2025

Journal

Journal of Supercomputing

In modern smart cities, user request handling has evolved beyond conventional software solutions and electronic services. Companies now leverage a diverse range of cutting-edge technologies, such as Artificial Intelligence, the Internet of Things (IoT), Edge/Fog computing, and Cloud computing, to deliver various smart services. These services cover domains such as smart healthcare, smart buildings, smart transportation, smart living, and smart administration. However, a significant challenge hindering the widespread adoption and effective deployment of these services is the limited awareness among citizens of their availability and features, including aspects like pricing models, security policies, and service-level agreements. This issue largely stems from the lack of centralized public repositories where companies can showcase their smart offerings. Consequently, citizens often rely on traditional Web search tools (e.g., search engines, social networks), which limits their ability to fully benefit from the advantages of smart services. In addition, users are often connected to multiple service providers, meaning that their related knowledge (e.g., profiles, service usage, ratings) is distributed across multiple information sources.
To bridge the gap between smart service providers and citizens, and to effectively utilize user and service information across multiple networks, we propose CrossRecSmart, a cross-network recommender system built upon a multiplex network of smart services. This system can be conceptualized as a large-scale, distributed, domain-specific marketplace for available smart services. Our approach integrates Graph Neural Networks (GNNs) with cross-network representation learning to construct this multiplex network. The complex and distributed nature of this task is addressed through a cross-network learning model that facilitates collaboration among multiple smart service providers and enables the aggregation of their knowledge. This is achieved by interconnecting multiple networks through bridge nodes, also referred to as anchor entities. Furthermore, we introduce an algorithm for the cross-network recommendation of top-rated smart services. Comparative analyses with existing recommender systems designed for smart cities demonstrate the superiority of our proposed approach, owing to the concept of anchor links.

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