Graph Neural Networks for User Preference Prediction in Multi-Domain Recommendation Systems
Keywords:
graph neural networks; multi-domain recommendation; user preference modeling; recommendation infrastructure; fairness; data governanceAbstract
Multi-domain recommendation systems must reconcile heterogeneous user interaction traces, item catalogs, contextual signals, and business constraints within a coherent infrastructure. This paper presents a system-level examination of graph neural networks for user preference prediction across domains. Rather than focusing on isolated model accuracy, the discussion addresses how relational inductive biases enable unified representations of users, items, attributes, contexts, and cross-domain transitions. The paper examines architectural choices for message passing over large-scale heterogeneous graphs, the incorporation of categorical and context-aware query signals, and the implications of multi-domain data sharing for privacy, governance, and operational stability. It further analyzes structural trade-offs in balancing domain-specific specialization with shared preference knowledge, the allocation of computational resources in continuous training pipelines, and the management of concept drift across catalogs with different temporal dynamics. Fairness and robustness receive particular attention because multi-domain graphs may propagate bias through cross-domain edges and create feedback loops that affect downstream user exposure. The paper also considers sustainability through sampling strategies, sparsified propagation, and lifecycle-aware system design. The conclusion identifies open challenges for future research, including cross-domain causal identification, federated graph learning, and accountable personalization governance. Overall, the paper argues that graph neural networks offer a promising systemic foundation for multi-domain recommendation but require careful architectural, infrastructural, and policy-level design to realize their benefits responsibly.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
