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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 401–410 of 796 papers

TitleStatusHype
Matrix Completion in the Unit Hypercube via Structured Matrix FactorizationCode0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
Collaborative Self-Attention for Recommender Systems—0
Prediction with Unpredictable Feature Evolution—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
On Low-rank Trace Regression under General Sampling DistributionCode0
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