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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 71–80 of 796 papers

Title	Date	Tasks	Status	Score
Counterfactual inference for sequential experiments	Feb 14, 2022	counterfactualCounterfactual Inference	CodeCode Available	5
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation	Jun 12, 2025	Contrastive LearningDenoising	CodeCode Available	5
Collaborative Filtering with Graph Information: Consistency and Scalable Methods	Dec 1, 2015	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters	May 22, 2017	Community DetectionDeep Learning	CodeCode Available	5
Collective Matrix Completion	Jul 24, 2018	Matrix Completion	CodeCode Available	5
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections	Dec 2, 2022	Matrix Completion	CodeCode Available	5
A regularized deep matrix factorized model of matrix completion for image restoration	Jul 29, 2020	Image RestorationMatrix Completion	CodeCode Available	5
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset	Nov 4, 2015	Matrix Completion	CodeCode Available	5
Spectral Geometric Matrix Completion	Nov 17, 2019	Matrix CompletionRecommendation Systems	CodeCode Available	5
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and Applications	Sep 26, 2022	Matrix CompletionRecommendation Systems	CodeCode Available	5

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