Issue No.05 - May (2012 vol.24)
V. S-M Tseng , Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.65
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), for mining and prediction of mobile users' movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three major components: 1) Similarity Inference Model (SIM) for measuring the similarities among stores and items, which are two basic mobile commerce entities considered in this paper; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm for efficient discovery of mobile users' Personal Mobile Commerce Patterns (PMCPs); and 3) Mobile Commerce Behavior Predictor (MCBP) for prediction of possible mobile user behaviors. To our best knowledge, this is the first work that facilitates mining and prediction of mobile users' commerce behaviors in order to recommend stores and items previously unknown to a user. We perform an extensive experimental evaluation by simulation and show that our proposals produce excellent results.
user interfaces, data mining, inference mechanisms, mobile commerce, pattern classification, IEEE, personal mobile commerce, pattern mining, pattern prediction, user movement behavior, user mobile commerce behavior, purchase transaction behavior, mobile commerce explorer framework, similarity inference model, personal mobile commerce pattern mine, PMCP-Mine algorithm, mobile commerce behavior predictor, Mobile communication, Business, Data mining, Predictive models, Mobile computing, Transaction databases, Trajectory, mobile commerce., Data mining
V. S-M Tseng, "A Framework for Personal Mobile Commerce Pattern Mining and Prediction", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 5, pp. 769-782, May 2012, doi:10.1109/TKDE.2011.65