About
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
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Greatest papers with code
A Unified Framework for Structured Low-rank Matrix Learning
ICML 2018
• microsoft/recommenders
• 
We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices.
Graph Convolutional Matrix Completion
• tkipf/gae
•
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
Ranked #4 on
Recommendation Systems
on YahooMusic Monti
(using extra training data)
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Ranked #1 on
Semantic Textual Similarity
on SentEval
MATRIX COMPLETION NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SENTIMENT ANALYSIS
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems.
Ranked #5 on
Recommendation Systems
on YahooMusic Monti
(using extra training data)
Inductive Matrix Completion Based on Graph Neural Networks
Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model?
Ranked #1 on
Recommendation Systems
on Douban Monti
Hybrid Recommender System based on Autoencoders
A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings.
Ranked #1 on
Recommendation Systems
on Douban
Generalized Low Rank Models
Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.
Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.
Ranked #11 on
Recommendation Systems
on MovieLens 1M
DICTIONARY LEARNING MATRIX COMPLETION RECOMMENDATION SYSTEMS