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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 221–230 of 753 papers

Title	Date	Tasks	Status
Computational and Statistical Tradeoffs in Learning to Rank	Aug 22, 2016	Learning-To-Rank	—Unverified
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain	May 4, 2022	Information RetrievalLearning-To-Rank	—Unverified
A Representation Theory for Ranking Functions	Dec 1, 2014	Learning-To-RankReranking	—Unverified
Community-based Cyberreading for Information Understanding	Mar 27, 2021	Learning-To-Rank	—Unverified
Communication-Efficient Algorithms for Statistical Optimization	Dec 1, 2012	Learning-To-Rank	—Unverified
Are Neural Ranking Models Robust?	Aug 11, 2021	Information RetrievalLearning-To-Rank	—Unverified
A Deep Investigation of Deep IR Models	Jul 24, 2017	Information RetrievalLearning-To-Rank	—Unverified
Co-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context	Apr 17, 2021	Learning-To-RankRe-Ranking	—Unverified
Coarse-to-Fine Contrastive Learning on Graphs	Dec 13, 2022	Contrastive LearningLearning-To-Rank	—Unverified
Click-aware purchase prediction with push at the top	Jun 21, 2017	Learning-To-Rank	—Unverified

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