Recommendation system using location-based ontology on wireless internet: An example of collective intelligence by using ‘mashup’ applications

Location-Based Service (LBS) is considered as a key component of upcoming ubiquitous environments. A recommendation system based on LBS is expected to be an important service in ubiquitous environments, and most hardware technologies such as location estimation of a user by using Global Positioning System (GPS), as well as hi-speed internet access through cell phones, are currently supported. However, in terms of software, most services are driven and supported by a LBS service provider only. Consequently, lack of participation of users may occur in mobile environments. In this study, we suggest a LBS knowledge base inference platform with ontology which considers the current location and available time of users. Our knowledge base supports user participation as collective intelligence. We mashed up Open Application Programming Interface (OpenAPI) for scalable implementation of the system. Through experiments, we show that a user can build up his/her knowledge base, and by using this information, the system recommends to other users appropriate information that matches the user’s condition and profile through inference.