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.
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.
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
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