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
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Most implemented papers
Graph Convolutional Matrix Completion
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.
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.
Sequence-Aware Recommender Systems
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
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
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
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
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
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
A posteriori guarantees on solving an underlying rank constrained optimization problem with these convex relaxations are provided.