A heterogeneous graph neural network model with multi-head attention integrating review information for recommendation

Recommendation systems (RS) can significantly boost the profitability of e-commerce platforms. Traditional RS methods rely on users' historical behaviors and product attributes but struggle with data sparsity, cold start problems, and complex user-item interactions. Graph neural networks (GNNs) have become popular in RS due to their ability to capture complex relationships and features within graph-structured data, improving accuracy and robustness. This paper proposes a model that combines product review information with GNNs to enhance recommendations. We design a heterogeneous graph neural network with multi-head attention integrating review information (HG-MHAR). This model constructs a user-item heterogeneous graph with ratings as edges and includes review information to build user and item feature embeddings. The model has two main components: the review feature aggregation graph learning module and the multi-head attention graph learning module. These components capture multiple relational patterns in the graph in parallel, improving the stability and robustness of feature representations. Experiments on five Amazon datasets show that HG-MHAR outperforms benchmark models, improving nearly 4% over traditional methods. This demonstrates the potential of GNNs in recommendation systems.