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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
Pairwise Learning for Neural Link Prediction	Dec 6, 2021	Learning-To-RankLink Prediction	CodeCode Available	1
PiRank: Scalable Learning To Rank via Differentiable Sorting	Dec 12, 2020	Learning-To-Rank	CodeCode Available	1
PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank	Aug 31, 2020	BenchmarkingLearning-To-Rank	CodeCode Available	1
RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering	May 1, 2020	Collaborative FilteringLearning-To-Rank	CodeCode Available	1
A Large Scale Search Dataset for Unbiased Learning to Rank	Jul 7, 2022	Causal DiscoveryLanguage Modelling	CodeCode Available	1
RankFormer: Listwise Learning-to-Rank Using Listwide Labels	Jun 9, 2023	Knowledge DistillationLearning-To-Rank	CodeCode Available	1
Selective Weak Supervision for Neural Information Retrieval	Jan 28, 2020	Information RetrievalLearning-To-Rank	CodeCode Available	1
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
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks	Nov 11, 2018	Learning-To-Rank	CodeCode Available	1
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems	Oct 17, 2024	Answer GenerationLanguage Modeling	CodeCode Available	1

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