Temporal Collaborative Filtering with Graph Convolutional Neural Networks

Temporal collaborative filtering (TCF) methods aim
at modelling non-static aspects behind recommender systems,
such as the dynamics in users’ preferences and social trends
around items. State-of-the-art TCF methods employ recurrent
neural networks (RNNs) to model such aspects. These methods
deploy matrix-factorization-based approaches to learn the user
and item representations. Recently, graph-neural-network-based
(GNN-based) approaches have shown improved performance in
providing accurate recommendations over traditional MF-based
approaches in non-temporal CF settings. Motivated by this, we
propose a novel TCF method that leverages GNNs to learn user
and item representations and RNNs to model their temporal
dynamics. A challenge with this method lies in the increased
data sparsity, which makes it more complicated to obtain quality
representations with GNNs. To overcome this challenge, we
train a GNN model at each time step using a set of observed
interactions accumulated time-wise. Comprehensive experiments
on real-world data show the improved performance obtained
by our method over several state-of-the-art temporal and nontemporal
CF models.