Cross-Domain Personalized Recommendation Model for Smart City Services Based on Multi-Source Data Fusion and Context-Aware Retrieval
Keywords:
smart city, multi-source data fusion, context-aware retrieval, cross-domain recommendation, personalization, system architecture, fairness, governanceAbstract
The proliferation of heterogeneous data streams in contemporary urban environments has created both an opportunity and a profound architectural challenge for intelligent service delivery. This paper presents a cross-domain personalized recommendation model for smart city services that integrates multi-source data fusion with context-aware retrieval to address the fragmentation of urban information silos. Moving beyond conventional single-domain recommendation architectures, the proposed framework synthesizes real-time sensor feeds, municipal administrative records, social media signals, and mobility traces into a unified semantic representation layer that preserves the ontological distinctiveness of each source while enabling cross-domain reasoning. The system-level design emphasizes structural trade-offs between latency, data freshness, and personalization accuracy, and it incorporates a modular orchestration of dense retrieval, knowledge graph grounding, and federated learning components to reconcile the tension between fine-grained personalization and urban-scale governance. We analyze the infrastructure demands of deploying such a model across cloud and edge tiers, discussing robustness under data drift, adversarial perturbations, and partial sensor outages. The paper further examines the fairness implications of cross-domain recommendation when services such as transportation, public health, and energy management are coupled, highlighting how disparate data provenance can encode systemic biases that propagate across interconnected recommendation pipelines. Through a discussion of evaluation strategies that combine offline simulation, online controlled experimentation, and regulatory sandbox approaches, we articulate a pathway toward sustainable, transparent, and accountable personalization in the smart city. The contribution lies not in a single algorithmic novelty but in a holistic system architecture and governance blueprint that treats cross-domain recommendation as a socio-technical infrastructure problem, where retrieval, fusion, and personalization must be continuously negotiated with privacy, fairness, and operational resilience.
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