A Context-Aware Mashup Recommender Based on Social Networks Data Mining and User Activities

eGovernment in the new context of smart cities aims at improving the participation model of citizens that are no longer mere consumers of services designed and offered by public bodies. The availability of (open) data from the PA and third party organizations, which can be made public and widely accessible, introduces a significant paradigm shift from citizen-centric to citizen-driven eGovernment. In this direction, the mashup model (for client-side composition), with the ability to use widespread and relatively simple Web technologies, seems to be a viable approach for simplifying service creation. To ensure an adequate penetration of this new way of service offering, social-aware service discovery techniques assume a crucial role. In the paper, we propose a recommender that offers, in addition to the conventional search techniques, some support for context- aware implicit search of services, based on social information. The ability to leverage the data characterizing the activity of users in the network easies service selection: social relationships and the potential behavioral similarities between people, or in general among users linked by similar interests, enable the inference of further behavior details when they are not directly retrievable from static or even dynamic user profiles.