融合图与序列模型，提升推荐系统性能
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
- 双模块共享统一嵌入空间，联合优化
- 在三个真实数据集上超越单一方法
- 适合需要高精度推荐的场景
基于图和序列的推荐方法是两种主流范式，各自在特定领域表现优异，但难以利用对方信号。为此，我们提出一种新方法，将图神经网络（GNN）和序列推荐器作为独立子模块，共享统一嵌入空间并联合优化。为促进正向知识迁移，设计损失函数，在模块内部及跨模块间同时强制对齐与均匀性。在三个真实世界数据集上的实验表明，该方法显著优于单独使用任一方法，并达到当前最优水平。代码已公开于 https://github.com/YuweiCao-UIC/GSAU.git。
原文摘要 · Abstract (English)
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.
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