Enhancing graph transformer encoding with graph heterogeneous memory for improved recommendation performance
Year: 2025
Type: article
Abstract: Recommender systems are crucial for mitigating information overload, yet their effectiveness is often constrained by data sparsity. While graph neural networks (GNNs) have shown promise, existing models face a critical limitation: they typically handle either complex heterogeneous relationships or capture historical user dependencies, but struggle to address both simultaneously. To bridge this gap, we propose the Heterogeneous Graph Memory Transformer (HMT), a novel architecture that synergistically integrates a heterogeneous graph transformer with dedicated graph memory modules. HMT is designed to concurrently learn rich semantic representations from heterogeneous graphs and preserve long-range contextual information from user interaction histories, thereby creating more robust and accurate user and item embeddings. Extensive experiments on three benchmark datasets-Amazon, iFashion, and Yelp2018-demonstrate HMT's superior performance. Notably, our model achieves state-of-the-art N@5 scores of 0.3295, 0.4273, and 0.2748, respectively, significantly outperforming strong baselines. Further analyses confirm the model's robustness against data imbalance and noise. The HMT framework offers a promising direction for developing next-generation recommender systems capable of contending with the complex and dynamic nature of real-world user-item ecosystems.
Source: Scientific Reports
Authors: Haiying Yu, Tianhe Hou, Yuxi Lin
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7117118782