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.
Highlights
Nowadays mobility has become a major concern in cities mainly because of the problems generated by the excess of population such as traffic jams and parking problem
The architecture is based on the main literature works of the last few years that have focused on context processing (Cao et al, 2015, Liu, Guo, 2017), but unlike the architectures proposed in these works, our architecture obeys to a context model and focuses on the collection of the context parameters mentioned in the proposed model
We proposed in this paper a general architecture for context data collection and processing
Summary
Nowadays mobility has become a major concern in cities mainly because of the problems generated by the excess of population such as traffic jams and parking problem. One of the main objectives of smart cities is to improve mobility and solve its problems which affect the quality of life of citizens and the environment (pollution problems). Mobility is improved by exploiting different mobility data of citizens to produce services. Among these services we find mobile personalized service recommendation systems that aim to recommend the right service to the right person in the right time. To design personalized mobile service recommendation systems two challenges must be faced: defining the context of the mobile user and designing an architecture for context data collection and processing.
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