HRMG-EA: Heterogeneous graph neural network recommendation with multi-level guidance based on enhanced-attributes

Abstract

Heterogeneous Graph Neural Networks are an efficient and powerful tool for modeling graph structure data in recommendation systems. However, existing heterogeneous graph neural networks often fail to model the dependencies between user and item attribute preferences, limiting graph structure optimization and consequently reducing the accuracy of recommendations. To overcome these issues, we propose a Heterogeneous graph neural network Recommendation with Multi-level Guidance based on Enhanced-Attributes (HRMG-EA). First, we design an attribute enhanced gated network to model user-item interaction attribute scenarios and obtain enhanced-attributes by capturing complex attribute dependencies. It effectively avoids the expansion of the graph scale in attribute graph scenarios and further covers personalized attribute relationship distribution characteristics of users and items. Then, we propose a novel multi-level graph structure guidance strategy based on enhanced-attributes. It guides graph structure learning from three optimization levels, optimizing from two perspectives: explicit (heterogeneity and homogeneity) and implicit (contrast enhancement). The former can screen higher-quality heterogeneous neighbor nodes in a direct interaction environment, and filter out redundant or erroneous edges under different similar semantic interest paths to improve the quality of the neighborhood environment. The latter aligns representation embeddings of enhanced-attributes and graph structure in a latent space, explores their potential commonalities, and obtains more comprehensive, fine-grained semantic and beneficial structural information. Finally, on two real-world datasets, HRMG-EA significantly outperforms the state-of-the-art baseline algorithms in both recall and normalized discounted cumulative gain. A large number of ablation experiments and analytical verifications also verify its effectiveness.