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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 741–750 of 753 papers

Title	Date	Tasks	Status
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation	Feb 5, 2024	Information RetrievalLearning-To-Rank	CodeCode Available
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction	Jun 1, 2021	FairnessLearning-To-Rank	CodeCode Available
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds	Aug 8, 2019	Learning-To-Rank	CodeCode Available
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance	Feb 17, 2025	Learning-To-RankPosition	CodeCode Available
BEER 1.1: ILLC UvA submission to metrics and tuning task	Sep 1, 2015	Learning-To-RankMachine Translation	CodeCode Available
Distractor Generation for Multiple Choice Questions Using Learning to Rank	Jun 1, 2018	BIG-bench Machine LearningDistractor Generation	CodeCode Available
Unlearning for Federated Online Learning to Rank: A Reproducibility Study	May 19, 2025	Learning-To-Rank	CodeCode Available
Unbiased Pairwise Learning to Rank in Recommender Systems	Nov 25, 2021	AttributeLearning-To-Rank	CodeCode Available
LTRR: Learning To Rank Retrievers for LLMs	Jun 16, 2025	Learning-To-RankRAG	CodeCode Available
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition	Apr 2, 2022	Learning-To-RankPosition	CodeCode Available

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