Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

04/22/2017
by   Federico Monti, et al.
0

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 stationarity structures of 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 graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.

READ FULL TEXT

page 3

page 4

page 5

page 7

03/02/2018

Convolutional Geometric Matrix Completion

Geometric matrix completion (GMC) has been proposed for recommendation b...
06/19/2022

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

Despite the success of the Sylvester equation empowered methods on vario...
05/27/2019

Collaborative Self-Attention for Recommender Systems

Recommender systems (RS), which have been an essential part in a wide ra...
08/25/2021

Inductive Matrix Completion Using Graph Autoencoder

Recently, the graph neural network (GNN) has shown great power in matrix...
01/29/2019

Geometric Matrix Completion with Deep Conditional Random Fields

The problem of completing high-dimensional matrices from a limited set o...
07/31/2021

Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks

Matrix completion problems arise in many applications including recommen...
05/26/2018

Nonlinear Inductive Matrix Completion based on One-layer Neural Networks

The goal of a recommendation system is to predict the interest of a user...

Code Repositories

mgcnn

Multi-Graph Convolutional Neural Networks


view repo

magnesium

Multi Graph Convolutional Neural Networks applied to Matrix Completion.


view repo