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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 371–380 of 753 papers

Title	Date	Tasks	Status
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval	Jan 21, 2021	Learning-To-RankPassage Reranking	—Unverified
Analysis of E-commerce Ranking Signals via Signal Temporal Logic	Jan 14, 2021	Learning-To-RankPosition	—Unverified
Metric Learning for Session-based Recommendations	Jan 7, 2021	Learning-To-RankMetric Learning	CodeCode Available
Individually Fair Rankings	Jan 1, 2021	FairnessLearning-To-Rank	—Unverified
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees	Jan 1, 2021	Learning-To-Rank	—Unverified
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision	Dec 29, 2020	Information RetrievalLearning-To-Rank	CodeCode Available
A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces	Dec 24, 2020	Learning-To-RankNatural Questions	—Unverified
Autoregressive Reasoning over Chains of Facts with Transformers	Dec 17, 2020	Learning-To-Rank	CodeCode Available
Building Cross-Sectional Systematic Strategies By Learning to Rank	Dec 13, 2020	Information RetrievalLearning-To-Rank	—Unverified
Learning from User Interactions with Rankings: A Unification of the Field	Dec 9, 2020	counterfactualLearning-To-Rank	—Unverified

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