Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Monti, F and Bronstein, MM and Bresson, X},
doi = {10.1109/ICASSP.2018.8462545},
pages = {6852--6856},
title = {Deep geometric matrix completion: A new way for recommender systems},
url = {},
year = {2018}

RIS format (EndNote, RefMan)

AB - © 2018 IEEE. In the last years, Graph Convolutional Neural Networks gained popularity in the Machine Learning community for their capability of extracting local compositional features on signals defined on non-Euclidean domains. Shape correspondence, document classification, molecular properties predictions are just few of the many different problems where these techniques have been successfully applied. In this paper we will present Deep Geometric Matrix Completion, a recent application of Graph Convolutional Neural Networks to the matrix completion problem. We will illustrate MGCNN (a multi-graph CNN able to deal with signals defined over multiple domains) and we will show how coupling such technique with a RNN, a learnable diffusion process can be realized for reconstructing the desired information. Extensive experimental evaluation shows how Geometric Deep Learning techniques allow to outperform previous state of the art solutions on the matrix completion problem.
AU - Monti,F
AU - Bronstein,MM
AU - Bresson,X
DO - 10.1109/ICASSP.2018.8462545
EP - 6856
PY - 2018///
SN - 1520-6149
SP - 6852
TI - Deep geometric matrix completion: A new way for recommender systems
UR -
ER -