Recommendised
E-Trade Through Web Data Mining
Dr.
Sandeep Gupta
Department of Computer Science
and Engineering, JIMS Engineering Management Technical Campus, Gr.noida (affiliated
to GGS IP University, New Delhi, India)
Sandeep.gupta@jagannath.org
I. INTRODUCTION
The aim of individualizing
information services is to provide users with individually tailored news and
information. It is to optimally support them retrieving information within
their scope of interest in the desired coverage, access time, and media.
Personalization is also about making online information easier to access, more
efficient in use, and providing an individual, personal online experience for
the users. Thereby personalization can be considered twofold: as a feature that
supports the information supply of a particular information provider, or as an
autonomous concept which is to grab and filter information and contents from
several information sources and individually present it to the users.
In either case the major
goal is to help the users getting the information they want in an efficient and
pleasing way. Yet the hurdle with achieving this goal is that the information
system must first know the users’ interests, needs, and preferences. Many of
the existing individualizing services put the strain of gathering this information on the users.
Users are confronted with incomprehensive registration forms, prompting the
users to explain their life story in data. Next they have to bother with
seemingly endless individualization and customization features, having to choose from dozens of check boxes
or enter cryptic search and keyword strings.
The result is that many
users either give up before having finished the individualization process or they end up
being frustrated because the effective results often they stay behind the
expectations of the users. Such systems seem to ignore that personalization is
not the actual goal task of the users but rather just getting the information
they are interested in.
II. RECOMMENDISED
E-TRADE
The
recommendised information service has its roots in the personalized navigation
system raised by Robert Armstrong and others of Carnegie Mellon University at
the National Conference on Artificial Intelligence (AAAI) in 1995.
Recommendised
information services are now an emerging research topic in global information
services. Conventional information services are giving gradually an approach to
individualize information services that, according to the interest, status and customized
needs of a customer.
Recommendised assistance
in e-trade mean that in the mode of e-trade a entrepreneur obtains his customer’s
data and access information by depending on his customers’ access to entrepreneur
website, and examines and exercises such information by using web data mining tools
to guide his enterprise decision so that based on the requirements of his customers
the entrepreneur could embark on e-trade activities, offer recommendised
information services, improve the awareness , satisfaction and loyalty of the
users and gain win-win for the entrepreneur and his customers (as shown in
figure1)
Figure1. Methodology for Recommendisation
Recommendised services in e- trade,
in nature, are web services centered on individualize requirements. In Figure
2, where “User Analysis Module” functions to learn the user’s features, create
user access model and offer recommendised services to the user by using
technology processing and web resources.
|
User Access
User
Individualized Services
Figure 2. User Analysis Module - Meaning of Recommendised Services in
E-trade
III. Web Data
Mining
As a key technology to provide
recommendised e- trade and help to collect individualize
information, web data mining can be used to analyze and examine user data,
create access model, requirement model and interest model that accord with user
features, making recommendised e-trade possible.
Oren EtZioni put forward the concept
of web data mining in his thesis in 1996 for the first time: applying data
mining methods to web helps to discover potential and useful models and
information.
According to data mining nature, web
data mining is classified as web content mining, web structure mining and web
usage mining. For more information, see Table 1. Web usage mining means that by
mining the log files and data at the corresponding site, we discover the nature
of visitors and users having access to this site. Data mining methods include
path analysis, association rules, classification rule, sequential patterns,
statistical analysis, dependent relationships modeling and cluster analysis
Classification
|
Secondary
Classification
|
Web Content Mining
|
Text Mining
|
Multimedia Planning
|
|
Web Structure Mining
|
Organizational Structure Mining
|
Page Structure Mining
|
|
Web Usage Mining
|
User Record Data Mining
|
Customization Mode Mining
|
Table
I. Classification of Web Data Mining
Data on web is unstructured, semi-structured and dynamic, so
web data mining has to go through the corresponding processing flow that is
composed of data positioning, data preprocessing, pattern recognition and
pattern analysis. In this process, we should first determine the source of
data, including web document, e- mail, website log data and transaction data.
Next, we should preprocess data, i.e., delete some redundant information and
unify information recognition, session recognition and transaction recognition,
and then carry out pattern recognition of the preprocessed data, i.e., use the
data mining tools to extract useful, potential and understandable information.
Finally, through pattern analysis, we can convert the filtered data into useful
rules and patterns to guide the practical e-trade activities.
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