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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 351–360 of 753 papers

Title	Date	Tasks	Status	Hype
Individually Fair Rankings	Jan 1, 2021	FairnessLearning-To-Rank	—Unverified	0
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision	Dec 29, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	0
A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces	Dec 24, 2020	Learning-To-RankNatural Questions	—Unverified	0
Autoregressive Reasoning over Chains of Facts with Transformers	Dec 17, 2020	Learning-To-Rank	CodeCode Available	0
Weakly Supervised Label Smoothing	Dec 15, 2020	Learning-To-RankPassage Retrieval	CodeCode Available	1
Building Cross-Sectional Systematic Strategies By Learning to Rank	Dec 13, 2020	Information RetrievalLearning-To-Rank	—Unverified	0
PiRank: Scalable Learning To Rank via Differentiable Sorting	Dec 12, 2020	Learning-To-Rank	CodeCode Available	1
Learning from User Interactions with Rankings: A Unification of the Field	Dec 9, 2020	counterfactualLearning-To-Rank	—Unverified	0
Unifying Online and Counterfactual Learning to Rank	Dec 8, 2020	counterfactualLearning-To-Rank	CodeCode Available	1
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews	Nov 19, 2020	Learning-To-RankRetrieval	—Unverified	0

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