A mobile cloud framework for context-aware and portable recommender system for smart markets

Abstract

Smart city systems are fast emerging as solutions that provide better and digitized urban services to empower individuals and organizations. Mobile and cloud computing technologies can enable smart city systems to (1) exploit the portability and context-awareness of mobile devices and (2) utilize the computation and storage services of cloud servers. Despite the wide-spread adoption of mobile and cloud computing technologies, there is still a lack of solutions that provide the users with portable and context-aware recommendations based on their localized context. We propose to advance the state-of-the-art on recommender systems—providing a portable, efficient, and context-driven digital matchmaking—in the context of smart markets that involves virtualized customers and business entities. We have proposed a framework and algorithms that unify the mobile and cloud computing technologies to offer context-aware and portable recommendations for smart markets. We have developed a prototype as a proof-of-the-concept to support automation, user intervention, and customization of users’ preferences during the recommendation process. The evaluation results suggest that the framework (1) has a high accuracy for context-aware recommendations, and (2) it supports computation and energy efficient mobile computing. The proposed solution aims to advance the research on recommender systems for smart city systems by providing context-aware and portable computing for smart markets.