ConsumSense: A Framework for Physical Consuming Behavior Prediction on Smartphones

Abstract

Automatic track and prediction of consumer behaviors involve huge commercial interests and have been studied extensively in the past. We have seen very successful application of such techniques on online consuming behaviors. However, it is still very challenging to predict consumer behaviors in physical stores, because many operations in the middle are not digitized. As smartphones are becoming indispensible in our daily life, we propose to build a framework, called ConsumSense, into the smartphones that observes in close-up the physical consuming behavior of the user and predicts his/her future purchases. Our framework addresses two difficult issues: (1) how to use the limited number of sensors on a smart phone to observe and predict the consuming behavior of its user? (2) how to verify and correlate the purchases? To demonstrate the feasibility of the proposed framework, we have developed the framework on Android and evaluated it by asking 14 participants to conduct a 3-week experiment by using Easy card during their daily life. The results show that time and location are the most important contexts for predicting consuming activities and our framework can achieve a 76% prediction accuracy.