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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 101–110 of 753 papers

Title	Date	Tasks	Status	Score
ImitAL: Learned Active Learning Strategy on Synthetic Data	Aug 24, 2022	Active LearningInformativeness	CodeCode Available	5
Learning to Rank Aspects and Opinions for Comparative Explanations	Jan 16, 2025	Explainable RecommendationLearning-To-Rank	CodeCode Available	5
ImitAL: Learning Active Learning Strategies from Synthetic Data	Aug 17, 2021	Active LearningImitation Learning	CodeCode Available	5
Learning to Rank from Relevance Judgments Distributions	Feb 13, 2022	Learning-To-Rank	CodeCode Available	5
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank	Sep 27, 2023	Learning-To-Rank	CodeCode Available	5
A General Framework for Pairwise Unbiased Learning to Rank	Jul 18, 2022	Learning-To-RankPosition	CodeCode Available	5
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs	Nov 2, 2018	Graph RankingKnowledge Graphs	CodeCode Available	5
Improving Pairwise Ranking for Multi-label Image Classification	Apr 11, 2017	ClassificationGeneral Classification	CodeCode Available	5
Learning to Rank Rationales for Explainable Recommendation	Jun 10, 2022	Explainable RecommendationLearning-To-Rank	CodeCode Available	5
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources	Nov 6, 2017	Context Aware Product RecommendationLearning-To-Rank	CodeCode Available	5

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