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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 341–350 of 753 papers

Title	Date	Tasks	Status	Hype
Leveraging User Behavior History for Personalized Email Search	Feb 15, 2021	Learning-To-Rank	—Unverified	0
Fairness Through Regularization for Learning to Rank	Feb 11, 2021	Binary ClassificationFairness	—Unverified	0
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank	Feb 11, 2021	counterfactualLearning-To-Rank	CodeCode Available	0
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance	Feb 1, 2021	Learning-To-RankRecommendation Systems	CodeCode Available	1
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization	Jan 22, 2021	Learning-To-RankMulti-class Classification	—Unverified	0
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval	Jan 21, 2021	Learning-To-RankPassage Reranking	—Unverified	0
Analysis of E-commerce Ranking Signals via Signal Temporal Logic	Jan 14, 2021	Learning-To-RankPosition	—Unverified	0
On the Calibration and Uncertainty of Neural Learning to Rank Models	Jan 12, 2021	Document RankingLearning-To-Rank	CodeCode Available	1
Metric Learning for Session-based Recommendations	Jan 7, 2021	Learning-To-RankMetric Learning	CodeCode Available	0
Neural Rankers are hitherto Outperformed by Gradient Boosted Decision Trees	Jan 1, 2021	Learning-To-Rank	—Unverified	0

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