Many recent advancements in recommender systems 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 one aims to capture indirect dependencies across the interactions graph. This paper presents a novel multi-representational learning framework that exploits the synergies between these two paradigms. 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：近期推荐系统的诸多进展集中于开发基于序列和基于图的方法。这两种方法均被证明有助于建模行为数据中的复杂关系，在个性化排序和下一项推荐任务中取得了有前景的结果，同时保持了良好的可扩展性。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示用户，后者则旨在捕获交互图中跨项目的间接依赖关系。本文提出了一种新颖的多表示学习框架，利用这两种范式之间的协同效应。我们在多个数据集上的实证评估表明，通过该框架对序列组件和图组件进行联合训练，能够显著提升推荐性能。