  This work investigates the problem of learning temporal interaction networks.
A temporal interaction network consists of a series of chronological
interactions between users and items. Previous methods tackle this problem by
using different variants of recurrent neural networks to model sequential
interactions, which fail to consider the structural information of temporal
interaction networks and inevitably lead to sub-optimal results. To this end,
we propose a novel Deep Structural Point Process termed as DSPP for learning
temporal interaction networks. DSPP simultaneously incorporates the topological
structure and long-range dependency structure into our intensity function to
enhance model expressiveness. To be specific, by using the topological
structure as a strong prior, we first design a topological fusion encoder to
obtain node embeddings. An attentive shift encoder is then developed to learn
the long-range dependency structure between users and items in continuous time.
The proposed two modules enable our model to capture the user-item correlation
and dynamic influence in temporal interaction networks. DSPP is evaluated on
three real-world datasets for both tasks of item prediction and time
prediction. Extensive experiments demonstrate that our model achieves
consistent and significant improvements over state-of-the-art baselines.
