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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 361–370 of 753 papers

TitleStatusHype
Attention-based neural re-ranking approach for next city in trip recommendations—0
Individually Fair Ranking—0
PairRank: Online Pairwise Learning to Rank by Divide-and-ConquerCode0
Neural Feature Selection for Learning to Rank—0
Maximizing Marginal Fairness for Dynamic Learning to RankCode0
Information Ranking Using Optimum-Path Forest—0
Leveraging User Behavior History for Personalized Email Search—0
Fairness Through Regularization for Learning to Rank—0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to RankCode0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—0
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