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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 231–240 of 753 papers

Title	Date	Tasks	Status
Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities	Mar 2, 2023	Learning-To-Ranktext-to-speech	—Unverified
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank	Feb 28, 2023	Learning-To-Rank	—Unverified
LaSER: Language-Specific Event Recommendation	Feb 24, 2023	Information RetrievalLearning-To-Rank	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
Ensemble Ranking Model with Multiple Pretraining Strategies for Web Search	Feb 18, 2023	Learning-To-Rank	—Unverified
Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank	Feb 15, 2023	Learning-To-Rank	—Unverified
PASSerRank: Prediction of Allosteric Sites with Learning to Rank	Feb 2, 2023	Drug DiscoveryLearning-To-Rank	CodeCode Available
Overcoming Prior Misspecification in Online Learning to Rank	Jan 25, 2023	Learning-To-Rank	CodeCode Available
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation	Jan 25, 2023	Learning-To-RankRecommendation Systems	—Unverified
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval	Jan 11, 2023	Conversational SearchInformation Retrieval	CodeCode Available

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