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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 381–390 of 753 papers

Title	Date	Tasks	Status
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems	Dec 10, 2018	Learning-To-RankRecommendation Systems	—Unverified
TopRank: A practical algorithm for online stochastic ranking	Jun 6, 2018	Decision MakingLearning-To-Rank	—Unverified
TopRank+: A Refinement of TopRank Algorithm	Jan 21, 2020	Learning-To-Rank	—Unverified
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval	Apr 1, 2024	FairnessLearning-To-Rank	—Unverified
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction	Jun 29, 2020	Click-Through Rate PredictionLearning-To-Rank	—Unverified
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank	Feb 28, 2023	Learning-To-Rank	—Unverified
Towards Constructing Sports News from Live Text Commentary	Aug 1, 2016	Document SummarizationLearning-To-Rank	—Unverified
Towards Deep and Representation Learning for Talent Search at LinkedIn	Sep 17, 2018	Learning Semantic RepresentationsLearning-To-Rank	—Unverified
Towards Disentangling Relevance and Bias in Unbiased Learning to Rank	Dec 28, 2022	Learning-To-Rank	—Unverified
Towards Explainable Test Case Prioritisation with Learning-to-Rank Models	May 22, 2024	Learning-To-Rank	—Unverified

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