Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to capture indirect dependencies across the interactions graph. This paper presents a novel multi-representational learning framework exploiting these two paradigms' synergies. Our empirical evaluation on several datasets demonstrates that mutual training of sequential and graph components with the proposed framework significantly improves recommendations performance.

翻译：近期推荐系统的研究进展聚焦于基于序列与基于图的方法。这两种方法在建模行为数据中的复杂关系方面均展现有效性，在保持良好可扩展性的同时，为个性化排序和下一项推荐任务带来了显著成果。然而，它们从数据中捕获的信号截然不同：前者通过用户与近期物品的有序交互直接表征用户，后者则旨在捕获交互图中存在的间接依赖关系。本文提出了一种新颖的多表示学习框架，充分利用这两种范式的协同效应。我们在多个数据集上的实证评估表明，通过所提框架对序列与图组件进行联合训练可显著提升推荐性能。