2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops

Investigating User Browsing Behavior
Ganesan Velayathan
The Graduate University for Advanced Studies,
National Institute of Informatics
2-1-2, Hitotsubashi,Chiyoda,Tokyo,JAPANganesan@nii.ac.jp
Abstract

on user browsing behavior. User browsing behavior isdefined here as the habitual actions performed by users
when browsing or searching, such as clicking links, bookmarking, printing or selecting text, and so on. If we candetect and learn these patterns of user behavior, we can use
these tendencies to perform automatic page evaluation without placing any burden on the user. We are also hypothesizing that user browsing strategies and habits do not change
greatly over time. Furthermore, we believe that browsing
behavior is unlikely to change even if a user is browsing ina different language environment, which means a languageindependent method of evaluating Web pages can be constructed. By using this method, we believe that a highly
accurate automatic self-constructed user profile can be created. This study is similar to the work done by Fox et al [5];however, the scope of their work was restricted to searchengine result pages and subsequent clickthroughs. The approach of building a customized browser is similar to Claypool’s Curious Browser [3]; however, we focus on keeping
the browser as similar as possible to Internet Explorer.

This paper describes our efforts to investigate factors
in user browsing behavior to automatically evaluate Web
pages that the user shows interest in. We developed a client
site logging tool to monitor and log the user’s browsing behavior. We performed user experiment using ten participants to collect the browsing behavior, and evaluated the
behaviors by performing classification learning using C4.5.
We generated common user browsing behavior rules andevaluated these common rules against the individual participant data. This paper reports those findings.

1. Introduction
In recent years, systems enabling Web personalization
for individuals have gained particular attention from commercial companies such as My Yahoo 1 , and Google Personalized Search 2 and researchers. Web personalization
can be described as actions “that make the Web experienceof a user personalized to the user’s taste” [4]. Most previous research and tools for Web personalization have relied
upon overt methods of asking users for their answers in order to construct user profiles. Building a user profile that
adapts to a user’s daily interests is a challenging task. Thisis because it is hard to predict which Web sites most interest the user without asking the user to interact explicitlywith the system. A user must interact with the system explicitly and tell the system each time a webpage is relevantto the task or not. This is laborious; it requires time andinclination, and users often forget to notify the system. A
less intrusive approach to the construction of user profiles isrequired.
With this in mind, this paper proposes a new method of
automatically discovering and judging user interest based

Starting from these considerations, we undertook construction of the GINIS Framework[13]. We used the GINIS
Framework to conduct experiments with ten participants.
This is a follow up work to our previous work which can befound in [13, 14, 15]. This paper does not present in detail
all the ideas behind this research. For better understandingof this work, ideas on building the client side logging mechanism can be found in [13], and the statistical evaluations
and the rules generated can be found in [15].
This paper focuses on evaluation of the common rules
generated using C4.5. We evaluated in detail the rules generated from the collected data and tested the rules with test
data from each individual user. We generated common rules
using all ten participant’s data, and used each participant’s
individual data to test the fitness of the common rules. Furthermore, we generated common rules for five participants(participants A-E) and tested the generated rules against theother five participants (participants F-J) , and vice versa.

1 http://my.yahoo.com/

2 http://labs.google.com/

0-7695-3028-1/07 $25.00 © 2007 IEEE
DOI 10.1109/WI-IATW.2007.122

Seiji Yamada
National Institute of Informatics
2-1-2, Hitotsubashi,Chiyoda,Tokyo,JAPANseiji@nii.ac.jp

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2 Current Practice and Research

3 Methodology

2.1

When browsing or searching the Web, we interact withthe browser’s user interface. For example, we print, save,bookmark, move the mouse, click a link, and so on. We
use the term navigation action here to describe the individual “components” of user behavior in performing actions using the browser directly. We use the term user behavior to describe the result achieved by performing thesenavigation actions. For example, the navigation actions“Hit Backspace Key,” “Click Back Arrow on Menu,” or“Use Back Button on Mouse” all constitute the same “MoveBack” user behavior.
A preliminary study was first made, monitoring one
user’s browsing behavior using video and screen shot captures. After carefully studying these screen shots and taking
into consideration the technical programming restrictions,
we chose around 40 specific user behaviors and 70 navigation actions and built a monitor for the browser. The logging attributes were designed based on the work presented
by Catledge and Pitkow [1], with some additions.
During the experimental stage, we gathered this information at the navigation action level, after which we preprocessed the log and construed user behavior. So, for example, if a user performed “Click Somewhere on Browser”
and “Drag Mouse while Holding Left Mouse Button” navigation actions, this would be collectively recorded as “ Text
Highlight.”
The experiment for collecting user behavior was divided
into two separate parts. The first part was the learning part,
during which the users were prompted to answer every timea “Next Page” navigation behavior was undertaken (by using any navigation action to move from a page to a newpage). On this questionnaire, users could choose “like,”“dislike,” or “unknown” in regard to the page indicated. The
default answer was set to “unknown,” as users tend to click
without really considering the question whenever they arebusy with deadlines etc.
The learning engine refined the logs from the raw log
database and stored it in the Behavioral Database (BDB).
Classification learning (building a decision tree using C4.5)
was performed based on the information from the BDB,
and user interest rules were created and stored in the Rule
Database (Rule DB). During the testing stage, the prediction engine compared the user’s new behavior with the behavioral patterns stored in the Rule DB, and the user’s implicit interest in a particular page was predicted based on
this comparison. Figure 1 show an overview of the steps ofthe system built for this research.
The GINIS Framework consists of four main modules:
a client interface to detect and log user behavior (thebrowser), a database to store the user log information (theraw logger), a learning engine, and a prediction engine.

Web Navigation

In the field of Web navigation, many previous studies
were based on data found in server logs for analyzing various aspects of user navigation. However, data available
in server logs only shows user behavior for a single site,
and it is difficult to collect and aggregate all the logs to discover common rules of user behavior. Shahabi and Chen
[11] have pointed out that Web server logs might be inaccurate because Web usage data from the server side are notreliable.
Little research has been done relating to client side web
navigation. One of the latest studies is the work presented
by Weinreich et al [16]. Weinreich focused on three aspectsof Web navigation: changes in the distribution of navigation
actions, speed of navigation, and within-page navigation.

2.2

Personalization

Personalization is the process of presenting the right information to the right user at the right moment [12]. In
most cases, personalization begins with building a user profile based on user interest. Interest data can be collected
either explicitly by asking for feedback from the user regarding preferences or implicitly by observing user behavior, such as time spent reading a Web page. Research suchas Chaffee’s[2] proposes building user preferences automatically by studying user browsing history.

2.3

Implicit Feedback

Implicit feedback techniques unobtrusively obtain information about users by observing their natural interactions
with the system [7]. The user behaviors most extensively
investigated as sources of implicit feedback include reading
time, as well as saving, printing and selection behaviors.
The primary advantage to using implicit techniques is thatsuch techniques remove the user burden from providing explicit feedback [6].
Morita and Shinoda [8] investigated reading time as afeedback factor, Seo and Zhang [10] considered user browsing patterns, and Kelly and Belkin [7] investigated the useof reading time and scrolling as an indication of relevance.
Fox et al. [5] found that dwell time, position, scroll count,and exit type are predictive actions of relevance judgments
for individual Web pages and that dwell time, number ofresults listings, and exit type are more predictive of overallsession satisfaction.
However, there are many other user browsing behaviors
to consider. This point remains in question, continuing tomotivate research towards its resolution.

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C4.5 CL
Learn andStore

User Behavior

Browser

New Navigation
Action( during testing)first method is by generating common rules using data fromall ten participants and testing the fitness of generated ruleswith each individual participant’s data.
However, since evaluation of the test data was performed
with the basic data used to create the rules, other methods ofevaluation are required. We furthered the evaluation of ourexperiment by building new rules using five participants’
data and evaluating the rules’ fitness with the other five participants’ data. This evaluation process was divided into
two parts. The first part was where the rules using the C4.5
classifier system was generated using participants A-E andtested against participants F-J. The second part was where
the rules were generated using the data of participants F-J
and tested against participants A-E.
Figure 2 indicates the results of this fitness test. The
bar chart indicates the total data used for each participant(the collected data from each participant which was used fortesting the fitness of the rules). The non-dotted line (color:red) indicates the error value of the fitness test (in %) ofeach data. The dotted line (color: green) indicates the fitness of A-E data tested against the rules generated using F-J
data, and also the rules generated using A-E data and tested
against F-J data. These were combined together into one
graph due to lack of space.

User Behavior &Interest Information

Data Cleanup

Logger

Us
er

Be
ha
v

Learneri or

ear General /mp
Pre -Build/Co
New Rules

Return Prediction of Interest

Predictor

Figure 1. Overview of GINIS Frameworks

4 Experiments and Results
4.1

Overview

We conducted systematic experiments using the GINIS
Framework with ten participants (6 male and 4 female) togather data in order to discover new rules linking user behavior and interest. C4.5[9] was used as the classification
algorithm. Verification was performed using the 10-foldvalidation method. The total number of behaviors loggedfrom the users was 64,312. There was an average 5.2 user
behaviors per page, with the total visited pages during thisexperiments being 12,368 pages.
After gathering all ten participants’ behavior logs, andremoving the instances of inconsistency, C4.5 classification
learning was performed. The C4.5 error rate was set at 25%.
Here, a “case” means a set of user behaviors or navigation
action data and the evaluation of the Web page when such
behaviors take place. The term “inconsistency” means thatthe cases of training data have the same type of behavior
occurrence but different kind of user evaluation.

4.2

4.3

Discussion

As with the first evaluation part, testing of the learningpart was performed using the same data. T resulted in highrule fitness. An average of 11.10% rule fitness error was
obtained. As for the second part, further evaluation of newrules generated, this resulted in average overall fitness errorof 18.02%.

Classification Learning and Rules

Test Data

Error All-Each500

As a result, 2249 cases were used as training data, andwithin these 1885 were judged as “of interest” and 364 were
judged as “not of interest” by the user. Of these evaluations, 2005 pages (89.15%) were correctly classified, and244 pages (10.85%) were incorrectly classified.
Twenty-six rules were output in total by C4.5. Five of
these were found to be rules governing “of interest”, and
21 of these were rules governing “not of interest.” The default class was “of interest”. Details of generated rules are
not shown due to lack of space. Statistical evaluation of
the generated rules can be found in our previous work [15].
This workshop paper focuses on evaluating the fitness of thegenerated rules.
To further the evaluation of the generated rules, we performed fitness evaluation using two different methods. The

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Instance

Error Half-Half

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218
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150
95

100
0
A

B

C

D

E

F

G

H

I

45.00%
40.00%
35.00%
30.00%
25 00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%

Error %

Navigation
Action & Interest
Information(during learning)

J

Par!cipant

Figure 2. Testing Common Rule’s Fitness
against Participant’s Data197

5 Present Limitations and Future Work

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scroll The situation and the nature of occurrence ofscrolling behavior were not considered in detail.
mouse Mouse locus data were not used to constitute behavior.
order The order of navigation/behavior was not considered.
session Actions and behaviors were recorded over a single
page, not over a sequence of navigation.
comparison No comparisons were made with other classification algorithms.
Considering these present restrictions, we are working
towards building more user behavior from the available collected experimental data. In particular, mouse locus dataand scroll action cover almost 50% of all collected data,
and further analyzing these behaviors will result in better
user behavior creation, thus further improving classification
learning results and enabling better rule creation.

6 Conclusions
We have presented here a method to automatically detectand log user behavior at the client-side by creating a clientside browser. We used this framework to conduct an open
environment field study. Based on the generated common
rules from the users we conducted rule fitness evaluation.
We successfully confirmed that all the rules generated by
C4.5 classification learning algorithm have 60% or higher
fitness, where the error for all the tests was lower than 40%.
As for the average rule fitness error, in total both of our evaluation methods indicates that the fitness error is less than
20%. From this, we can conclude that the rules generatedusing C4.5 have high fitness.

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