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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 321–330 of 796 papers

TitleStatusHype
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering ApproachCode1
Partial Trace Regression and Low-Rank Kraus DecompositionCode0
Fixing Inventory Inaccuracies At Scale—0
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian CopulaCode1
Median Matrix Completion: from Embarrassment to Optimality—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—0
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs—0
Discrete-Aware Matrix Completion via Proximal Gradient—0
Tensor Completion Made Practical—0
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