A framework for personal mobile commerce pattern mining and prediction

Eric Hsueh Chan Lu, Wang-chien Lee, Vincent Shin Mu Tseng

Research output: Contribution to journalArticle

37 Citations (Scopus)

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

Fingerprint

Mobile commerce

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

@article{d1a5eba7598c499da44155256e607cc4,
title = "A framework for personal mobile commerce pattern mining and prediction",
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.",
author = "Lu, {Eric Hsueh Chan} and Wang-chien Lee and Tseng, {Vincent Shin Mu}",
year = "2012",
month = "4",
day = "18",
doi = "10.1109/TKDE.2011.65",
language = "English (US)",
volume = "24",
pages = "769--782",
journal = "IEEE Transactions on Knowledge and Data Engineering",
issn = "1041-4347",
publisher = "IEEE Computer Society",
number = "5",

}

A framework for personal mobile commerce pattern mining and prediction. / Lu, Eric Hsueh Chan; Lee, Wang-chien; Tseng, Vincent Shin Mu.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 5, 5728814, 18.04.2012, p. 769-782.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A framework for personal mobile commerce pattern mining and prediction

AU - Lu, Eric Hsueh Chan

AU - Lee, Wang-chien

AU - Tseng, Vincent Shin Mu

PY - 2012/4/18

Y1 - 2012/4/18

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84859728952&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859728952&partnerID=8YFLogxK

U2 - 10.1109/TKDE.2011.65

DO - 10.1109/TKDE.2011.65

M3 - Article

AN - SCOPUS:84859728952

VL - 24

SP - 769

EP - 782

JO - IEEE Transactions on Knowledge and Data Engineering

JF - IEEE Transactions on Knowledge and Data Engineering

SN - 1041-4347

IS - 5

M1 - 5728814

ER -