A smart city mobile application for multitype, proactive, and context-aware recommender system

This paper presents a design and implementation of a multitype, proactive and context-aware recommender system in the environment of Internet of Things (IoT). The main features of this recommender system includes the consideration of the context of the user in recommendation, the ability to recommend multi-types in the same applications such as Restaurant, Gas Station, Attraction... etc... Also, the proposed recommender system is proactive, where the recommendations are pushed to the user without explicit query by him/her. The system was trained and tested. Then it was developed as Android application and tested by 50 users who filled a survey. The results show that the system got an overall accuracy of 91.2%. Also, as a mobile application, the majority of the users found this application useful in daily life (92%), support smart city operation (92%), and would recommend the application for others (86%).