbiased Ranking FunctionsCode0
Distilled Neural Networks for Efficient Learning to RankCode0
Metric Learning for Session-based RecommendationsCode0
Learning a Deep Listwise Context Model for Ranking RefinementCode0
Learning Cluster Representatives for Approximate Nearest Neighbor SearchCode0
Sustainable transparency in Recommender Systems: Bayesian Ranking of Images for ExplainabilityCode0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to RankCode0
A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor SearchCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to RankCode0
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