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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.

MATRIX COMPLETION MULTI-TASK LEARNING

Graph Convolutional Matrix Completion

7 Jun 2017tkipf/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)

LINK PREDICTION MATRIX COMPLETION RECOMMENDATION SYSTEMS

Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

9 Oct 2014iskandr/fancyimpute

The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.

MATRIX COMPLETION

Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset

4 Nov 2015andrewssobral/lrslibrary

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.

MATRIX COMPLETION

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

NeurIPS 2017 fmonti/mgcnn

Matrix completion models are among the most common formulations of recommender systems.

Ranked #5 on Recommendation Systems on YahooMusic Monti (using extra training data)

MATRIX COMPLETION RECOMMENDATION SYSTEMS

Inductive Matrix Completion Based on Graph Neural Networks

ICLR 2020 muhanzhang/IGMC

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?

MATRIX COMPLETION RECOMMENDATION SYSTEMS TRANSFER LEARNING

Hybrid Recommender System based on Autoencoders

24 Jun 2016fstrub95/Autoencoders_cf

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.

MATRIX COMPLETION RECOMMENDATION SYSTEMS

Generalized Low Rank Models

1 Oct 2014madeleineudell/LowRankModels.jl

Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.

DENOISING MATRIX COMPLETION

Dictionary Learning for Massive Matrix Factorization

3 May 2016arthurmensch/modl

Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.

DICTIONARY LEARNING MATRIX COMPLETION RECOMMENDATION SYSTEMS