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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 51–60 of 753 papers

Title	Date	Tasks	Status	Hype	Score
PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank	Aug 31, 2020	BenchmarkingLearning-To-Rank	CodeCode Available	1	5
RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering	May 1, 2020	Collaborative FilteringLearning-To-Rank	CodeCode Available	1	5
Learning to Blindly Assess Image Quality in the Laboratory and Wild	Jul 1, 2019	Image Quality AssessmentLearning-To-Rank	CodeCode Available	1	5
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank	May 26, 2023	Contrastive LearningLearning-To-Rank	CodeCode Available	1	5
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1	5
Gradient Boosting Neural Networks: GrowNet	Feb 19, 2020	Learning-To-Rankregression	CodeCode Available	1	5
Selective Weak Supervision for Neural Information Retrieval	Jan 28, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	1	5
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision	Jun 21, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering	Oct 10, 2017	Answer SelectionClustering	CodeCode Available	1	5
Offline Model-Based Optimization by Learning to Rank	Oct 15, 2024	Ensemble LearningLearning-To-Rank	CodeCode Available	1	5

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