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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 331–340 of 753 papers

TitleStatusHype
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search—0
Community-based Cyberreading for Information Understanding—0
Fairness in Ranking: A Survey—0
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
NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of SortingCode1
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