Modeling higher-order social influence using multi-head graph attention autoencoder
Year: 2024
Type: article
Abstract: 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.
Source: Information Systems
Institutions: Paderborn University, Bielefeld University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
OpenAlex ID: W4403147914