Architecture for Prediction of Mobile Transaction Using Historical Mobile Location and Transaction Data

With the increase in the number of mobile commerce transaction, it is beneficial to have architecture to provide better mobile commerce experience and facilities to users. The locations and mobile commerce data generated by users can be analyzed using data mining techniques to arrive at similarity of items and stores. Similarity data clubbed with the patterns in historical transactions of user can be used for prediction of mobile transaction of the user. Here we propose a novel architecture called as Mobile Commerce Explorer. 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.