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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 151–160 of 753 papers

TitleStatusHype
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility StudyCode0
Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search DatasetCode0
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval—0
Learning to Rank Patches for Unbiased Image Redundancy ReductionCode0
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking ModelsCode0
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMsCode0
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages—0
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented GenerationCode0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
ShaRP: A Novel Feature Importance Framework for RankingCode0
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