  Given a sequence of sets, where each set contains an arbitrary number of
elements, the problem of temporal sets prediction aims to predict the elements
in the subsequent set. In practice, temporal sets prediction is much more
complex than predictive modelling of temporal events and time series, and is
still an open problem. Many possible existing methods, if adapted for the
problem of temporal sets prediction, usually follow a two-step strategy by
first projecting temporal sets into latent representations and then learning a
predictive model with the latent representations. The two-step approach often
leads to information loss and unsatisfactory prediction performance. In this
paper, we propose an integrated solution based on the deep neural networks for
temporal sets prediction. A unique perspective of our approach is to learn
element relationship by constructing set-level co-occurrence graph and then
perform graph convolutions on the dynamic relationship graphs. Moreover, we
design an attention-based module to adaptively learn the temporal dependency of
elements and sets. Finally, we provide a gated updating mechanism to find the
hidden shared patterns in different sequences and fuse both static and dynamic
information to improve the prediction performance. Experiments on real-world
data sets demonstrate that our approach can achieve competitive performances
even with a portion of the training data and can outperform existing methods
with a significant margin.
