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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 341–350 of 753 papers

TitleStatusHype
Effective and secure federated online learning to rank—0
Learning to Rank by Optimizing NDCG Measure—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking—0
Learning to Rank Binary Codes—0
Learning to Rank Based on Subsequences—0
Learning to Rank based on Analogical Reasoning—0
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale—0
Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision—0
An Attention-Based Deep Net for Learning to Rank—0
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