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：近期推荐系统的发展聚焦于基于序列与基于图的方法。两种方法在建模行为数据中的复杂关系方面均展现出有效性，在个性化排序和下一项推荐任务中取得了良好成果，同时保持了优异的可扩展性。然而，它们从数据中捕捉的信号截然不同：前者通过用户与近期物品的有序交互直接表征用户，后者则旨在捕捉交互图中存在的间接依赖关系。本文提出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。在多个数据集上的实验评估表明，通过该框架对序列组件与图组件进行联合训练，能够显著提升推荐性能。