Article: Improved simplified swarm optimisation for bipartite graph convolutional network Journal: International Journal of Web and Grid Services (IJWGS) 2025 Vol.21 No.3/4 pp.328 - 355 Abstract: Bipartite graphs have been widely applied in data mining to represent data relationships, such as in e-commerce recommendation systems. Graph neural networks (GNNs), with their powerful ability to process structured data and explore higher-order information, have become the state-of-the-art method for recommendation problems. Recommendation systems increasingly rely on graph structures to represent relationships between users and items, like user click behaviours and purchase records. Through graph convolutional networks (GCNs), these structures capture connections between users and items, integrating structural information (e.g., user-item links) with node features (e.g., user preferences and item attributes) for accurate recommendations. This study combines improved simplified swarm optimisation (iSSO) with bipartite graph convolutional networks and eye-tracking technology to explore user preference behaviour, called iSSO-BGCN. We construct a node-feature bipartite graph, using iSSO's optimisation capabilities and natural gradient descent to train the model. Trials validate its ability to deliver precise recommendations. Inderscience Publishers - linking academia, business and industry through research

Title: Improved simplified swarm optimisation for bipartite graph convolutional network

Authors: Zhenyao Liu; Wei-Chang Yeh

Addresses: Integration and Collaboration Laboratory, Department of Industrial Engineering and Management Engineering, National Tsing Hua University, Hsinchu, Taiwan ' Integration and Collaboration Laboratory, Department of Industrial Engineering and Management Engineering, National Tsing Hua University, Hsinchu, Taiwan

Abstract: Bipartite graphs have been widely applied in data mining to represent data relationships, such as in e-commerce recommendation systems. Graph neural networks (GNNs), with their powerful ability to process structured data and explore higher-order information, have become the state-of-the-art method for recommendation problems. Recommendation systems increasingly rely on graph structures to represent relationships between users and items, like user click behaviours and purchase records. Through graph convolutional networks (GCNs), these structures capture connections between users and items, integrating structural information (e.g., user-item links) with node features (e.g., user preferences and item attributes) for accurate recommendations. This study combines improved simplified swarm optimisation (iSSO) with bipartite graph convolutional networks and eye-tracking technology to explore user preference behaviour, called iSSO-BGCN. We construct a node-feature bipartite graph, using iSSO's optimisation capabilities and natural gradient descent to train the model. Trials validate its ability to deliver precise recommendations.

Keywords: graph neural networks; GNNs; graph convolutional networks; GCNs; improved simplified swarm optimisation; iSSO; bipartite graph; recommendation system.

DOI: 10.1504/IJWGS.2025.150161

International Journal of Web and Grid Services, 2025 Vol.21 No.3/4, pp.328 - 355

Received: 16 Oct 2024
Accepted: 26 Feb 2025
Published online: 02 Dec 2025 *

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Title: Improved simplified swarm optimisation for bipartite graph convolutional network

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