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 (MF-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 negatively impacts
obtaining meaningful 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 non-temporal CF models.
