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

Chenyun Li; Guanhong Zhang; Odbal H

doi:10.1117/12.3049595

8 November 2024 A heterogeneous graph neural network model with multi-head attention integrating review information for recommendation

Chenyun Li, Guanhong Zhang, Odbal H

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161N (2024) https://doi.org/10.1117/12.3049595
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyun Li, Guanhong Zhang, and Odbal H "A heterogeneous graph neural network model with multi-head attention integrating review information for recommendation", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161N (8 November 2024); https://doi.org/10.1117/12.3049595

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