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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 211–220 of 753 papers

TitleStatusHype
Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model—0
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
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity OptimizationCode1
Learning To Rank Diversely At Airbnb—0
Toward Understanding Privileged Features Distillation in Learning-to-Rank—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
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