06-02-2013, 03:30 PM
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 ðSIMÞ 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 (PMCPMine)
algorithm for efficient discovery of mobile users’
Personal Mobile Commerce Patterns (PMCPs); and 3)
Mobile Commerce Behavior Predictor ðMCBPÞ 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.
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 ðSIMÞ 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 (PMCPMine)
algorithm for efficient discovery of mobile users’
Personal Mobile Commerce Patterns (PMCPs); and 3)
Mobile Commerce Behavior Predictor ðMCBPÞ 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.