Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is ineffective since the prediction accuracy of future event occurrences for users and items with few observed interactions is low. On the other hand, multivariate Hawkes processes (MHPs) can be used to handle multi-dimensional random processes where different dimensions are correlated with each other. However, an MHP either fails to describe the correct mutual influence between dimensions or become computational inhibitive in most real-world events involving a large collection of users and items. To tackle this challenge, we propose local low-rank Hawkes processes to model large-scale user–item interactions, which efficiently captures the correlations of Hawkes processes in different dimensions. In addition, we design an efficient convex optimization algorithm to estimate model parameters and present a parallel algorithm to further increase the computation efficiency. Extensive experiments on real-world datasets demonstrate the performance improvements of our model in comparison with the state of the art.

中文翻译：

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本地低阶Hawkes流程，用于对时间的用户-项目交互进行建模

Hawkes流程在对多个反复出现的用户-项目交互事件进行建模时变得非常流行，这些事件在各个领域都表现出相互激励的特性。通常，将每个用户项目对的交互序列建模为独立的Hawkes流程是无效的，因为对于用户和很少观察到交互的项目，未来事件发生的预测准确性较低。另一方面，多元霍克斯过程（MHP）可用于处理不同维度相互关联的多维随机过程。但是，MHP要么无法描述尺寸之间的正确相互影响，要么在涉及大量用户和物品的大多数现实事件中成为计算抑制因素。为了应对这一挑战，我们提出了本地低等级的Hawkes流程，以对大规模的用户-项目交互进行建模，从而有效地捕获了不同维度的Hawkes流程的相关性。此外，我们设计了一种有效的凸优化算法来估计模型参数，并提出了一种并行算法来进一步提高计算效率。在现实世界的数据集上进行的大量实验表明，与现有技术相比，我们的模型在性能上有所提高。