iShopping: Nearest Shopping Location Identifier
Published on Sep 16, 2019
iShopping: Nearest Shopping Location Identifier is a Android project based on Mobile Commerce Explorer. 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.
With the rapid advance of wireless communication technology and the increasing popularity of powerful portable devices, mobile users not only can access worldwide information from anywhere at any time but also use their mobile devices to make business transactions easily, e.g., via digital wallet. Meanwhile, the availability of location-acquisition technology, e.g., Global Positioning System (GPS), facilitates easy acquisition of a moving trajectory, which records a user movement history.
DISADVANTAGES OF EXISTING SYSTEM:
Do not know the relations between the items in the different levels
An object’s movements are more complicated than what the mathematical formulas can represent.
However, there is no work consider user relations in the mobile pattern mining.
We envisage that, in the coming future of Mobile Commerce (M-Commerce) age , some m-commerce services will be able to capture the moving trajectories and purchase transactions of users. Take the recent announced Shopkick as an example, it gives mobile users rewards and offers when users checkin in stores and on items. Anticipating that some users may be willing to exchange their locations and transactions for good rewards and discounts, we expect more mobile commerce applications, whether they will bear a business model similar with Shopkick or not, will appear in the future. In this paper, we aim at developing pattern mining and prediction techniques that explore the correlation between the moving behavior and purchasing transactions of mobile users to explore potential M-Commerce features.
Our proposed system includes:
1) Similarity Inference Model 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.
ADVANTAGES OF PROPOSED SYSTEM:
We propose the MCE framework, a new approach for mobile commerce behavior mining and prediction. The problems and ideas in MCE have not been well explored in the research community.
We propose a novel model SIM for automatically measuring the similarities among stores and items from a mobile transaction database.
To understand the personal mobile behaviors, we propose a novel algorithm PMCP-Mine for mining PMCPs from a mobile transaction database
Based on the SIM and PMCP-Mine, we propose a novel prediction technique MCBP for precisely prediction of the mobile behaviors which include movements and transactions of a user.
We design a simulation model and conduct a series of experiments to evaluate the performance of our proposal. The results show superior performance over other mining techniques in terms of predictive precision and recall.
1. Mobile network database
2. Data mining mechanism
3. personal mobile commerce patterns
4. Behavior prediction engine
Mobile network database
The mobile network database maintains detailed store information which includes locations.
Data mining mechanism
Our system has an “offline” mechanism for similarity inference and PMCPs mining, and an “online” engine for mobile commerce behavior prediction. When mobile users move between the stores, the mobile information which includes user identification, stores, and item purchased are stored in the mobile transaction database. In the offline data mining mechanism, we develop the SIM model and the PMCP-Mine algorithm to discover the store/item similarities and the PMCPs, respectively.
Personal mobile commerce patterns
A Frequent-Transaction is a pair of store and items indicating frequently made purchasing transactions.
Behavior prediction engine
In the online prediction engine, we propose a mobile commerce behavior predictor (MCBP) based on the store and item similarities as well as the mined PMCPs. When a mobile user moves and purchases items among the stores, the next steps will be predicted according to the mobile user’s identification and recent mobile transactions. The framework is to support the prediction of next movement and transaction.
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
Operating System : Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : Java, JSP
Server side Script : Java Server Pages.
Database : MYSQL