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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 651–660 of 753 papers

TitleStatusHype
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016—0
Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation—0
Choice by Elimination via Deep Neural Networks—0
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task—0
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016—0
Click-aware purchase prediction with push at the top—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context—0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results—0
Communication-Efficient Algorithms for Statistical Optimization—0
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