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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 201–210 of 753 papers

Title	Date	Tasks	Status	Score
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs	Feb 19, 2024	Document RankingLearning-To-Rank	CodeCode Available	5
Is Non-IID Data a Threat in Federated Online Learning to Rank?	Apr 20, 2022	Federated LearningInformation Retrieval	CodeCode Available	5
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank	Feb 17, 2019	Active LearningCrowd Counting	CodeCode Available	5
Ranking for Relevance and Display Preferences in Complex Presentation Layouts	May 7, 2018	Deep Reinforcement LearningLearning-To-Rank	CodeCode Available	5
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models	Mar 24, 2024	Information RetrievalLearning-To-Rank	CodeCode Available	5
Ranking Structured Objects with Graph Neural Networks	Apr 18, 2021	Graph RankingGraph Regression	CodeCode Available	5
An Offline Metric for the Debiasedness of Click Models	Apr 19, 2023	counterfactualLearning-To-Rank	CodeCode Available	5
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank	Feb 11, 2021	counterfactualLearning-To-Rank	CodeCode Available	5
Safe Exploration for Optimizing Contextual Bandits	Feb 2, 2020	counterfactualInformation Retrieval	CodeCode Available	5
Learning to Rank Rationales for Explainable Recommendation	Jun 10, 2022	Explainable RecommendationLearning-To-Rank	CodeCode Available	5

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