Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.

翻译：序列推荐系统旨在通过全面建模嵌入在用户-项目交互序列中的复杂用户偏好，预测用户可能感兴趣的后续项目。然而，现有序列推荐系统通常仅基于项目ID信息建模用户的单一低层偏好，而忽略了由项目属性信息（如项目类别）所揭示的高层偏好。此外，这些系统往往利用有限的序列上下文信息来预测下一个项目，而忽视了更丰富的项目间语义关系。为此，本文提出了一种新颖的层次化偏好建模框架，以实质性建模复杂的低层与高层偏好动态，实现精准的序列推荐。具体而言，该框架设计了一个新颖的双重Transformer模块和一个创新的双重对比学习方案，分别用于区分性地学习用户的低层与高层偏好，并有效增强两个层次的偏好学习。此外，我们还设计了一个语义增强的上下文嵌入模块，以生成信息更丰富的上下文嵌入，进一步提升推荐性能。在六个真实世界数据集上的大量实验表明，所提方法在性能上优于现有最先进方法，并验证了设计方案的合理性。