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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 291–300 of 753 papers

Title	Date	Tasks	Status
AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment	Feb 9, 2020	DiversityEnsemble Learning	—Unverified
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization	Jan 22, 2021	Learning-To-RankMulti-class Classification	—Unverified
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus	Sep 1, 2015	Learning-To-RankText Generation	—Unverified
Joint Upper & Lower Bound Normalization for IR Evaluation	Sep 12, 2022	Learning-To-Rank	—Unverified
JPLink: On Linking Jobs to Vocational Interest Types	Feb 6, 2020	Learning-To-Rank	—Unverified
Individually Fair Rankings	Jan 1, 2021	FairnessLearning-To-Rank	—Unverified
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions	Apr 21, 2020	Distractor GenerationLearning-To-Rank	—Unverified
ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking	Sep 19, 2019	Learning-To-RankRe-Ranking	—Unverified
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models	Dec 1, 2012	General ClassificationLearning-To-Rank	—Unverified
A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style	Aug 15, 2019	BIG-bench Machine LearningLearning-To-Rank	—Unverified

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