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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 251–260 of 753 papers

TitleStatusHype
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model—0
SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—0
Learning To Rank Diversely At Airbnb—0
Joint Upper & Lower Bound Normalization for IR Evaluation—0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Intersection of Parallels as an Early Stopping CriterionCode0
Reinforcement Learning to Rank with Coarse-grained Labels—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Image Quality Assessment: Learning to Rank Image Distortion Level—0
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