Abstract
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.
Abstract (translated)
顺序推荐系统（SRSs）旨在通过全面建模用户项交互序列中嵌入的复杂偏好来预测可能引起用户兴趣的后续项目。然而，大多数现有的SRS通常基于项目ID信息建模用户的单一低级偏好，而忽略了由项目属性信息揭示的高级别偏好，例如项目类别。此外，它们经常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，在本文中，我们提出了一种新颖的层次化偏好建模框架，以显著地对复杂的低级和高级别偏好动态进行建模，从而实现准确的顺序推荐。具体来说，在该框架中，设计了一种新的双变换器模块以及一种新的双重对比学习方案，分别区别性地学习用户的低级和高级别偏好，并有效增强低级和高级别的偏好学习。此外，还设计了一种新型语义增强上下文嵌入模块，以生成更具信息量的上下文嵌入，进一步提升推荐性能。在六个真实世界数据集上的广泛实验表明了我们提出的方法不仅优于现有的最先进方法，而且验证了我们的设计理念的合理性。
URL
https://arxiv.org/abs/2410.22790
