An efficient mobile commerce explorer for mobile user's behavior pattern mining and prediction

Abstract

Due to 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 user's mobile commerce behaviors such as their movements and purchase transactions. In this paper, we explore a new data mining capability for a mobile commerce environment based on location based service (LBS). A novel framework, called Mobile Commerce Explorer (MCE) used for mining and prediction of mobile users' movements and purchase transactions under the context of mobile commerce. The MCE framework consists of three main components like 1) Similarity Inference Model (SIM)measuring the similarities among stores and items; 2) Personal Mobile Commerce Pattern Mine (PMCP-Mine) algorithm used for finding the mobile user's Personal Mobile Commerce Patterns (PMCPs); 3) Mobile Commerce Behavior Predictor (MCBP) for prediction the future mobile user behaviors. Location-based service (LBS) is used for recommending the stores and items previously unknown to a user. The Administrator controller will monitor the whole transaction process, so there will be a secure transaction.