Matrix Completion
Leaderboards
Greatest papers with code
Graph Convolutional Matrix Completion
• tkipf/gae
• 
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
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.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
• SeongokRyu/Graph-neural-networks
•
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.
COMMUNITY DETECTION IMAGE CLASSIFICATION MATRIX COMPLETION NODE CLASSIFICATION
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems.
#3 best model for
Recommendation Systems
on MovieLens 100K
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.
SOTA for 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.
#8 best model for
Recommendation Systems
on MovieLens 1M
DICTIONARY LEARNING MATRIX COMPLETION RECOMMENDATION SYSTEMS
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.