AN EFFICIENT APPROACH FOR WEB-SITE ADAPTATION
Seema Jani, Sam Makki
Department of Electrical Engineering and Computer Science
University of Toledo, Toledo, Ohio, USA
Xiaohua Jia
Department of Computer Science
City University of Hong Kong, Kowloon, Hong Kong
Keywords: Web personalization, Data mining, Graph, Probability, Clustering
Abstract: This paper implements a novel approach defined as the Preference-function Algorithm (PFA) for web-site
adaptation. The algorithm extracts future preferences from the users’ past web navigational activities.
Server web logs are used to identify users’ navigation behaviours by examining the traverses of various web
pages. In this approach, the sessions are modeled as a finite state graph, where each visited web page is
defined as a state. Then, traversing among various states provides the framework for determining the
interest of the users.
1 INTRODUCTION
The continuous growth of the World Wide Web
poses many challenging issues for a person using it.
Most Web structures are large and complicated and
users often miss the goal of their inquiry, or receive
ambiguous results when they try to navigate through
the web pages (
Eirinaki et. al., 2003). The business
environment is such that in order to do well it is
important to meet the needs of the customers and
also create revenue by doing so. Therefore, the need
to retain the attention of the users of a web-site and
understand the needs of those users leads to the
importance of analyzing the users’ behavior. Once
the behavior of the users is known, a company can
use this information for a variety of objectives and
actions such as, to personalize the web-site, make
recommendations, enhance the web-site and target
advertise.
The objective of a Web personalization system
is to “provide users with information they want or
need, without expecting them to ask for it explicitly”
(
Mulvenna et. al., 2000). Personalizing a web-site
can be done in various ways. One way is to have the
information provided by the users via
questionnaires, surveys or registration forms.
Another is to use demographic, geographic, or
psychographic profiles or other information to
divide or segment large populations into smaller
groups. The last is to seek to understand the
behavioral preferences of a specific, individual user
and then deliver web-site content specifically
targeted at that person. This is known as web usage
mining (
Eirinaki et. al., 2003) and is the one that this
paper targets. This method is more dynamic in
nature and specifically takes into account the
navigational tendencies of the user within a specific
web-site.
Web usage mining can be regarded as a three-
phase process, consisting of the data preparation,
pattern discovery, and pattern analysis phases
(
Srivastava et. al., 2000). In the first phase, Web
server log data is processed in order to identify
users, sessions, pages viewed and so on. The first
phase, or data preparation phase, is one of the most
time-consuming, but technically straightforward
tasks. In the second phase, methods such as those in
the area of Statistics or Artificial Intelligence are
applied to detect interesting patterns. The second
phase is the most diverse and expanding of all three
phases and has been the subject of continuing
research and advancement. In the third phase of the
Web usage mining process, these patterns are stored
so that they can be further analyzed and applied.
108
Jani S., Makki S. and Jia X. (2005).
AN EFFICIENT APPROACH FOR WEB-SITE ADAPTATION.
In Proceedings of the Seventh International Conference on Enterprise Information Systems - DISI, pages 108-114
Copyright
c
 SciTePress

One main area of application is web-site adaptation,
where information obtained from phase two is used
in conjunction with specific web page relationships
and actions. The relationships and actions are
determined and provided by the owners of the web-
site.
2 RELATED RESEARCH
Pattern analysis in the context of Web usage mining
has been the subject of numerous research projects.
Two distinct directions are, in general, considered in
Web usage mining research: Statistics and Artificial
Intelligence techniques. The first approach using
Statistics consists of a range of applications from
overall analysis to an adapted version of statistical
data mining techniques (Srivastava et. al., 2000).
These data mining techniques are those that mine for
rules using the pre-defined support and confidence
values. The second approach uses Artificial
Intelligence techniques which draw upon methods
and algorithms developed from machine learning
and pattern recognition (Srivastava et. al., 2000).
2.1 Statistical Mining Techniques
The research and application of statistical techniques
in the analysis of web usage data is a wide area.
Three specific research areas are covered in this
section. These are the overall statistics of the web
usage data, probability analysis and standard data
mining techniques.
Statistical techniques are the most common
method to extract knowledge about the users of a
web-site. There are different levels of analysis
which have been used from the area of Statistics in
web mining. One general area is the use of overall
statistical information, which is given by some
software programs. Several commercial software
packages such as Analog (Analog, 2004) and OLAP
(The OLAP Report, 2005) are available for web log
analysis.
One more statistical approach which has been
taken in the past research is the use of probability in
the form of Markov chain modeling (
Borges, 2004).
Another well known area of Statistics which has
been used in the area of Web usage mining is the use
of data mining techniques. These techniques
involve finding association rules within a set of data
and mining by determining the rules based on the
rules’ confidence and support (
Agrawal et. al., 1994).
One example of work done in this area is with the
application of the “a priori” algorithm, in which the
association rule mining searches for relationships
between the items in the data.
2.2 Artificial Intelligence Techniques
The second type of technique involves the use of
Artificial Intelligence techniques such as clustering
and classification. Clustering and classification are
machine learning techniques that are used to group
together a set of items having similar characteristics.
In the Web Usage domain, there are two kinds of
interesting clusters to be discovered: page clusters
and usage clusters. Clustering of pages discovers
groups of pages having related content. Clustering of
users, however, tends to establish groups of users
exhibiting similar browsing patterns.
Classification is the task of mapping a data item
into one of several predefined classes. Classification
is done by using supervised inductive learning
algorithms such as decision tree classifiers, naïve
Bayesian classifiers, k-nearest neighbor classifiers,
Support Vector Machines, etc.
Mobasher (
Mobasher et.al., 1999) modeled the
profile based on the clustering approach and named
it PACT, which stands for Profile Aggregations
based on Clustering Transactions. The goal is to
effectively capture common usage patterns from
potentially anonymous click-stream data. The data
is preprocessed and then used to create the profile.
Preprocessing of the data is done in two steps:
identifying users and determining pageviews. First,
unique users are identified from the anonymous
usage data. Erroneous or redundant references are
removed from the data. Second, pageviews are
identified. Pageview identification is the task of
determining which page file accesses contribute to a
single browse display. Relevant pageviews are
included in
transaction files and weights are
assigned to reflect the significance of the pageview.
The clustering approach however requires that the
structure of the web-site to be known.
Classification is also used to determine the user
preferences (
Baglioni et. al., 2003). Classification
algorithms require training data as input. In this
example, the input is a set of cases whereby each
case specifies values for a collection of attributes
and for a class. The output of the classification
algorithm is a model that describes or predicts the
class value of a case on the basis of the values of the
attributes of the case. The predictive accuracy of the
extracted model is evaluated on a test set for which
the actual class is known.
This method requires registered users to provide
information about themselves. The registered users’
information is broken up whereby 67% become the
training set and 33% become the test set. The
attributes of a class consist of the site pages or
sections visited by the user and the class consists of
the user’s sex. In this case the goal is to accurately
AN EFFICIENT APPROACH FOR WEB-SITE ADAPTATION
109

User Profile
Figure 2: Step 2 in Preference-function Web Mining
Recommendations
Web-site
Scenario File
determine the user’s sex based on the web pages that
a user visited. The classification accuracy of this
approach is 54.8%. The major drawback is the
information about the users which has to be obtained
ahead of time.
3 OVERALL PROPOSAL
To extract the related information from the users’
log, we use a new approach called the Preference-
function Algorithm. The Preference-function
consists of two components defined as the Likeness-
factor and the Time-factor. The Likeness-factor
provides an overall idea as to whether or not a
particular web page has been visited frequently by
the user. The Time-factor provides the overall
interest of that web page. These two components
are further elaborated upon in the next few sections.
Also in this approach, knowledge of the structure of
the web-site is not required because this knowledge
will be gained from the user’s specific actions. The
overall process is divided into two steps as shown in
Figures 1 and 2.
In step 1, the user profile will be determined by first
preprocessing the data, which is also known as data
preparation, and then the specific usage mining will
be performed using the Preference-function. The
data preparation consists of creating useful
information from the log files. Nevertheless should
mention that, log files may contain many entries that
are irrelevant or redundant for the mining task.
Initially, the raw data is cleaned which includes
removing all redundant or unnecessary information.
All information relating to image files and map files,
which exist in the log files, is irrelevant to
identifying the user’s behavior. Then, the sessions
and transactions are identified based on the available
data. For our experiment we consider a session
length of a 6 hour period and will start when the first
page is requested from a designated web-site. The
end of the session will be determined when the user
leaves the web-site, or when the time on one web
page has exceeded 30 minutes. The 30 minute
timeout assumption is based on the results from
Catledge and Pitkow
(Catledge et. al., 1995). Various
transactions can occur during each session.
Individual entries for page accesses are grouped into
meaningful transactions. The transactions are first
defined as being unique and are grouped together
based on the IP addresses, since any access from
different IP addresses is identified as a different
transaction.
In step 2, as shown in Figure 2, involves making
recommendations based on a company-provided
scenario. In the case of this experiment, the web-site
owner will include suggested actions in the scenario.
3.1 Preference-function (PF)
Because the topology of the web-site is not known,
information on the server log will provide a way to
reconstruct a partial topology. The following
assumptions will provide a framework for analyzing
the server logs:
- A session is to start when the first page of a
designated web-site is requested from any IP
address.
- The beginning of a user session is
determined to be the first time a unique IP
address is observed.
- The end of the session will be determined
when the user has left the web-site or when a
timeout of 30 minutes has been exceeded on
one web page.
- Each unique IP address will be compared
with those that share the same operating
system.
- Previously visited web pages are allowed.
Relevant information will be extracted from the
server logs and combined to form a Preference-
function (PF). The Preference-function will be
composed of the multiplication of two variables:
Likeness-factor and Time-factor.
Web Server
Logs
Preprocess
ing
Cleaning
Session
Identificati
on User
Identi
f
icati
Preference
-function
Algorithm
User Session
File
User Profile
Figure 1: Step 1 in Preference-function Web Mining
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3.2 Likeness-factor (LF)
In this paper, the sessions are modeled as a finite
state machine
with each web page visited within a
web-site defined as a state. Two additional states
will be added, S and F, to denote the start and final
states. The transition from each state to the next will
be denoted as an edge on a weighted directed graph.
The Likeness-factor is determined by summing the
path weights (pw) of the individual paths that were
used to reach a particular state. The path weights are
defined as follows: each unique path toward a
particular state by definition will be given a partial
path weight of 1; any repetition that takes place,
such as leaving the current page and then returning,
will be given a partial path index of ½; and the total
path weight for the path is determined by summing
the values for the partial path weight of the unique
path plus each of the various detours. The Likeness-
factor (LF) is determined by adding various path
weights to a particular state.
The path weight of a
state (s) is determined in equation (1) where “n” is
the number of detours once a specified state is
reached:
(1)
∑
=
+=
n
i
is
pwpw
1
1
The formula for the Likeness-factor is given by
summing up all the path weights of all the paths (m)
to a particular state (s):
(2)
∑
=
=
m
j
sjs
pwLF
1
3.3 Time-factor (TF)
The reference length approach (Cooley et. al., 1997) is
based on the assumption that the amount of time a
user spends examining an object is related to the
interest of the user for the object’s contents. On this
basis, a model for user sessions is obtained by
distinguishing the navigational objects (i.e.,
containing only links interesting to the user) from
the content objects (i.e., containing the information
the user was looking for). The distinction between
navigational and content accesses is related to the
distance (in time) between one request and the next.
This Time-factor is added to the evaluation function
which was developed above in (1) and (2).
The Time-factor (TF) considers that, generally,
the more time a user spends on a web page, the
higher interest the user has for information on that
page. Therefore, the TF is determined by dividing
the time (sec) at a single web page (determined from
the web server logs) by the total time for that
session, where “l” is the number of sessions. The
formula for TF is shown in equation (3) where “l” is
the number of total states and “p” is the number of
times that the state “s” appears:
1
1
∑
∑
=
=
=
l
k
k
p
k
sk
s
t
t
TF
(3)
3.4 Overall Equation
From equations (2) and (3) we can compute the
Preference-function as follows:
Preference-function (PFw) = Likeness-factor
(LFw) * Time-factor (TFw) (4)
3.5 Example
Suppose we have a web-site which consists of web
pages A, B and C, and based on the server log it is
known that a unique user used the following paths:
{
}
{}
{}
{}
BAC
BCB
CCBCA
BCBA
→→
→→
→→→→
→→→
- Based on the first path, the path weights (pw)
would be calculated as follows:
2/1
1
=
=
→
→→→
BC
CBAs
pw
pw
The pwB1 for this path would be:
pwB1 = 1 + ½ = 1 ½
- For the second path the pw session
S
B
C
F
A
pw
BC
= 1/2
pw
SABC
= 1
AN EFFICIENT APPROACH FOR WEB-SITE ADAPTATION
111

pw
CB
= 1/2
S
B
C
F
A
pw
SBC
= 1
- The pwB2 for the second path would be:
pwB2 = 1
For the third path, pwB would be as follows:
The pwB3 for the third path would be:
pwB3 = 1 + ½ = 1 ½
- For the fourth path, pwB would be as follows:
- The pwB4 for the fourth path would be:
pwB4 = 1
- Therefore the Likeness-factor for site B would
be equal to
 LFB = pwB1 + pwB2 + pwB3 + pwB4 = 1
½ + 1 + 1 ½ + 1 = 5
- The overall graph is
Also based on the server log it is known that the user
spent 10 secs in A, 240 secs in B and 600 secs in C.
Then the TF for each of the web pages becomes
TF(A) = 10/850 = 0.012
TF(B) = 240/850 = 0.282
TF(C) = 600/850 = 0.706
Therefore the Preference Factor for webpage B
would be:
PFB = LFB * TFB
= 5 * 0.282
= 1.41
4 PROGRAMMING DESIGN AND
IMPLEMENTATION
4.1 Programming Language Choice
The programming language Java was used in
creating the Preference-function Web Mining.
Several other programming languages such as C++,
Visual Basic, Perl or Python could have been used
for this implementation.
The Preference-factor Algorithm program can be
shown as the following pseudo-code:
Begin {
Create GUI layout
Show GUI
Instantiate class variables
Wait for action from user
If
“Mine Logs” button pressed {
Display dialog box to choose file
Open Server Log File
Read Log File
pw BC = 1/2
S
B
C
F
A
pw
SACB
= 1
pw
CC
= 1/2
S
B
C
F
A
pw
CAB
= 1
S
B
C
F
A
pw
SABC
= 1 pw
SACB
= 1
pw
SBC
= 1 pw
SCAB
= 1
p
w
BC
= ½
p
w
CB
= ½
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Break up each line into individual components
Ip address, Date & Time, Command, Web page,
Status, Bytes, Name, Zone, Type, Reference,
Agent
Create new record with components Eliminate all
the unnecessary and null items(such as .gif, .jpg,
.css., .png, .ico, search type, .db
Create a session based on unique ip addresses
and times
Store within each session the web pages (states) and
clock time
Create a linked list of each session
Within each session create various paths taken by
the user
Determine the time in seconds for each state
Determine the Likeness-factor by following the
paths and determining if any states are visited
again
Determine the total time for the session
Determine the Time-factor for each web page by
dividing the time by the total time
Determine the Preference-function for each web
page by multiplying the Likeness-factor and the
Time-factor
Determine the highest and second-highest
preference factor and its web page
Append to text area in GUI
}
end if
If
“Web Site File” button pressed {
Display dialog box to choose file
Open web-site file
}
end if
If “Suggested Action” button pressed {
Read web-site file
Check to see if each line is equal to the web
page with the highest or second-highest
Preference-function
If equal then append text area with s
uggestions Close web-site file
}
end if
If “Exit” button pressed
Exit program
end if
End
5 EXPERIMENTAL PROCEDURE
5.1 Step 1: Server Log Analysis
Data preparation is one of the central tasks in web
mining (Baglioni et. al., 2003). In fact, it usually takes
up most of the time of the total analysis process.
First, unwanted requests, which are in the form of
rows, need to be filtered out. These are usually
image files, javascript files and style sheets, to name
a few. Next, any unwanted information about the
request needs to be filtered, such as the user
identification, request method and query strings.
The
following are several lines from the server log
file used for experimentation:
• localhost - - [04/Feb/2004:15:09:36 -0500]
"GET /phpdev/ HTTP/1.0" 200 383
• localhost - - [04/Feb/2004:15:09:36 -0500]
"GET /phpdev/yak1.htm HTTP/1.0" 200 1048
• localhost - - [04/Feb/2004:15:09:36 -0500]
"GET /phpdev/yak2.htm HTTP/1.0" 200 6798
5.2 Step 2: A Recommendation
Scenario
Ultimately the owner of the web-site has to give
guidelines regarding the correlations and importance
of web pages. This information is provided by the
owner in the action file. Below is a sample action
file for the sample web-site created for this project.
For example, in the first line the web page name is
‘\phpdev\’ and the action to be taken is ‘php books’.
/phpdev/
→
php books
/phpdev/yak1.htm
php books
→
/phpmyadmin/
php admin books
→
/AnalogX/
other web mining sites
→
/site/
Apache info
→
/private/
Apache info
→
/start_here.htm
Apache directory
information
→
/phpinfo.php
php books
→
5.3 An Example of Preference-
function Algorithm
A GUI was designed to provide for an easy
interaction between program and the user. There are
four actions that a user can take shown in Figure 3.
Figure 3: Four Actions
When the “Mine Logs” button is pressed, a dialog
box appears for the user to choose which server log
AN EFFICIENT APPROACH FOR WEB-SITE ADAPTATION
113

file to open. Once the file has been opened, the
results of the mining are shown.
Next, a web-site action file has to be selected.
This is done by pressing the “Web Site File” button.
Wh
Actions”
but
6 CONCLUSIONS
, information about
the users’ preferences makes designing web pages
n with a user-created web-site and
the
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