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arXiv · 2412.04276

Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems

Yuwei Cao·Liangwei Yang·Zhiwei Liu·Yuqing Liu·Chen Wang·Yueqing Liang·Hao Peng·Philip S. Yu
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Abstract

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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Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu. 2024-12-05. Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems. https://doi.org/10.1145/3701716.3715498

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