Towards an Extensible Context Model for Mobile User in Smart Cities

Abstract

In smarts cities environments, a recommender system (RS) has for goal to recommend relevant services to the user who is sometimes mobile. Thus, to be able to provide accurate personalized recommendations, the RS should be aware to the user’s context (preferences, location, activities, environment.,.), thereby, it should be Context-Aware Recommender System (CARS, for short). Therefore, the context modeling becomes crucial for developing CARSs. Although there is a lack of context models in the RS literature, several ones have been proposed in pervasive computing field. Nevertheless, most of them are dedicated for closed spaces and should be reviewed to be more suitable for open intelligent environments such as smart cities. This paper aims to propose an extensible ontology-based context model for representing contextual information within a smart city. The proposed context model would subsequently allow to design and develop CARSs.