Geometric matrix completion with recurrent multi-graph neural networks

Federico Monti, Michael M. Bronstein, Xavier Bresson

Research output: Contribution to journalConference articlepeer-review

347 Scopus citations

Abstract

Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationary structures on user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines a novel multi-graph convolutional neural network that can learn meaningful statistical graph-structured patterns from users and items, and a recurrent neural network that applies a learnable diffusion on the score matrix. Our neural network system is computationally attractive as it requires a constant number of parameters independent of the matrix size. We apply our method on several standard datasets, showing that it outperforms state-of-the-art matrix completion techniques.

Original languageEnglish
Pages (from-to)3698-3708
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - 2017
Externally publishedYes
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: 4 Dec 20179 Dec 2017

Funding

FundersFunder number
ERC Starting307047, 724228
German Excellence Initiative
TU Munich Institute for Advanced Study
Radcliffe Institute for Advanced Study, Harvard University
Google
Seventh Framework Programme291763, NRFF2017-10

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