Modeling higher-order social influence using multi-head graph attention autoencoder

• Published in: Information systems (Oxford) Vol. 128; p. 102474
• Main Authors: Meydani, Elnaz, Duesing, Christoph, Trier, Matthias
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Attention mechanism; Graph attention networks; Graph autoencoders; Social recommender systems; Graph autoencoders; Attention mechanism; Graph attention networks; Social recommender systems
• ISSN: 0306-4379
• DOI: 10.1016/j.is.2024.102474

Recommender systems are powerful tools developed to mitigate information overload in e-commerce platforms. Social recommender systems leverage social relations among users to predict their preferences. Recently, graph neural networks have been utilized for social recommendations, modeling user-user social relations and user–item interactions as graph-structured data. Despite their improvement over traditional systems, most existing social recommender systems exploit only first-order social relations and overlook the importance of social influence diffusion from higher-order neighbors in social networks. Additionally, these techniques often treat all neighboring nodes equally, without highlighting the most influential ones. To address these challenges, we introduce GATE-SR, a novel model that leverages a multi-head graph attention autoencoder to capture indirect social influence from higher-order neighbors while emphasizing the most relevant users. Moreover, we incorporate implicit social connections derived from coherent communities within the network. While GATE-SR performs comparably to baseline models in rich data environments, its strength lies in excelling at cold-start scenarios—where other models often fall short. This focus on cold-start performance aligns with our goal of building a robust recommender system for real-world challenges. Through extensive experiments on three real-world datasets, we demonstrate that GATE-SR outperforms several state-of-the-art baselines in cold-start scenarios. These results highlight the crucial role of accentuating the most influential neighbors, both explicit and implicit, when modeling higher-order social connections for more accurate recommendations. •Varied attention enhances recommendations by assigning importance to neighbors.•Autoencoder’s stacked layers model high-order social relations effectively.•Community detection cuts over-individualized recommendations, optimizing complexity.•Mitigate data sparsity using implicit social connections.•Adept at mitigating cold-start probelm, emphasizing higher-order social influence.