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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 381–390 of 753 papers

Title	Date	Tasks	Status
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews	Nov 19, 2020	Learning-To-RankRetrieval	—Unverified
MatRec: Matrix Factorization for Highly Skewed Dataset	Nov 9, 2020	Learning-To-RankRecommendation Systems	—Unverified
Extended Missing Data Imputation via GANs for Ranking Applications	Nov 4, 2020	ImputationInformation Retrieval	—Unverified
U-rank: Utility-oriented Learning to Rank with Implicit Feedback	Nov 1, 2020	Click-Through Rate PredictionLearning-To-Rank	—Unverified
What Are You Trying to Do? Semantic Typing of Event Processes	Nov 1, 2020	Learning-To-RankVocal Bursts Type Prediction	—Unverified
Embedding Meta-Textual Information for Improved Learning to Rank	Oct 30, 2020	ArticlesInformation Retrieval	—Unverified
Addressing Purchase-Impression Gap through a Sequential Re-ranker	Oct 27, 2020	Learning-To-RankReranking	—Unverified
Self-Supervised Ranking for Representation Learning	Oct 14, 2020	ClusteringContrastive Learning	—Unverified
"What Are You Trying to Do?" Semantic Typing of Event Processes	Oct 13, 2020	Learning-To-RankObject	—Unverified
Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank	Oct 7, 2020	Learning-To-RankSentence	CodeCode Available

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