Users` Moving Patterns Analysis for Personalized Product Recommendation in Offline Shopping Malls
Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users` propensity accurately and to provide personalized recommendation services in real time, because they have difficulty in considering users situation as moving path. In this paper we use users` moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users` moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users` high preference in real time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.