In the landscape of contemporary recommender systems, user-item interactions are inherently dynamic and sequential, often characterized by various behaviors. Prior research has explored the modeling of user preferences through sequential interactions and the user-item interaction graph, utilizing advanced techniques such as graph neural networks and transformer-based architectures. However, these methods typically fall short in simultaneously accounting for the dynamic nature of graph topologies and the sequential pattern of interactions in user preference models. Moreover, they often fail to adequately capture the multiple user behavior boundaries during model optimization. To tackle these challenges, we introduce a boundary-aware Multi-Behavioral Dynamic Graph Transformer (MB-DGT) model that dynamically refines the graph structure to reflect the evolving patterns of user behaviors and interactions. Our model involves a transformer-based dynamic graph aggregator for user preference modeling, which assimilates the changing graph structure and the sequence of user behaviors. This integration yields a more comprehensive and dynamic representation of user preferences. For model optimization, we implement a user-specific multi-behavior loss function that delineates the interest boundaries among different behaviors, thereby enriching the personalized learning of user preferences. Comprehensive experiments across three datasets indicate that our model consistently delivers remarkable recommendation performance.

翻译：在当代推荐系统的研究领域中，用户-项目交互本质上是动态且序列化的，通常以多种行为为特征。先前的研究通过序列交互和用户-项目交互图，利用图神经网络和基于Transformer的架构等先进技术，探索了用户偏好的建模。然而，这些方法通常无法同时考虑图拓扑结构的动态性以及用户偏好模型中交互的序列模式。此外，它们在模型优化过程中往往未能充分捕捉用户多行为边界。为应对这些挑战，我们提出了一种边界感知的多行为动态图Transformer（MB-DGT）模型，该模型动态优化图结构以反映用户行为和交互的演化模式。我们的模型包含一个基于Transformer的动态图聚合器，用于用户偏好建模，该聚合器融合了变化的图结构和用户行为序列。这种集成产生了更全面、更动态的用户偏好表示。在模型优化方面，我们实现了一个用户特定的多行为损失函数，该函数界定了不同行为之间的兴趣边界，从而丰富了用户偏好的个性化学习。在三个数据集上的综合实验表明，我们的模型始终能提供卓越的推荐性能。