Enhancing graph transformer encoding with graph heterogeneous memory for improved recommendation performance.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Recommender systems combat information overload but struggle with data sparsity.
• Existing graph neural networks (GNNs) often handle either complex relationships or user history, not both.
• A gap exists in models that can simultaneously process heterogeneous graph structures and temporal user dependencies.

Purpose of the Study:

• To propose a novel architecture, the Heterogeneous Graph Memory Transformer (HMT), for improved recommender systems.
• To address the limitations of existing GNNs in handling both heterogeneous graph data and user interaction histories.
• To generate more robust and accurate user and item embeddings by integrating diverse data sources.

Main Methods:

• Developed the Heterogeneous Graph Memory Transformer (HMT) architecture.
• Integrated a heterogeneous graph transformer with graph memory modules.
• Employed HMT to concurrently learn from heterogeneous graphs and user interaction histories.

Main Results:

• Achieved state-of-the-art performance on benchmark datasets (Amazon, iFashion, Yelp2018) with N@5 scores of 0.3295, 0.4273, and 0.2748.
• Demonstrated superior performance compared to strong baseline models.
• Confirmed robustness against data imbalance and noise.

Conclusions:

• The HMT framework offers a significant advancement for recommender systems.
• HMT effectively handles complex, heterogeneous data and user dependencies.
• This approach paves the way for next-generation recommender systems in dynamic ecosystems.