**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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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.

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)

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

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.

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

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

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

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

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

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

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