Embedding plays a critical role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision models. In this paper, we propose a novel embedding update mechanism, Structure-aware Embedding Evolution (SEvo for short), to encourage related nodes to evolve similarly at each step. Unlike GNN (Graph Neural Network) that typically serves as an intermediate part, SEvo is able to directly inject the graph structure information into embedding with negligible computational overhead in training. The convergence properties of SEvo as well as its possible variants are theoretically analyzed to justify the validity of the designs. Moreover, SEvo can be seamlessly integrated into existing optimizers for state-of-the-art performance. In particular, SEvo-enhanced AdamW with moment estimate correction demonstrates consistent improvements across a spectrum of models and datasets, suggesting a novel technical route to effectively utilize graph structure information beyond explicit GNN modules.

翻译：嵌入在现代推荐系统中扮演关键角色，因为它们是真实世界实体的虚拟表示，也是后续决策模型的基础。本文提出一种新颖的嵌入更新机制，即结构感知嵌入演化（简称SEvo），以鼓励相关节点在每一步中同步演化。与通常作为中间环节的图神经网络（GNN）不同，SEvo能够在训练中以可忽略的计算开销直接将图结构信息注入嵌入中。我们从理论上分析了SEvo及其可能变体的收敛性质，以证明其设计的有效性。此外，SEvo可无缝集成到现有优化器中，实现最先进的性能。特别地，具有动量估计校正的SEvo增强型AdamW在多种模型和数据集上均表现出持续改进，这为超越显式GNN模块而有效利用图结构信息提供了一条新颖的技术路线。