Web server logs have abundant information about the nature of users accessing it. The analysis of the users current interestbased on the navigational behavior may help the organizationsto guide the users in their browsing activity and obtain relevantinformation in a shorter span of time [1].Web usage mining is used to discover interesting user navigationpatterns and can be applied to many real-world problems, such as improving Web sites/pages, makingadditional topic or product recommendations, user/ customer behavior studies, etc [23].Web usage mining, in conjunction with standard approaches to personalization helps to address some of the shortcomings of these techniques, including reliance on subjective lack of scalability, poor performance, user ratings and sparse data[2,3,4,5,6]. But, it is not sufficient to discover patterns from usage data for performing the personalization tasks. It is necessary to derive a good quality of aggregate usage profiles which indeed will help to devise efficient recommendation for web personalization [11, 12, 13].Also the unsupervised and competitive learning algorithms has help to efficiently cluster user based access patterns by mining web logs [19, 20, 24]. This paper presents and experimentally evaluates a technique for finely tuninguser clusters based on similar web access patterns on their usage profiles by approximating through least square approach. Each cluster is having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users.Experimental results indicate thatusing the generated aggregate usage profiles with approximating clusters through least square approach effectively personalize at early stages of user visits to a site without deeper knowledge about them.

1. ACEEE Int. J. on Network Security , Vol. 03, No. 01, Jan 2012
A Least Square Approach ToAnlayze Usage Data For
Effective Web Personalization
S.S.Patil
Rajarambapu Institute of TechnologyRajaramnagar/CSE,Sangli, India
sachin.patil@ritindia.edu
Abstract—Web server logs have abundant information about information of web pages link, relevant data, products
the nature of users accessing it. The analysis of the users etc.Traditionally, collaborative filtering technique was
current interestbased on the navigational behavior may help employed to do this task. It generally produces
the organizationsto guide the users in their browsing activity recommendations on objects yet not rated by user, by
and obtain relevantinformation in a shorter span of time matching the ratings of current user for objects with those of
[1].Web usage mining is used to discover interesting user similar users. To increase the user click rate and service quality
navigationpatterns and can be applied to many real-world of Internet on a specific website, Web developer or designer
problems, such as improving Web sites/pages, needs to know what the user really wants to do & its interest
makingadditional topic or product recommendations, user/ to customize web pages to the user by learning its
customer behavior studies, etc [23].Web usage mining, in navigational pattern. Various approaches are defined to unreal
conjunction with standard approaches to personalization the applicative techniques to gethigher and corrective
helps to address some of the shortcomings of these recommendations for user surf.
techniques, including reliance on subjective lack of
scalability, poor performance, user ratings and sparse II. OVERVIEW OF RELATED WORK
data[2,3,4,5,6]. But, it is not sufficient to discover patterns
Various approaches have been devised for recommender
from usage data for performing the personalization tasks.
systems[2, 3, 4, 5]. The explicitfeedback from the user or rating
It is necessary to derive a good quality of aggregate usage
on items help to match interest with online clustering ofusers
profiles which indeed will help to devise efficient
with “similar interest” to provide recommendations. But
recommendation for web personalization [11, 12, 13].Also
practically itleads toward limitations of scalability and
the unsupervised and competitive learning algorithms has
performance[6] due to the lack of sufficient user information.
help to efficiently cluster user based access patterns by
Otherapproaches relating usage mining are implied to discover
mining web logs [19, 20, 24].
patterns or usage profiles from implicitfeedback such as page
This paper presents and experimentally evaluates a
visits of users.The offline pattern discoveryusing numerous
technique for finely tuninguser clusters based on similar
data mining techniques are used toprovide dynamic
web access patterns on their usage profiles by approximating
recommendations based on the user ’s
through least square approach. Each cluster is having users
shortterminterest.”WebPersonalizer” a usagebasedWeb
with similar browsing patterns. These clusters are useful
Personalization system using Web mining techniques to
in web personalization so that it communicates better with
provide dynamicrecommendations was proposed in [6]. In
its users.Experimental results indicate thatusing the
[7], a novelapproach using LCS algorithmimproves the quality
generated aggregate usage profiles with approximating
of the recommendations system for predictions by classifying
clusters through least square approach effectively
user navigation patterns. K-means clustering followed
personalize at early stages of user visits to a site without
byclassification for recommender systems [8] is used to
deeper knowledge about them.
predict the futurenavigations and has improved the accuracy
of predictions.Recent developmentsfor online personalization
IndexTerms—Aggregate Usage Profile,Least
through usagemining have been proposed. In [2],
SquareApproach, Web Personalization, Recommender
experimental evaluation of two different techniques suchas
Systems,Expectation Maximization.
PACT and ARHP based on the clustering of user transactions/
pageviews respectively for the discovery ofusage profiles
I. INTRODUCTION
was proposed.In [9], Poisson parameters to determine the
Tremendous growth of unstructured information available recommendation scores helped tofocus on the discovery of
on internet & e-commerce sites makes it very difficult to access user’s interest in a sessionusing clustering approach. They
relevant information quickly & efficiently.Web data research are used to recommend pages to the user. This novel approach
has encountered a lot of challenges such as scalability, in [9] involving integrated clustering, association rules and
multimedia and temporal issues etc.Web user drowns to huge Markovmodels improved webpage prediction accuracy.
information facing the problem of overloaded information.Web Various clustering algorithms had helped to group the user
personalization is the process based on users past behavior sessions as like K-means, Fuzzy C-means and Subtractive
for providing users with relevant content including massive Clustering [16, 17, 23, 24]. The clusters formed as a result of
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2. ACEEE Int. J. on Network Security , Vol. 03, No. 01, Jan 2012
applying these algorithms are aggregated to form web navigational patterns/interest from the user session file.
profiles. The recommendation engine uses these profiles, to Clustering techniqueis employed to determine the session
generate pages for recommendation. In [21, 22], Formal clusters using model-based Expectation Maximization as in
Concept Analysis approach is used todiscover user access [1]. The profile interest islearnt by determining an aggregate
patterns represented asassociation rules from web logs which usage profile using theformula:
can then be used forpersonalization and
recommendation.However, the existingsystem does not
satisfy users particularly in large web sites in terms of the wherein represents the weight of the page in sessions º c
quality of recommendations.This paper proposes
and nc represents the number of sessions in cluster c.Table 1
toclassifyusernavigation patterns through web usage mining
shows the aggregate usage profiles for 6 clusters under 13
system andeffectively provideonline recommendation. The
distinct categories of pageviews URLs.(explained in IV)
tested resultson ritindia.edu dataset indicate to improve the
quality of thesystem for recommendations. TABLE I. AGGREGATE USAGE PROFILES
III. METHODOLOGY
Personalization using usage mining consists of four basic
stages. The process embeds of:
1) Data Preprocessing
2) Pattern discovery
3) Pattern recognition
4) Recommendation process
Classifying and matching an online userbased on his
browsing interestsforrecommendations of unvisited pages
has been employed in this paper using usage mining to II.PATTERN R ECOGNITION :
determine the interest of “similar” users. The individual profile effectiveness is measured using
Therecommendation[10] consists of offlinecomponent and weighted average visit percentage. It is to represent the
online component. The offline componentinvolves Data significance of user’s interest in the cluster.If the aggregate
Preprocessing, Pattern Discovery and PatternAnalysis. The usage profilesconsist of m clusters and k pages, then the
outcome of the offline component is thederivation of significance in thecluster can be determined as follows:
aggregate usage profiles using web usage miningtechniques.
The online component is responsible for matchingthe current whereI(j) is the index of the maximum value in each page
user’s profile to the aggregate usage profiles to generate the andMI(j) represents the maximum value. Also the weight of
necessary recommendations. page is considered as per pageview j.This maximization
I. DATA PREPROCESSING :
function is used to recommend pages to users belonging to
Preprocessing is the primary task of personalization a profile/cluster.
involving cleansing of data, session and user identification, IV. RECOMMENDATION PROCESS :
page view and transaction identification [10, 14, 15]. Let there The recommendation engineis the online component of a
be set of pages P= {p1,p2,p3,p4,….,pn} and set of n sessions, usage-based personalization system.The goal of
S= { s1,s2,s3,…..,sn} where each siº S is a subset of P. A file personalization based on anonymous Webusage data is to
consisting of session profile of user requests for pages is compute a recommendation set for the current (active) user
maintained.In [1], for a particular session, a session-pageview session, consisting of the objects (links, ads, text, products,
matrix is maintainedconsisting of a sequence of page requests etc.) that most closely match the current user profile.
in that session. A row representing a session and every Recommendation set can represent a long/short term view of
column represents a frequency of occurrence of pageview user’s navigational history based on the capability to track
visit in a session. Then the weight of the pageview is users across visits.The test data of user sessions are taken
determined by evaluating the importance of a page interms as sequence of pages in time order. An active window size is
of the ratio of the frequency of visits to the page withrespect fixed and those many pages are taken from the user session
to the overall page visits in a session and is represented by a as active session. Then the similarity between the active
weighted session-pageview matrix. Each session si is modeled session and all the cluster profiles is calculated using a vector
as a vector over the n-dimensionalspace of pageviews. similarity measure and the most similar profile selected for
II. PATTERN DISCOVERY :
recommendations [1, 2, 25]. If an active session is represented
The primary task of pattern discovery is to find out as siandcluster as ck, then their similarity can be measured as
thehidden patterns using various mining techniques such follows:
asclustering, association rule, classification etc., which helps
to uncover the user behavior with respect to the site. It is an
offline task which helps to determine sessions with similar
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3. ACEEE Int. J. on Network Security , Vol. 03, No. 01, Jan 2012
wherewi, j, represents weight of page i in active session jand IV. EXPERIMENTAL EVALUATION
wi, k, represents weight of page i in cluster k. The method of
least squares assumes that the best-fit similarity of a given This section provides a detailed experimental evaluation of
type is the matching score that has the minimal sum of the the profile generation techniques.The privately available data
deviations squared (least square error) from a given set of set at the University of Shivaji, containing web log files of
data.Suppose that the data points are sim(s1,c1),sim(s2,c2),...., ritindia.edu web site have been used for this research. It
sim(sj,ck) where sjis the independent variable and ckis the includes the page visits of users who visited the “ritindia.edu”
dependent variable.The fitting score (sj) has the deviation web site in period of June 2006 to April 2011. The initial log
(error) d from each data point,i.e., d1=c1-(s1),d2=c2-5ØSÜ(s2), file produced a total of 16,233 transactions and the total number
...,dn=cn-5ØSÜ(sn). According to the method of least squares, of URLs representing pageviews was 27. By using Support
the best matching score has the property that: filtering for long transactions,pageviews appearing in less
than 0.5% or more than 85% of transactions were eliminated.
Also, short transactions with at least 5 references were
eliminated. The visits are recorded at the level of URL category
This least square error approach helps to fine tune the and in time order, which includes visits to major 13distinct
scores such as to approximate user clusters based on similar categories ofpageviews URLs. Each sequence in the dataset
web access patterns on their usage profiles. Thena corresponds to a user’s request for a page.
recommendation score for each page view p in the selected The 13 categories are:
cluster/profile is calculated. If C is the most similar cluster/ Id Category Id Category
profile to the active session S, then a recommendation score 1 aboutkes 8 placement
for each page view p in C is as: 2 aboutrit 9 lifeatrit
3 courses 10 contact
4 departments 11 mission-vision
Profiles having a similarity greater than a threshold value
5 facilities 12 achievements
µc are selected as matching clusters in the decreasing order
6 faculty 13 academics-at-rit
of their scores. The weight of pageview p in C is computed
7 admission
twice i.e. directly and indirectly but to compensate the impact,
A clustering model is estimated using approximately 15,000
square root in the above function is taken and results are
samples within the dataset. They are further classified into
normalized to value between 0 and 1. If the pageview p is in
training and testing sets. Dataset is split into 70% training
the current active session, then its recommendation value is
and 30% testing sets such that the model is designed using
set to zero. These matching clusters can be used
training set and then evaluated using test samples for
forrecommending pages instantaneously which have not
performance. Applying the clustering algorithm for
beenvisited by the user.
Expectation Minimization with 12 iterations resultsin 6major
The following figure shows the overall process of web
clusters. Each cluster represents several sessions of
personalization using web usage data.
navigational patterns representing “similar” interest in the
web pages or the usage profile and the aggregate usage
profile is determined using Equation(1). During the online
phase, the pages visited in a session are stored in a user
session file and after each page visit, the relative frequency
of pageviews in the active session is determined. An active
session with sliding window size ‘n’ (in our experiment, the
size is 5 as it represents the average number of pagevisits in
the dataset) consists of the current page visit and the most
recent n-1 pages visited. The window slides, as the user
browses through various pages. Now, using the cosine
similarity measure,the active session is matched with the
aggregate usage profiles and matching cluster(s)having value
greater than the threshold and are used for recommending
pages exceeding threshold that have not been visited by the
user.
For example, consider the following 3 sessions consisting of
page visits:
18
1 8 13
1 13 1 13 1 7 1
The sliding window consists of the pages 1 13 1 13 1 in the
Figure 1.THE OVERALL PROCESS OF PERSONALIZATION fifth page visit.
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4. ACEEE Int. J. on Network Security , Vol. 03, No. 01, Jan 2012
Table 2 and 3represents the page visits and frequency of Clusters greater than the threshold value, are chosen to be
visited pages in the sliding window. It states the weight of matching clusters as shown in Table4. This table depicts the
the pageviewby evaluating the importance of a page in terms comparative study of aggregate usage profiles and the
of the ratio of the frequency of visits to the page with respect maximization function to show recommendations. It has been
to the overall page visits in the active session. found that when the user visits page 1 (window size 1), the
TABLE II. PAGE VISITS IN THE SLIDING WINDOW
appropriate clusters, exceeding the threshold value are cluster
2 and 4. It is seen that pages 2,3,6,7,9 can be recommended
from cluster 2 and 4. Similarly when the user visits page 8
subsequent to pagevisit 1(window size 2), the appropriate
matching clusters are cluster 2, cluster 3 and cluster 4. As the
window size increases to the fixed size limit (n=5),
correspondingly, the matching clusters for the visited page(s)
in the active session and the recommendations are dynamic
in nature.Table 5 shows the recommended set of pages for all
3 demo sessions.
TABLE III. FREQUENCY OF PAGE VISITED/WEIGHTED PAGEVIEW TABLE V. RECOMMENDATION SET FOR SESSION1, SESSION2,
SESSION3
TABLE IV. MATCHING CLUSTERS
As compared to experimental study in [1], the
recommendation of pages for demo sessions is calculated on
Equation (3) for measuring similarities of sessions. Then the
pages are recommended in the session as per Equation(6) for
simple similarities of clusters which provides a less precision
for recommending the matching scores of clusters.As
compared to same, the least square approach analysis in our
study helps to find deviations & to refine further the
recommendations as per Equation (6) with similarity
measures.In [1], if the above 3 demo samples are measured,
then the recommendation set varies a lot recommending less
pages under given threshold.
CONCLUSION AND FUTURE WORK
The ability to collect detailed usage data at the level of
individual mouse click provides Web-basedcompanies with
a tremendous opportunity for personalizing the Web
experience of clients.The practicality of employing Web usage
mining techniques for personalization is directlyrelated to
the discovery of effective aggregate profiles that can
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5. ACEEE Int. J. on Network Security , Vol. 03, No. 01, Jan 2012
successfully capture relevant usernavigational patterns and [8] AlMurtadha Y, Sulaiman M, Mustapha N, Udzir N(2010).
can be used as part of usage-basedrecommendersystemto Mining Web Navigation Profiles for Recommendation
provide real-timepersonalization. System.Inform.Technol.J. 9:790-796.
In this work, the primary objective was to classify and match [9] Sule G,Ozsu M (2003).A User Interest Model for WebPage
Navigation. DMAK, Seoul. 1:46-57.
an online userbased on his browsing interests.Identification
[10] Mobasher B, Cooley R, Srivatsava J(2000).Automatic
of the current interests of the user based on theshort-term Personalization Based on Web Usage Mining. ACM. 43:142-151.
navigational patterns instead of explicit userinformation has [11] Cooley R, Mobasher B, Srivatsava J(1997). Web
proved to be one of the potential sources forrecommendation Mining:Information and Pattern Discovery on the World Wide Web.
of pages.Inparticular context ofanonymous usage data, these IEEE. 9: 558-567.
workunder least square approximation show promise in [12] Srivatsava J, Cooley R, Deshpande M, Tan P(2000). WebUsage
creatingeffective personalization solutions that can help retain Mining: Discovery and Applications of Usage Patterns from
and convert unidentified visitors based ontheir activities in WebData. ACM. 1: 12-23.
the early stages of their visits. Future work involves with [13] Agarwal R,Srikant R(1994). Fast Algorithms for
MiningAssociation Rules in Large Database. Procd.of Conf.on Very
various types of transactions derived from user sessions,
Large Data Bases, USA. 1:12-15.
such as to isolate specifictypes of “content” pages in the [14] Sumathi C, Padmaja R,Valli,Santhanam T(2010). J. of
recommendation process. Also theplan is to Comp.Sci. 1:785-793.
incorporateclient-side agents and use of optimization [15] Suresh R,Padmajavalli R(2006). Overview of DataPreprocessing
techniques to assess thequality of recommendations. in Data and Web Usage Mining. IEEE. 1: 193-198.
[16] Chiu S (1994). Fuzzy Model Identification Based on Cluster
REFERENCES Estimation. J. of Intelligent and Fuzzy Systems. 2(3).
[17] Bezdek J (1973). Fuzzy Mathematics in Pattern Classification,
[1] Sumathi CP, PadmajaValli R (2010).Automatic Recommendation Cornell Univ. PhD Thesis.
of Web Pages in WebUsage Mining. Internat.J. on Computer Science [18] Witten I, Frank E (2005). Data Mining: Practical Machine
and Engg.2(9). Learning Tools and Techniques. 2 Edt. : Kaufmann M, Francisco S.
[2] Mobasher B, Honghua D, Tao L, Nakagawa M(2002). Discovery [19] Hartigan T, Wong A (1979). A K-Means Clustering Algorithm.
and Evaluation of Aggregate Usage Profiles for Web Applied Statistics. 28:100-108.
Personalization.ACM. 6:61-82. [20] Ng K, Junjie M, Huang J, Zengyou Y (2007). On the Impact
[3] Eirinaki M, Vazirgiannis M (2003).Web mining for Web of Dissimilarities Measure in K-modes Clustering Algo. IEEE. 29(3).
personalization.ACM. 3(1). [21] Vasumathi D, Govardhan A (2005). Efficient Web Usage Mining
[4] Khalil F, JiuyongL,Hua W (2008). Integrating Recommendation Based On Formal Concept Analysis. IFIP. 163(2).
Models for Improved Web Page Prediction Accuracy. Australa.Conf. [22] Spiliopoulou M (2000). Web usage mining for Web Site
on Computer science. 74:91-100. Evaluation. ACM. 43(8): 127-134.
[5] Forsati R, MeybodiM,Ghari N (2009). Web Page [23] Ratanakumar J (2010). An Implementation of Web
Personalization based on Weighted Association Rules. Internat. Personalization Using Mining Techniques. J. of Theoretical and
Conf. on Electronic Computer Tech. 1: 130-135. Appl. I. T. 5(1):67-73.
[6] Mobasher B,Dai H,LuoandT,Nakagawa (2001).Improvingthe [24] Memon K, Dagli C (2003). Web Personalization using Neuro-
effectiveness of collaborative filtering on anonymous Web usagedata. Fuzzy Clustering. IEEE. 1: 525-529.
IJCAI, Seattle. [25] Mobasher B, Cooley R,Srivatsava J (1999).Creating Adaptive
[7] Mehrdad J, Norwati M, Ali M, Nasir B (2009). A Recommender Web Sites Through Usage-Based Clustering of URLs. Proc. KDEX
System for Online Personalizationin the WUM Applications. Procd. ’99. Workshop on Knowledge and Data Engineering Exchange.
of the World Congress onEngg.and Comp. Sci. 9(2).
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