Designing a COntext REcommendation System (CORES) for context prediction based on the Dempster-Shafer theory

Abstract

Context prediction and service recommendation is a method that can be used to support strategic decision making through the acquisition and utilization of information pertinent to the context of the environment, user, or mobile device. Those are essential for satisfying customers by providing highquality intelligence services. This paper proposes a novel COntext REcommendation System (CORES), which is a decision making methpd for predicting a user's mobility by analyzing and synthesizing existing data, context information, and user preferences based on Dempster-Shafer evidence theory. CORES, developed with R and Visual C++ programming language, suggests the user's next location based on a maximum belief value that is calculated by combining bpas, the probabilities for each candidate's next location. The paper concludes with a discussion of implications of the experimental results. © 2011 International Information Institute.