• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.80-89
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• 2014

In this paper, we conceive a context-aware recommendations system for water distribution in future smart water grids, with taking the end users' profiles, water types, network conditions into account. A spectral clustering approach is developed to cluster end users into different communities, based on the end users' common interests in water resources. A back-propagation (BP) neural network is designed to obtain the rating list of the end users' preferences on water resources and the water resource with the highest prediction rating is recommended to the end users. Simulation results demonstrate that the proposed scheme achieves the improved accuracy of recommendation within 2.5% errors notably together with a better user experience in contrast to traditional recommendations approaches.