Behavior-Based Web Page Evaluation
Ganesan Velayathan

Seiji Yamada

The Graduate University for Advanced Studies
National Institute of Informatics (NII)
2-1-2 Hitotsubashi
Chiyoda, 101-8430 Tokyo, Japanganesan@grad.nii.ac.jp

National Institute of Informatics (NII)
2-1-2 Hitotsubashi
Chiyoda, 101-8430 Tokyo, Japanseiji@nii.ac.jp

ABSTRACTThis paper describes our efforts to factor in a user’s browsing
behavior to automatically evaluate web pages that the user showsinterest in, based on user browsing behaviors while browsing. Toevaluate a webpage automatically, we have developed a clientside logging tool: the GINIS Framework. We do not focus just onclicking, scrolling, navigation, or duration of visit alone, but we
propose integrating these patterns of interaction to recognize andevaluate a user’s response to a given web page.

Categories and Subject Descriptors
H.1.2 [User/Machine Systems]: Human factors; H.3.3 [Information Storage and Retrieval]: Information Filtering.

General Terms
Human Factors, Design, Theory.

Keywords
Information extraction, web usage mining, automatic profiling,
web browsing behavior, web-human interaction.

1. INTRODUCTION
The need to automatically build a user profile to assist in
everyday user browsing tasks has been steadily increasing, due toinformation overload on the internet. Building a user profile thatadapts to a user’s daily interests is a challenging task. This isbecause it is hard to predict which web site most interests the user
without asking the user to interact explicitly with the system.
The reason conventional profiles are not adaptive is that the user
evaluation process has been manual – that is, a user must
explicitly tell the system each time a webpage is relevant to thetask or not. This is laborious, and users often lack the time orinclination to carry this out. A less intrusive approach to theconstruction of user profiles is required. How might this best beachieved?
When users use a web browser to browse the Internet, they
(whether intentionally or not) interact with the browser’s interface,by clicking, scrolling, book marking, printing or selecting text, etc.
If we can detect and learn a user’s patterns of behavior, we canuse these individual browsing habits to perform automatic pageevaluation, and hence preferential page selection and prediction.
We aim to build a behavioral interaction database which matches
a user’s actions (such as scrolling, clicking, etc) with preCopyright is held by the author/owner(s).
WWW 2006, May 23–26, 2006, Edinburgh, Scotland.
ACM 1-59593-323-9/06/0005.

established behavioral patterns mined from that user, thus giving
some idea of the user’s interest (or lack of interest) in a particular
webpage. Using this method, we believe we can achieve adetailed, granular, self-constructing user profile. We believe that
careful study of these patterns at the client-side will lead us to abetter understanding of which pages most interest the user [1].
However, logging the user behavior at the client-side is achallenging task, for which we needed to build a high-level user
client and logging tools. In this paper, we introduce the GINIS
Framework, a framework developed to automatically log userbrowsing behavior.

2. RESEARCH OVERVIEW
2.1 Problem with Conventional Methods
Most of the profiling methods used at present consider any web
pages contained in browser history as “positive trainingexamples”, which means that pages not reached or viewed by theuser are regarded as “negative training examples”. Expanding on
this idea, [2]proposed measuring the time spent on a web page asa factor for evaluation, but this is not ideal; some users just open a
web page and then navigate to another page without closing thefirst page. This practice is becoming more common with the useof tab-browsers, although this can be ameliorated by enforcing alimit on maximum duration. Further, researchers like [3]introduce examining navigational history to evaluate interest, but
this is not always a good indication; the user might easily
navigate to a page but not be interested in its contents. At present,
it is difficult to discover whether a certain page is of interest ornot.

2.2 Overview
Developing client side logging tools would produce a better
logging environment. Logging at the client side produces richer
data, although it makes necessary the difficult task of building arich browser. However, compared to conventional web usage
mining methods, like proxy side collection or server sidecollection[3], client side logging gives a richer and more accurate
picture of the user’s behavior.

3. USER BEHAVIOR
3.1 Selecting and Gathering
User’s behavior during browsing can be classified into twogroups: direct (implicit) behaviors and indirect (explicit)
behaviors. Direct behaviors are behaviors that directly show thatthe user might be interested in the contents of the web page or not,e.g. printing a web page (behavior indicating interest) or closing abrowser window (behavior indicating non-interest). Indirect
behaviors are behaviors that, taken by themselves as single

behaviors, do not show whether the user likes or dislikes the webpage, but when studied in terms of the sequence occurrence of theaction, yield patterns that could predict the user’s interest or noninterest.

3.2 User Behavior Examples

decision tree, on the basis of this comparison, predicts which
pages interest the user and which do not.
Figure 1 shows the architecture of the GINIS Framework during
the learning stage, and figure 2 shows the GINIS Framework
testing stage.
Behavior patterns anduser interest information

Table 1. Direct user behaviors
IDBehavior

(DB1)
(DB2)
(DB3)
(DB4)

Print a page
Bookmark a page
Click a link
Input a new URLCopytext
toclipboard
Terminatethebrowser
Stop Navigation

(DB5)
(DB6)
(DB7)

Yes
Yes
Yes
Yes

Level
(high/medium/low)
HighHigh
MediumHigh

YesHigh

NoHighNoMediumInterest

Data Cleanup

Behavior
Raw Log DB

Behavioral DB

Interest
Information

Figure 1. GINIS Learning Stage.

In the case of direct behaviors with the browser, we might say thata single action often indicates either the user’s interest or noninterest. However, the level of interest could differ from user touser.
Table 2. In-direct user behaviors
ID
(IB1)

Behavior

Explanation
Navigate from P1 toN(P1)-N(P2)…N(Pn)
Pn(IB2)
Navigate to P1, scroll
down n-times, scroll
N(P1)-[S(Dn)-S(Up) -S(Dn)]up n-times, and scroll
down n-times.
(IB3)
Navigate to P1, click aN(P1)-C(L1)-N(P2)link on P1, jump to P2
N: Navigate, S: Scroll, Dn: Down, Up: Up, C: Click
In the case of indirect user behaviors, one single action might not
be enough to indicate interest in a particular page, but by studying
the session of behavior and the action occurrence pattern, wemight be able to extract indirect behaviors that can be said toindicate interest or non-interest in a page.

4. GINIS FRAMEWORKIn this section, we discuss the various modules of the GINIS
Framework, which was developed specifically for web usage
mining. The GINIS Framework consists of 4 main modules: aclient interface to detect and log user behavior (Browser), a
database to store the user log information (Raw Logger), a
learning engine and a prediction engine.

4.1 Architecture
The web browser was developed based on MSIE6.0, and iscapable of logging user behavior. The raw database stores rawuser log data. The learning engine refines the logs from the raw
database and stores it in the User Behavioral Database (UBD).
Based on UBD, a decision tree is constructed [3] and stored at theserver. During the testing stage, the prediction engine generates
and compares the user’s new behavior decision tree with the UBD

Figure 2. GINIS Testing Stage.

5. CONCLUSION
We have presented here a method to automatically detect and log
user behavior at the client-side by creating a client-side browser.
Whether intentionally or not, users display certain patterns ofbehavior when they come across a page that they like or dislike.
By studying these patterns, user behavior during browsing could
be made to play a major role in establishing which pages a userfavors.

6. REFERENCES[1] L. Catledge and J. Pitkow. Characterizing browsing
behaviors on the World Wide Web. Computer Networks and
ISDN Systems, 27(6), 1995.

[2] M. Morita and Y. Shinoda. Information Filtering Based onUser Behavior Analysis and Best Match Text Retrieval. In
Proc. of the 17th Annual International ACM SIGIR
Conference on Research and Development in Information
Retrieval (SIGIR ’94), pages 272-281, 1994.

[3] R. Cooley, Pang-Ning Tan, and Jaideep Srivastava.
Discovery of interesting usage patterns from web data.
Technical Report TR 99-022, University of Minnesota, 1999.

[4] T. Lim, W. Loh, and Y. Shih. A comparison of prediction
accuracy, complexity, and training time of thirty-three oldand new classification algorithms, Machine Learning
Journal,40:203-228, 2000.