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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 221–230 of 753 papers

Title	Date	Tasks	Status
An Offline Metric for the Debiasedness of Click Models	Apr 19, 2023	counterfactualLearning-To-Rank	CodeCode Available
Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval	Apr 18, 2023	Argument RetrievalLearning-To-Rank	—Unverified
Learning To Rank Resources with GNN	Apr 17, 2023	Graph Neural NetworkInformation Retrieval	—Unverified
Metric-agnostic Ranking Optimization	Apr 17, 2023	Information RetrievalLearning-To-Rank	—Unverified
OPI at SemEval 2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation	Apr 14, 2023	Information RetrievalLearning-To-Rank	—Unverified
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts	Apr 11, 2023	Contrastive LearningDescriptive	—Unverified
Explicit and Implicit Semantic Ranking Framework	Apr 11, 2023	Learning-To-RankText Summarization	—Unverified
Deep Ranking Ensembles for Hyperparameter Optimization	Mar 27, 2023	Hyperparameter OptimizationLearning-To-Rank	—Unverified
Unbiased Learning to Rank with Biased Continuous Feedback	Mar 8, 2023	Learning-To-RankPosition	—Unverified
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation	Mar 3, 2023	Learning-To-Rankreinforcement-learning	—Unverified

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