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.

翻译：序列推荐旨在通过捕捉用户行为序列中的连续相关性来理解用户偏好，通常基于历史交互表示为商品购买序列。现有方法主要通过建模序列模式来预测下一项商品。尽管这些方法有效，但存在两个自然缺陷：（i）用户偏好本质上是动态的，而协同信号的演化过程常被忽视；（ii）观测到的交互往往是非均匀采样的，但现有方法均基于均匀时间间隔假设建模物品转移。因此，如何有效建模和预测用户偏好的潜在动态过程成为关键研究问题。为应对上述挑战，本文聚焦连续时间序列推荐，提出了一种名为GDERec的图常微分方程框架。技术上，GDERec由一个由两组件构成的自回归图常微分方程刻画，这两个组件分别通过两种定制化图神经网络进行参数化，从混合动力系统视角捕捉用户偏好。两种定制化图神经网络以自回归方式交替训练，从非均匀观测中追踪底层系统的演化过程，从而学习有利于序列推荐的用户和物品有效表征。在五个基准数据集上的大量实验表明，我们的模型优于多种最先进的推荐方法。