  Sequential recommendation aims at understanding user preference by capturing
successive behavior correlations, which are usually represented as the item
purchasing sequences based on their past interactions. Existing efforts
generally predict the next item via modeling the sequential patterns. Despite
effectiveness, there exist two natural deficiencies: (i) user preference is
dynamic in nature, and the evolution of collaborative signals is often ignored;
and (ii) the observed interactions are often irregularly-sampled, while
existing methods model item transitions assuming uniform intervals. Thus, how
to effectively model and predict the underlying dynamics for user preference
becomes a critical research problem. To tackle the above challenges, in this
paper, we focus on continuous-time sequential recommendation and propose a
principled graph ordinary differential equation framework named GDERec.
Technically, GDERec is characterized by an autoregressive graph ordinary
differential equation consisting of two components, which are parameterized by
two tailored graph neural networks (GNNs) respectively to capture user
preference from the perspective of hybrid dynamical systems. The two customized
GNNs are trained alternately in an autoregressive manner to track the evolution
of the underlying system from irregular observations, and thus learn effective
representations of users and items beneficial to the sequential recommendation.
Extensive experiments on five benchmark datasets demonstrate the superiority of
our model over various state-of-the-art recommendation methods.
