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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 41–50 of 753 papers

Title	Date	Tasks	Status	Hype
Learning to Rank Microphones for Distant Speech Recognition	Apr 6, 2021	channel selectionDecoder	CodeCode Available	1
NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting	Feb 15, 2021	Information RetrievalLearning-To-Rank	CodeCode Available	1
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance	Feb 1, 2021	Learning-To-RankRecommendation Systems	CodeCode Available	1
On the Calibration and Uncertainty of Neural Learning to Rank Models	Jan 12, 2021	Document RankingLearning-To-Rank	CodeCode Available	1
Weakly Supervised Label Smoothing	Dec 15, 2020	Learning-To-RankPassage Retrieval	CodeCode Available	1
PiRank: Scalable Learning To Rank via Differentiable Sorting	Dec 12, 2020	Learning-To-Rank	CodeCode Available	1
Unifying Online and Counterfactual Learning to Rank	Dec 8, 2020	counterfactualLearning-To-Rank	CodeCode Available	1
PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank	Aug 31, 2020	BenchmarkingLearning-To-Rank	CodeCode Available	1
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	Aug 19, 2020	Click-Through Rate PredictionLearning-To-Rank	CodeCode Available	1
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network	Jun 7, 2020	Learning-To-RankQuestion Answering	CodeCode Available	1

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