Work

Relational Perception and Semantic Perception-Based Heterogeneous Graph Neural Network for Product Recommendation

Year: 2025

Type: article

Abstract: To address the complexity of user-product interactions and semantic diversity in multi-relational heterogeneous graphs, this paper proposes a heterogeneous graph neural network recommendation framework (RSA-HGNN4Rec) that integrates relational perception and semantic perception. The model realizes fine-grained relational interaction modeling and dynamic high-order semantic path learning through a dual-path mechanism, and introduces a gating fusion mechanism to adaptively integrate multi-source information. To further validate the model efficiency, this paper compares the computational complexity of RSA-HGNN4Rec with mainstream models and provides a visualization analysis of the semantic path attention weights. Experiments on multiple real datasets, including Amazon-MR, ACM-MR, and DBLP-MR, demonstrate that RSA-HGNN4Rec significantly outperforms mainstream baseline models in terms of Recall@20 and NDCG@20 metrics for product recommendation tasks, while also demonstrating excellent generalization ability and interpretability on node classification tasks.

Source: IEEE Access

DOI: 10.1109/access.2025.3648703

Authors: Yu Wang, Haibo Zhang, Nan Li

Institution: Dalian University of Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Shahjalal University of Science and Technology