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. A key source of input information for recommender systems is the real-time location of the user (i.e., the real-time location of user's smartphone). Adoption of this approach has until recently been hampered by the lack of an accurate indoor positioning system for smartphones. With an in-door real-time tracking system that can track the location of a phone with precision on centimeter level, recommender systems can use this high resolution location information to provide more accurate interest of the user to shopping items, analyze his/her shopping behavior, and offer better learning mechanisms for context-aware mobile shopping. Recently Sonitor has developed a solution that will realise this much coveted level of accuracy using a low cost infrastructure and full backwards compatibility with billions of smartphone devices running iOS or Android. In this project, we aim at exploiting this unique capability of Sonitor Ultimate Sense in order to provide more intelligent context-aware recommender systems and consequently make the end-user more satisfied with his/her, e.g., shopping experience, using these systems. This can also be used in predictive modelling (machine learning) to optimize product and advertisement placement in stores.
Project leader: Wilfred Booij
Institution: SONITOR IPS HOLDING AS