Personalization in smart urban environments: a taxonomy and survey of recommender systems

Abstract As cities adopt pervasive sensing, integrated data platforms, and AI, recommender systems are becoming central to shaping equitable, efficient, and citizen-focused urban services. This survey synthesizes peer-reviewed work across mobility, healthcare, energy, tourism, retail, and e-governance, offering a taxonomy linking collaborative filtering, content-based methods, hybrid designs, deep learning, and context-aware approaches to urban decision-making needs. Our review spans major smart-city domains, with most empirical studies in mobility and tourism, while deep and graph-based techniques remain unevenly distributed and comparatively rare in governance and energy. We examine how heterogeneous data sources, including IoT streams, geospatial signals, environmental indicators, and demographic attributes, are fused to support personalization under constraints such as latency, reliability, and privacy. The review highlights advances that address sparsity and cold start through graph neural models, sequence modeling, and transfer learning, and it covers operational enablers such as edge inference and streaming architectures for real-time recommendation. We assess risk and governance dimensions, including privacy preservation, fairness, exposure balance across neighborhoods, explainability, and mechanisms for audit and oversight. The survey identifies opportunities in pollution management, citizen education, and participatory platforms that broaden civic engagement. It also outlines how hybrid physical-virtual interactions, digital twins, immersive interfaces, generative models, and emerging quantum algorithms may reshape personalization and oversight in city-scale settings. Finally, we call for field evaluations and standardized benchmarks that jointly measure accuracy, latency, robustness to distribution shift, and equity.