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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 731–740 of 753 papers

Title	Date	Tasks	Status
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain	Aug 6, 2021	Learning-To-RankNeural Architecture Search	CodeCode Available
HAPI: A Model for Learning Robot Facial Expressions from Human Preferences	Mar 21, 2025	Bayesian OptimizationFacial expression generation	CodeCode Available
Groupwise Query Performance Prediction with BERT	Apr 25, 2022	Learning-To-RankPrediction	CodeCode Available
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application	Mar 2, 2018	Decision MakingLearning-To-Rank	CodeCode Available
Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms	Jul 4, 2024	Community Question AnsweringLearning-To-Rank	CodeCode Available
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank	Mar 8, 2018	Crowd CountingImage Retrieval	CodeCode Available
Unbiased Learning to Rank with Unbiased Propensity Estimation	Apr 16, 2018	Learning-To-Rankparameter estimation	CodeCode Available
Fitting Sentence Level Translation Evaluation with Many Dense Features	Oct 1, 2014	Learning-To-RankMachine Translation	CodeCode Available
Reinforcement Online Learning to Rank with Unbiased Reward Shaping	Jan 5, 2022	Learning-To-RankPosition	CodeCode Available
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems	Feb 20, 2023	Learning-To-RankReinforcement Learning (RL)	CodeCode Available

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