  Session-based recommendation (SBR) aims to predict the user next action based
on the ongoing sessions. Recently, there has been an increasing interest in
modeling the user preference evolution to capture the fine-grained user
interests. While latent user preferences behind the sessions drift continuously
over time, most existing approaches still model the temporal session data in
discrete state spaces, which are incapable of capturing the fine-grained
preference evolution and result in sub-optimal solutions. To this end, we
propose Graph Nested GRU ordinary differential equation (ODE), namely GNG-ODE,
a novel continuum model that extends the idea of neural ODEs to continuous-time
temporal session graphs. The proposed model preserves the continuous nature of
dynamic user preferences, encoding both temporal and structural patterns of
item transitions into continuous-time dynamic embeddings. As the existing ODE
solvers do not consider graph structure change and thus cannot be directly
applied to the dynamic graph, we propose a time alignment technique, called
t-Alignment, to align the updating time steps of the temporal session graphs
within a batch. Empirical results on three benchmark datasets show that GNG-ODE
significantly outperforms other baselines.
