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.
@article{arxiv.2412.04276,
title = {Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems},
author = {Yuwei Cao and Liangwei Yang and Zhiwei Liu and Yuqing Liu and Chen Wang and Yueqing Liang and Hao Peng and Philip S. Yu},
journal= {arXiv preprint arXiv:2412.04276},
year = {2025}
}