Context-aware service recommendation system using sensor data

Abstract

Background/Objectives: With the advent of the Internet of things, context awareness technology has been widely investigated to recognize changes in the environment and to provide services accordingly. Methods/Statistical analysis: However, providing an appropriate service to individual users is difficult because the system administrator determines the context awareness model. In addition, context awareness modeling based on ontology or collaborative filtering has a limitation in that it receives context-aware service only in a limited environment. Findings: A context awareness system provides appropriate services through the process of identifying certain circumstances. To this end, it should obtain and analyze data of the circumstances around users, which are transferred from the sensor network or their smartphones. A sensor data analysis technology that can facilitate this function is essential to establish the accurate context awareness. As users can prefer different services under the same circumstances, a method of adjusting their service use patterns by examining their preferences based on the circumstances identified is also required. In this study, an integrated interface of context awareness services using sensors is established to satisfy the demand for accurately analyzing sensor data mentioned above and applying user preference to service user patterns. Moreover, a system that receives and reflects user feedback to obtain and analyze the service use patterns of users, consider individual preferences, and recommend appropriate services to users is designed and implemented. Improvements/Applications: The service developer chooses sensors directly related to the service and determines whether to recommend the service according to the user's response, thereby providing the service that user needs. © 2016 Authors.