Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic’s influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model called Marked Point Processes with Memory-Enhanced Neural Networks (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users’ activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to \(76.5\% \), \(65.6\% \), \(77.2\% \), and \(57.7\% \), respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain.

标记时间点过程模型（MTPP）旨在对连续时间内的事件序列和事件标记（相关特征）进行建模。这些模型已应用于各种应用领域，在连续时间内捕获事件动态是有益的，例如教育系统、社交网络和推荐系统。然而，当前的MTPP存在两个主要限制，即事件动态对标记分布影响的低效表示以及在建模中丢失历史标记分布的细粒度表示。受这些限制的启发，我们提出了一种新颖的模型，称为中号装柜P哦int 进程中号埃默里-乙增强的氮神经网络t作品（MoMENt）可以捕获标记和事件动态之间的双向相互关系，同时提供细粒度的标记表示。具体来说，MoMENt 由两个并发网络构成：用于捕获模型事件动态的循环活动更新器 (RAU) 和用于表示标记的记忆增强标记更新器 (MEMU)。RAU 和 MEMU 组件都设计为在每一步相互更新，以模拟标记和事件动态的双向影响。为了获得制造商分布的细粒度表示，MEMU 设计有外部存储器，可以使用潜在分量向量对详细的标记级特征进行建模。我们对六个真实世界用户交互数据集进行的广泛实验表明，MoMENt 可以准确地表示用户的活动动态、提升时间、类型和标记预测，以及高达 \(76.5\% \)、\(65.6\与基线方法相比，分别为 % \)、\(77.2\% \) 和 \(57.7\% \)。此外，我们的案例研究表明，MoMENt 在随着时间的推移提供对用户系统关系的有意义且细粒度的解释方面的有效性，例如，用户选择如何影响他们在推荐领域的未来偏好。