A framework for personal mobile commerce pattern mining and prediction

Eric Hsueh Chan Lu*, Wang Chien Lee, S. Tseng

*Corresponding author for this work

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

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.

Original languageEnglish
Article number5728814
Pages (from-to)769-782
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume24
Issue number5
DOIs
StatePublished - 18 Apr 2012

Keywords

  • Data mining
  • mobile commerce.

Fingerprint Dive into the research topics of 'A framework for personal mobile commerce pattern mining and prediction'. Together they form a unique fingerprint.

  • Cite this