Learning Graph ODE for Continuous-Time Sequential Recommendation

TL;DR

This paper introduces GDERec, a graph ODE framework for continuous-time sequential recommendation that models dynamic user preferences and irregular interactions using tailored graph neural networks.

Contribution

The paper proposes a novel graph ODE model with two GNN components to capture user preference evolution from irregularly-sampled data, advancing sequential recommendation methods.

Findings

GDERec outperforms state-of-the-art methods on five benchmark datasets.

The model effectively captures dynamic user preferences.

It handles irregular interaction intervals successfully.

Abstract

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.