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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 541–550 of 753 papers

TitleStatusHype
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
Metalearners for Ranking Treatment Effects—0
Meta Learning to Rank for Sparsely Supervised Queries—0
Metric-agnostic Ranking Optimization—0
Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks—0
MidRank: Learning to rank based on subsequences—0
Minimax Regret for Cascading Bandits—0
Misspecified Linear Bandits—0
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach—0
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention—0
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