Knowledge Graph-Enhanced Recommendation Systems for Personalized E-Commerce Intelligence
Keywords:
knowledge graph, recommendation system, e-commerce, personalization, graph neural network, fairness, scalability, governance, socio-technical infrastructure, policyAbstract
The rapid expansion of e-commerce platforms has intensified the demand for recommendation systems that transcend simple collaborative filtering and content-based approaches. Knowledge graphs offer a structured, semantically rich representation of entities and their relationships, enabling more interpretable and context-aware personalization. This paper presents a comprehensive examination of knowledge graph-enhanced recommendation systems from an interdisciplinary systems perspective, integrating insights from artificial intelligence, large-scale infrastructure design, socio-technical governance, and policy analysis. We begin by reviewing the architectural paradigms that underpin the fusion of knowledge graphs with deep neural networks and graph neural networks, highlighting the critical role of embedding techniques and attention mechanisms. The discussion then moves to structural trade-offs inherent in such systems, including the balance between expressiveness and computational scalability, the handling of heterogeneous and noisy data, and the tension between offline knowledge completeness and real-time inference demands. Governance and fairness considerations are addressed through the lens of algorithmic bias propagation within relational structures, the opacity of graph-based representations, and the challenges of ensuring equitable treatment across diverse user segments. Deployment challenges are explored in terms of infrastructure resilience, energy consumption of large graph models, and the sustainability of continuous knowledge updates. Policy implications are framed within the broader context of data sovereignty, privacy regulations such as the General Data Protection Regulation, and the accountability of automated decision-making in consumer markets. The paper concludes with forward-looking perspectives on hybrid human-AI curation, federated knowledge graph learning, and the evolution of recommendation ecosystems toward more transparent and robust intelligence. By synthesizing technical depth with socio-technical critique, this work aims to provide a foundational reference for researchers, engineers, and policymakers engaged in the development of personalized e-commerce intelligence.
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