On the Challenge of Service Recommendation to Mobile Users in Smart Cities: Context and Architecture

Abstract

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.