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A Dynamic Community-Based Personalization for e-Government Services

The government portals and websites contain a wide range of information and services to help citizens and businesses comply in ease with the government requirements. One of the most important challenges for e-Government services is matching particular needs and interests of citizens and businesses to achieve efficient e-Government services quality. Recommender systems have been proposed to deliver users with the most interesting information service thereby addressing the information overload problem. In this paper, we focus on the implementation of a recommender system in an e-Government context to provide personalized G2C e-services. The proposed approach combines the expert pre-defined categorization of the e-government services and the citizens rating history to identify the relevant services for the active citizen. The efficiency of the proposed e-Government services personalization method is studied via a comparative study with the benchmark item-based and a state-of-the-art recommender system for Government-to-Business e-services. Experimental results show a considerable improvement for the proposed recommendation approach in term of performance and accuracy.