Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git.

翻译：基于图的方法与序列方法是两种流行的推荐范式，各自在其领域表现出色，但缺乏利用对方信号的能力。为解决这一问题，我们提出了一种整合两种范式以提升性能的新方法。我们的框架使用基于图神经网络（GNN）的推荐器和序列推荐器作为独立的子模块，同时共享一个联合优化的统一嵌入空间。为实现正向知识迁移，我们设计了一个损失函数，用于在子模块内部及子模块之间强制实施对齐性和均匀性。在三个真实世界数据集上的实验表明，所提方法显著优于单独使用任一范式，并取得了最先进的结果。我们的实现已在 https://github.com/YuweiCao-UIC/GSAU.git 公开提供。