A Context-aware Recommendation System using smartphone sensors
Xueyang Zou, Mariel Gonzales, Sara Saeedi · 2016
Nowadays, the ubiquity of mobile devices (such as smartphones and tablets) has encouraged the development of context-aware and personalized computation to filter the query results and provide recommendations based on different situations of a user. On the other hand, global positioning system (GPS) technology along with microelectromechanical system (MEMS) sensors enables location sensing on mobile devices. Context-aware computation can provide customized services in different contexts - where context is related to the user's location, activity and historical information. Our main focus in this paper, the Context-aware Recommendation System (Co-ARS), is one of the major applications that has been refined over the years due to the evolving geospatial technologies and data mining. The proposed Co-ARS application achieves the list of recommendations by utilizing the user's context information (such as location and preferred transportation mode), item's context information (such as restaurant ratings and types), and personalized preference information (based on individuals past behavior). In this paper, we have described the application of such a system in the City of Calgary using android smartphones. The implemented system used various data filtering and management techniques to provide beneficial and accurate recommendation results to the users.