Mobitrack PhD - Skattefunn

Offentlig prosjektsammendrag

In recent years, the rapid development of mobile technology has led to unprecedented opportunities to realise intelligent environments. The widespread adoption of mobile phone platforms with ever more powerful sensors and processing capabilities has created new opportunities for context aware solutions. This type of environment will enable the user to receive information based on the location and behaviour of the user. Mobile recommender systems, acting as information analyzers, can support the decision making process of users by providing suggestions related to the environment the user is moving through. For example, in a shopping zone, such systems recommend items that are of potential use for customers using both context and behavioral input. A key source of input information for recommender systems is the real-time location of the user (i.e., the real-time location and orientation of user’s smart phone). Adoption of this approach has until recently been hampered by the lack of an accurate indoor positioning system for smartphones. Using an in-door real-time tracking system that can track the location of a phone with precision on the centimeter level, allows recommender systems to point users more accurately to shopping items of interest. Further, the high resolution location information can be of help to analyze a user’s shopping behavior, and to offer better learning mechanisms for context-aware mobile shopping.