Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems

• URL: http://arxiv.org/abs/2412.04276v2
• Date: Wed, 29 Jan 2025 06:51:35 GMT
• Title: Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
• Authors: Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu, Chen Wang, Yueqing Liang, Hao Peng, Philip S. Yu,
• Abstract summary: Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone.
• Score: 51.716704243764994
• License:
• 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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