Prediction of M-commerce user behavior by pattern mining

Information plays a major role in any organization. Due to a wide range of potential applications, research on mobile commerce has received a lot of interests from both of the industry and academia. Among them, one of the active topic areas is the mining and prediction of users’ mobile commerce behaviors such as their movements and purchase transactions. In this paper, we propose a novel framework, called Mobile Commerce Explorer (MCE) which is a new approach for mobile commerce behavior mining and prediction. The MCE framework consists of components like Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users’ Personal Mobile Commerce Patterns (PMCPs); and Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. The proposed work is to recommend stores and items previously unknown to a user. The framework MCE achieves a very high precision in mobile commerce behavior predictions and the experimental results show that our proposed framework are highly accurate under various conditions. We have conducted experiments that implement our approach on real-life aggregated data and the results support the viability of our integration approach as well as the appropriateness of extended association rules. We also intend to undertake a further performance study with larger data sets, using different hardware platforms and various types of indexes.