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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 691–700 of 796 papers

TitleStatusHype
Sequence-aware item recommendations for multiply repeated user-item interactions—0
Waveform Design for OFDM-based ISAC Systems Under Resource Occupancy Constraint—0
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization—0
Always Valid Risk Monitoring for Online Matrix Completion—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion—0
Convergence of the majorized PAM method with subspace correction for low-rank composite factorization model—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
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