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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 121–130 of 753 papers

TitleStatusHype
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank—0
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems—0
Learning to Rank Context for Named Entity Recognition Using a Synthetic DatasetCode0
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition—0
Replicating Relevance-Ranked Synonym Discovery in a New Language and Domain—0
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data—0
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to RankCode0
NOWJ1@ALQAC 2023: Enhancing Legal Task Performance with Classic Statistical Models and Pre-trained Language Models—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
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