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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 511–520 of 753 papers

Title	Date	Tasks	Status	Hype
MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank	Dec 2, 2018	Collaborative Filteringfeature selection	—Unverified	0
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank	Nov 30, 2018	Document SummarizationLearning-To-Rank	CodeCode Available	0
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks	Nov 11, 2018	Learning-To-Rank	CodeCode Available	1
Intervention Harvesting for Context-Dependent Examination-Bias Estimation	Nov 5, 2018	Learning-To-RankPosition	—Unverified	0
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs	Nov 2, 2018	Graph RankingKnowledge Graphs	CodeCode Available	0
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs	Nov 1, 2018	Entity LinkingInformation Retrieval	—Unverified	0
Ontology-Based Retrieval \& Neural Approaches for BioASQ Ideal Answer Generation	Nov 1, 2018	Abstractive Text SummarizationAnswer Generation	—Unverified	0
Online Diverse Learning to Rank from Partial-Click Feedback	Nov 1, 2018	Learning-To-RankRecommendation Systems	—Unverified	0
Online Learning to Rank with Features	Oct 5, 2018	Learning-To-Rank	—Unverified	0
Entity Linking within a Social Media Platform: A Case Study on Yelp	Oct 1, 2018	Entity LinkingLearning-To-Rank	CodeCode Available	0

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