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.

本文提出了一种新型的 Deep Structural Point Process（DSPP）来学习时间交互网络。通过将拓扑结构和长程依赖结构结合，设计拓扑融合编解码器和关注偏移编解码器，以提高模型表达能力，在项预测和时间预测任务中均实现了显著改进。

深度结构点过程用于学习时空交互网络

Deep Structural Point Process for Learning Temporal Interaction Networks

In recent years, deep learning has gained an indisputable success in computer
vision, speech recognition, and natural language processing. After its rising
success on these challenging areas, it has been studied on recommender systems
as well, but mostly to include content features into traditional methods. In
this paper, we introduce a generalized neural network-based recommender
framework that is easily extendable by additional networks. This framework
named NHR, short for Neural Hybrid Recommender allows us to include more
elaborate information from the same and different data sources. We have worked
on item prediction problems, but the framework can be used for rating
prediction problems as well with a single change on the loss function. To
evaluate the effect of such a framework, we have tested our approach on
benchmark and not yet experimented datasets. The results in these real-world
datasets show the superior performance of our approach in comparison with the
state-of-the-art methods.

该文介绍了一种名为 NHR 的神经混合推荐框架，可用于商品预测问题。该框架利用神经网络从同一和不同的数据源中提取更详细的信息，以及在同样的问题上使用不同的损失函数达到评级预测问题，测试结果在真实世界数据集中显示了优异的性能。