Matrix Completion

121 papers with code • 0 benchmarks • 4 datasets

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


Use these libraries to find Matrix Completion models and implementations

Most implemented papers

Graph Convolutional Matrix Completion

riannevdberg/gc-mc 7 Jun 2017

We consider matrix completion for recommender systems from the point of view of link prediction on graphs.

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

iskandr/fancyimpute 9 Oct 2014

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

Hybrid Recommender System based on Autoencoders

fstrub95/Autoencoders_cf 24 Jun 2016

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.

Sequence-Aware Recommender Systems

taylorhawks/RNN-music-recommender 23 Feb 2018

In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process.

Unsupervised Metric Learning in Presence of Missing Data

rsonthal/MRMissing.jl 19 Jul 2018

Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.

Inductive Matrix Completion Based on Graph Neural Networks

muhanzhang/IGMC ICLR 2020

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?

GLocal-K: Global and Local Kernels for Recommender Systems

usydnlp/Glocal_K 27 Aug 2021

Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item.

Matrix Completion on Graphs

kushagramahajan/GraphRegMC-scRNAseq 7 Aug 2014

Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

rofuyu/exp-grmf-nips15 NeurIPS 2015

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.

Low-Rank Inducing Norms with Optimality Interpretations

LowRankOpt/LRINorm 9 Dec 2016

A posteriori guarantees on solving an underlying rank constrained optimization problem with these convex relaxations are provided.