TY  - EJOU
AU  - Li, Siyue 
AU  - Jin, Tian 
AU  - Wang, Erfan 
AU  - Tao, Ranting 
AU  - Lu, Jiaxin 
AU  - Xi, Kai 

TI  - DMGNN: A Dual Multi-Relational GNN Model for Enhanced Recommendation
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 2
SN  - 1546-2226

AB  - In the era of exponential growth of digital information, recommender algorithms are vital for helping users navigate vast data to find relevant items. Traditional approaches such as collaborative filtering and content-based methods have limitations in capturing complex, multi-faceted relationships in large-scale, sparse datasets. Recent advances in Graph Neural Networks (GNNs) have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks. However, existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships, leading to reduced recommendation diversity and limited generalization. To address these challenges, this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions. Our approach constructs two complementary graph structures: a User-Item Interaction Graph (UIIG), which explicitly models direct user behaviors such as clicks and purchases, and a Relational Association Graph (RAG), which uncovers latent associations based on user similarities and item attributes. The proposed Dual Multi-relational Graph Neural Network (DMGNN) features two parallel branches that perform multi-layer graph convolutional operations, followed by an adaptive fusion mechanism to effectively integrate information from both graphs. This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns. Extensive experiments conducted on benchmark datasets—including MovieLens-1M, Amazon-Electronics, and Yelp—demonstrate that DMGNN outperforms state-of-the-art baselines, achieving improvements of up to 12.3% in Precision, 9.7% in Recall, and 11.5% in F1 score. Moreover, DMGNN significantly boosts recommendation diversity by 15.2%, balancing accuracy with exploration. These results highlight the effectiveness of leveraging hierarchical multi-relational information, offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems. Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized, diverse, and robust recommender systems.
KW  - Recommendation algorithm; graph neural network; multi-relational graph; relational interaction

DO  - 10.32604/cmc.2025.066382