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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 91–100 of 753 papers

TitleStatusHype
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter—0
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval—0
Bounded-Abstention Pairwise Learning to Rank—0
Improving Neural Ranking via Lossless Knowledge Distillation—0
A Hierarchical Semantics-Aware Distributional Similarity Scheme—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents—0
Bring you to the past: Automatic Generation of Topically Relevant Event Chronicles—0
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
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