Can We Find Common Rules of Browsing Behavior?
Ganesan Velayathan

Seiji Yamada

The Graduate University for Advanced Studies

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

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

ABSTRACTThis paper describes our efforts to investigate factors in user’s
browsing behavior to automatically evaluate web pages that theuser shows interest in. To evaluate web pages automatically, wedeveloped a client-side logging/analyzing tool: the GINIS
Framework. We do not focus on just clicking, scrolling,navigation, or duration of visit alone, but we propose integratingthese patterns of interaction to recognize and evaluate user
response to a given web page. Unlike most previous web studies
that have analyzed access seen at proxies or server, this work
focuses primarily on client-side user behavior using a customized
web browser. First, GINIS unobtrusively gathers logs of user
behavior through the user’s natural interaction with the webbrowser. Then it analyses the logs and extracts effective rules toevaluate web pages using C4.5 machine learning system.
Eventually, GINIS becomes able to automatically evaluate web
pages using these learned rules, after which the evaluation can beutilized for a variety of user profiling applications. We
successfully confirmed, for example, that time spent on a web
page is not the most important factor in predicting interest frombehavior, which conflict with the finding of most previous studies.

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
Web-human interaction, browser interface, navigation, web usage
mining, user modeling.

1. INTRODUCTION
Over the last decade, the Web has grown massively in size,
popularity, business application, and number of users.
Accompanying this remarkable growth has been an increase in
spamming and ill-intentioned websites designed to obtain income
illicitly. Many proposals have been made for tools and systems toassist with everyday user browsing and searching. In recent years,
systems enabling web personalization for individuals have gained
particular attention. This is the field of web personalization.
Most previous research working toward web personalization has
relied upon overt methods of asking users for their answers inorder to construct user profiles. Building a user profile that adapts
Copyright is held by the author/owner(s).
WWW 2007, May 8--12, 2007, Banff, Canada.

to a user’s daily interests is a challenging task. This is because itis hard to predict which web sites most interest 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 interact
with the system explicitly and tell the system each time a
webpage is relevant to the task or not. This is laborious; it
requires time and inclination, and users often forget to notify thesystem. A less intrusive approach to the construction of user
profiles is required.
With this in mind, this study proposes a new method of
automatically discovering and judging user interest based on userbrowsing behavior. User behavior is defined here as the habitual
actions performed by users when browsing and searching, such as
clicking links, bookmarking, printing or selecting text, and so on.
When users use a web browser to browse the Internet, they
(whether intentionally or not) interact with the browser’s interface.
If we can detect and learn these user’s patterns of behavior, we
can use these tendencies in individual browsing habits to perform
automatic page evaluation. The behavior performed whenever auser browses and the patterns learned from previous users
constitute a learned behavioral database. We believe that throughcomparison with this database, new websites visited can be
automatically evaluated without placing any burden on the user.
We are also hypothesizing that user browsing strategies and habits
do not change greatly. By using this method, this study verifies
that a highly accurate automatic self-constructed user profile canbe created. Furthermore, we believe that browsing behavior isunlikely to change even if a user is browsing in a different
language environment, which means a language-independent
method of evaluating web pages can be constructed.
This study focuses on highly accurate logging at the client-side.
Logging at the client-side enables a more complete grasp of user
behavior than proxy sites or server sites. However, because
logging user behavior at the client-side is a challenging task forwhich we needed to build high-level client software, we
encountered various difficulties. Also, two issues emerged withregard to acquiring correct user browsing behavior. The first wasthat overt collection of data for experimental purposes was notdesirable; in short, if users were conscious of an experimental
environment, this would interfere with their usual browsing habits.
The other issue is that it was necessary for the experimental
instrument for data collection to be identical to the browser
regularly used by the users. Starting from these considerations, weundertook construction of the GINIS Framework, which expands
the Internet Explorer Component available on .NET Frameworkfrom Microsoft. We used the GINIS Framework to conduct
experiments with test participants. This paper presents thosefindings.

The paper is organized into 8 sections. Section 2 describes someproblematic areas from previous and related research. Section 3
explains the details of web page evaluation based on user
behavior as well as the GINIS Framework we have developed forclient-side logging of user behavior. Section 4 describes theexperiments conducted using the GINIS Framework and in
Section 5, we present the result of these experiments. Section 6
discusses some observations, Section 7 on future direction of thisresearch and Section 8 concludes the paper.

2. RELATED WORK
2.1 Web Navigation
Interest in web navigation has been an issue for commercial
companies and researchers all around the world since thebeginning of the WWW. One of the latest studies is the work
presented by Weinreich et al., [5] observing 25 users for a mean
usage duration of 105 days. Prior to this was the work presented
by Catledge and Pitkow [1], (the first long-term client-side usage
study made), observing 107 users over a duration of 21 days.
However, most of these past works tended to focus on tracking
user behavior itself rather than looking for connections between
user behavior and user interest.
Past works on user interest and web usage behavior tended tofocus more on navigation history, explicitly clicked links and thetime spent on a web site. Morita et al., [2] found that there is astrong tendency for users to spend a greater length of time reading
articles of interest to that user, and to spend less time on pages ofless interest. However, Weinreich [5] found that users spend lessthan 12 seconds on nearly 50% of the web pages shown to them.
This means that users make nearly 50% of their decisions tonavigate to the next page before thoroughly reading the contents.

2.2 Personalized Web Search
Personalization is the process of presenting the right information
to the right user at the right moment [6]. In most cases,
personalization begins with building a user profile based on userinterest. Interest data can be collected either explicitly by asking
for feedback from the user regarding preferences or implicitly byobserving user behavior, such as time spent reading a web page.
Most research on personalizing web searches tend to focus onbuilding user profiles based on requiring users either to fill out
written or online questionnaires, or to specify web page categories
of interest [14]. These conventional pre-defined user profiles must
be manually constructed when a tool is initially used, and can be
refined only through manual adjustment. However, since userinterests change with time and task-orientation, every time auser’s interest changes, the user has to manually re-create the user
profile. Users may provide inconsistent or incorrect information,and the profile built up is static, whereas user interest may changeover time, meaning that the construction of such a profile mightplace a burden on users that they do not wish to accept [6].
Research such as Chaffee’s [8] proposes building user preferences
automatically by studying user browsing history. Studies such as[6] focus on automatically learning from user search history.
Many other studies, such as [9][10] have focused on building
accurate user profiles based on implicit user behavior.
To achieve effective personalization, profiles should distinguishbetween long-term and short-term interests, and should include amodel of the user’s context, i.e., the task in which the user iscurrently engaged and the environment in which they are situated[6].

2.3 Implicit Feedback
Implicit feedback techniques unobtrusively obtain information
about users by observing their natural interactions with the system.
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 that such techniques remove from the user theburden of providing feedback [11] .
Most previous implicit feedback research experiments have been
conducted in a controlled laboratory and have tended to focus ontime spent on a web site as one of the most important factors[12][13]. However, there are many other factors to consider. This
point remains in question, continuing to motivate research
towards its resolution.

3. BEHAVIOR-BASED EVALUATION
When browsing or searching the web, we tend (intentionally ornot) to interact with the browser’s interface. For example, we
print, save, bookmark, move the mouse, click a link, and so on.
This study will refer collectively to these interactions as
navigation actions. Behavior such as printing, bookmarking,saving page, etc. is termed direct behavior. Kim [7] found that
95% of users are interested in the contents when they performthese kinds of direct actions. However, [5] found that only 2% of
all user activity constitutes this direct behavior. From this we canconclude that users will not necessarily create bookmarks or print
even on those pages in which they have indicated interest. These
things are known from experience. Profiles constructed by taking
direct behavior as the only target can be expected to produce
results to a certain extent, but as long as users are not actively
undertaking this direct behavior, discovering any generalized
rules is extremely difficult.
In order to build user profiles automatically, we believe that
implicit user behavior (indirect user behavior) will play a major
role. Careful study of indirect user behavior could help us tobetter understand user interest, and thus to further enhance
techniques for web personalization.

3.1 Behavior Attributes
After carefully studying the daily navigation actions of users, wechose around 40 specific user behaviors and built a monitor forthe browser. The details of behaviors monitored are described
below. Some less important behaviors, such as Windows Update
and View Help, among others, were omitted during the
programming stage, as we considered these actions lesssignificant for the study of user behavior.
We use the term navigation action here to describe the individual“components” of user behavior in performing actions using thebrowser directly. We use the term user behavior to describe theresult achieved by performing these navigation actions. For
example, the navigation actions “Hit Backspace Key,” “Click
Back Arrow on Menu,” or “Use Back Button on Mouse” allconstitute the same “Move Back” user behavior.
Some user behaviors were gathered at the navigation action level
rather than at the user behavior level; this is because we preferred
to keep the granularity of the log as high as possible. During theexperimental stage, we gathered this information at the navigation
action level, after which we pre-processed the log and construed
user behavior. So, for example, if a user performed “Click
Location” and “Drag Mouse while Holding Left Mouse Button”

navigation actions, this would be collectively recorded as“Highlight Text.”
Over 70 navigation actions and around 40 user behaviors were
logged during this experiment. The logging attributes were
designed based on the work presented by Catledge and Pitkow[1],with some additions. Outdated attributes were left out during thesystem design stage. Table 1 shows a short sample of user
behaviors added.
Table 1: Sample of User Behaviors Added
User Behavior

Description of Behavior / Action

Bookmark to Desktop

Bookmark to Desktop

Change Encoding

Change Encoding

Change Font Size

Change Font Size

Close Current Tab

Close Current Tab

Open Favorite Bar

Open Favorite Bar

Open Link to New Tab

Open New Page in Tab Window

Open New Tab

Open New Tab

Open Search Bar

Open Search Bar

Overwrite To File

Overwrite the Existing FileShow Full Screen

Show in Full Screen

Text Copy

Copy the Text

Text Copy All

Copy All the Text

Text CutCut the Text

Text Highlight

Highlight the Text

Text Paste

Paste the Text

Resize Browser

Resize Current Browser

Figure 1. An Overview of GINIS Framework
The GINIS Framework consists of 4 main modules: a clientinterface to detect and log user behavior (the browser), a databaseto store the user log information (the Logger), a learning engine(the Learner) and a prediction engine(the Predictor).

3.2 GINIS Framework Architecture
A framework called GINIS was developed during this research
for the purpose of logging user behavior at the client-side. The
GINIS Framework was built based on .NET Framework 2.0,
using Microsoft Internet Explorer Browser Component. It is anextendable framework intended for client-side logging.
The experiment for collecting user behavior was divided into 2
separate parts. The first part was the learning part, during which
the users were prompted to answer every time before navigating
to a new page whether they liked the page they just viewed. Thisfavorable information (along with behavior data) was collected
and stored in a database.
The learning engine refined the logs from the raw data databaseand stored it in the User Behavioral Database (UBD).
Classification learning (building a decision tree using C4.5) was
performed based on the information form the UBD. During thetesting stage, the prediction engine generated and compared theuser’s new behavior with the behavioral patterns stored in the
UBD, and the user’s implicit interest in a particular page waspredicted based on this comparison.
Figure 1 show an overview of the 2 steps of the system built forthis research.

Figure 2. GINIS Browser User Interface
The browser: Used to log user navigation actions, this mimics
almost all of Internet Explorer 6, with some extension of theright-click menu. A simple AJAX plug-in and form input plug-in
was added to enhance the browsing and logging features.
Naturally, it is a simple tab browser; because of the popularity of
tab browsers in the Japanese community. Since Internet Explorer
7 will be tab-browsable, we hope that the result of this study will
be useful in the future. The user interface menu of GINIS browser
is shown in Figure 2.
The logger: A real-time user navigation logger, this logs user
navigation behavior, including primitive navigation actions likemouse movements, scrolls, clicks, bookmarking, etc., as well as
more advanced combinations of navigation actions like Text
Highlight, Text Copy etc.
The learner: A behavioral database builder, this builds a decision
tree based on user interest information. The C4.5 classification
learning system, a landmark decision tree machine learning
method widely used in practice, was chosen as the classification
algorithm [3]. Survey [4] show that it provides good classification
accuracy, and it is the fastest among the compared main-memory
classification algorithms for machine learning and data mining.

Another reason C4.5 was chosen in preference to other algorithms
its ability to generate rules from the decision tree. C4.5 rules areshown as linguistic information (such as if-then type rules) whichpeople can easily comprehend and use.
The predictor: Based on the classification learning built at thelearning stage using the analyzer, the predictor module willautomatically classify and assign user behavior to “of interest”
and “not-of-interest” classes. Based on the user behavior
performed, the predictor module also has the ability to classify
web pages viewed by a user as “pages of interest” and “pages of
non-interest,” which can be used to build profiles for
personalization.

4. EXPERIMENTS
4.1 Overview
We conducted systematic experiments using the GINIS
Framework to gather data in order to discover new rules linking
user behavior and interest. Discovered from the data gatheredfrom the participants, the rules were summarized either as shareduser behavior rules and individual user behavior rules. C4.5 wasused as the classification algorithm. The C4.5 system can deal
with both numerical information and character string information
at the same time, and it can represent a decision tree with a formof if-then type rules, making it more effective than other tree-likeclassification methods. Verification was performed using the 10fold validation method. We designed the experiment to be
performed within an open environment in order to expose andclarify unintentional user behavior while browsing (one of themain objectives of the study). Each user installed the GINIS
browser on his or her desktop and used it in the conduct of his or
her daily activities. We did not control or restrict the user in any
way, preferring to keep the experiment as non-laborious as
possible.
When using the GINIS browser, a questionnaire dialog box pops
up every time the user undertakes a “Next Page” navigation
behavior (by using any navigation action to move from a page toa new page). 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 when they are very busy
with deadlines etc. The browser also allows the user to terminate
the popup, instead using the question button at the side of themenu bar.
The log file generated by the GINIS Framework contains data on
the date, time, Window ID, Tab ID on which the behavior
occurred, the circumstances surrounding how the behavior took
place (use of toolbar, use of menu, use of right click, mouse,keyboard, short cuts etc.), what the behavior represented (printing
out a page, copying text, etc.), the URL of the page where theevent occurred, and the user interest information (whether thepage was liked, disliked, or unknown). Furthermore, web page
text data, detail query keywords, and character strings inputted
via keyboard were collected for future use.

4.2 Data Gathering
10 unpaid volunteers (6 male and 4 female) were recruited toparticipate in this study. They ranged in age from 21 years old to38 years old (mean age: 29.1 years). All of them had between 4
and 12 years of web browsing history (mean history: 8.3 years).
Almost all the participants used the internet for more than 6 hours

per day (mean usage time: 10 hours per day), mainly either during
work hours or for pleasure and entertainment. All participants
were bilingual (Japanese and English); 3 worked as translators, 3
as software engineers, 2 as office workers, and 2 were fulltime
students. The participants used the GINIS browser for a period of
16 to 31 days (mean usage duration: 22 days).

4.3 Preliminary Data Processing
GINIS was built with very high logging functionality to enable
future use of the data. The original log file comprises over
460,000 lines, including 195,000 action data. In the preliminary
data processing stage, we performed a data clean-up to 65%.
Around 65,000 lines were left after this process. Most of theremoved data were mouse movement logging (mouse locus andaxis) and text copied/pasted to/from the clipboard by the user.
This text information was logged but has not yet been used. We
believe that these logs could be used in future for building user
profiles for personalization.

4.4 Data Summary
Table 2, shows the 5 most and 5 least frequently performed
behaviors. Behaviors that did not take place were omitted. The
total number of behaviors logged from the users was 64,312.
There was an average 5.2 user behaviors per page.

ScrollKey Input

Frequency(times)
19091
14188

Form Input

9329

Behavior

Behavior
Go Forward
Stop Loading
Add
toFavorite

Frequency(times)126
88

Navigation
4585 Print
Link
Search Text
1284 Save As
Table 2: Occurrence of the 5 most and least frequent
behaviors7964
2On-page “Scroll” was the most common behavior performed on a
page. We do support the finding of [5] that “Navigation Link” isstill the most important behavior performed on the browser. Even
though our results showed “Navigation Link” occurred only 4585
times, placing it fourth, the three behaviors which were ranked
higher in occurrence (Scroll, Key Input, and Form Input) all takeplace multiple times while “Navigation Link” tends to be
performed singly.

5. RESULTS
5.1 Result A: Discovery of Shared User
Behavior Rules
5.1.1 Overview
Classification learning was performed using the C4.5 machinelearning algorithm. 2856 cases were used as training data. The
C4.5 error rate was set at 25%. As a result, 13 rules were obtained.
8 of these were found to be rules governing pages “not of interest”,
and 5 of these were rules governing pages “of interest.” Out of
2856 training data, 1997 pages were classified as “of interest” and859 pages were classified as “not of interest” by the test
participants.
2027 pages (70.97%) were correctly classified, and 829 pages(29.03%) were incorrectly classified at this point. As a result of

performing 10-fold cross validation, the Confusion Matrix
indicated in the following table was obtained.
Table 3: Classification Learning Results (before removing the
instances of inconsistency)
User
Evaluation

Classified as“of interest”

Classified
as“not-of-interest”

Total

Of Interest

1852

145

1997

Not of Interest

684

175

859

As a result of validating the data, we found that our evaluations
contained many instances of inconsistency. Following on from
this, we performed a clean-up of the data. We further refined thedata using a method of adopting those cases where inconsistentevaluations were more frequent, given the occurrence of the same
behavior only, and an equal number of random cases. As a result,2249 cases were left, and within these 1885 were judged as “ofinterest” to the user and 364 were judged as “not of interest” by
the user. Of these evaluations, 2005 pages (89.15%) werecorrectly classified, and 244 pages (10.85%) were incorrectlyclassified. This represented a significant improvement inclassification capability. Table 5 shows the Summary of Result A.
Table 4: Classification Learning Results (after removing the
instances of inconsistency.
User
Evaluation

Classified as“of interest”

Classified
as“not-of-interest”

Total

Of InterestNot of Interest

1808

77

1885

167

197

364

Table 5: Summary of Result A
Item

BeforeTotal
Number
Instances

After

of

2856

2249

Correctly
InstancesClassified

Incorrectly
InstancesClassified

2027(70.97%)829(29.03%)

2005(89.15%)244(10.85%)

5

5

8

21

0.158
0.385

0.139
0.296

Number of “of interest”
rules
Number of “not ofinterest” rules
Mean Absolute Error
Root Mean Squared Error
Relative Absolute Error

91.61%

51.07%

Root Relative
Error

Squared99.36%

80.38%

Precision of “of interest”

0.730

0.915

Recall of “of interest”

0.927

0.959

of

0.547

0.719

Recall of “not of interest”

0.204

0.541

Precisioninterest”

of

“not

5.1.2 Rules
Rules were created using C4.5, after first removing inconsistentevaluations. 26 rules were output in total. 5 of these were found to
be rules governing “of interest”, and 21 of these were rules
governing “not of interest.” All the rules “of interest” and the“not of interest” are presented below. The default class was set to“of interest”. The term “Stay Time” in these rules is indicated inseconds; all other terms are indicated by number of occurrencesof that particular user behavior. The term “Key Input” represents
all keyboard stroke actions, while the term “Form Input”
represents user behavior in inputting for forms available on web
pages (using the keyboard).

5.1.3 Rules “Of Interest”
These are the 5 rules for pages tagged “of interest” by C4.5
classification system.
Rule 112:
Scroll <= 1
Form Input > 1
Key Input <= 2Move Back <= 0
Æ Class Interested [97.5%]

Rule 15:Scroll <= 0
Reload <= 0
Key Input <= 6ÆClass Interested [94.3%]

Rule 67:
Scroll <= 3
Reload <= 0
Search Text <= 0Form Input <= 3Move Back <= 0ÆClass Interested [93.5%]
Rule 101:
Scroll > 3
Reload <= 0Form Input > 1
Navigate <= 0
Key Input <= 8Move Back > 0Æclass Interested [84.5%]

Rule 25:
Reload <= 0Form Input >= 1
Æ Class Interested [87.2%]

5.1.4 Rules “Not of Interest”
These are the rules for pages tagged “not of interest” by C4.5
classification system.
Rule 2:Scroll <= 0
Search Text <= 0Form Input <= 0
Key Input > 0
Key Input <= 5Move Back <= 0
Text Copy <= 0ÆClass Not of Interest [92.6%]
Rule 28:
Scroll <= 0
Key Input > 9
Key Input <= 10Move Back <= 0
Text Copy <= 0ÆClass Not of Interest [84.1%]
Rule 20:
Form Input > 2
Move Back > 1ÆClass Not of Interest [82.0%]

Rule 24:
Stay Time <= 16
Scroll <= 0Form Input > 0
Form Input <= 1

Rule 69:
Stay Time > 3
Scroll > 0
Search Text <= 0Form Input > 3
Form Input <= 4ÆClass Not of Interest [88.2%]
Rule 57:
Search Text <= 0Form Input <= 0
Key Input <= 0Move Back > 0ÆClass Not of Interest [83.7%]
Rule 52:
Stay Time > 3
Stay Time <= 5
Scroll > 0
Scroll <= 16Form Input > 0
Form Input <= 1ÆClass Not of Interest [82.0%]
Rule 36:
Stay Time > 128
Stay Time <= 259
ÆClass Not of Interest [79.4%]

Rule 108:
Reload > 0
Search Text <= 0Form Input <= 1
Text Copy <= 0ÆClass Not of Interest [75.9%]

Rule 21:
Scroll <= 0
Key Input > 2
Key Input <= 6Move Back > 0ÆClass Not of Interest [75.8%]
Rule 116:
Reload > 0
Form Input > 2
Move Back > 0ÆClass Not of Interest [75.8%]

Rule 32:
Form Input <= 3
Key Input > 15
Key Input <= 16ÆClass Not of Interest [75.8%]

Rule 34:
Form Input > 2
Key Input > 6
Key Input <= 7ÆClass Not of Interest [70.7%]

Rule 99:
Stay Time > 5
Scroll > 3
Search Text <= 0Form Input > 1
Move Back <= 0ÆClass Not of Interest [68.6%]
Rule 39:
Stay Time > 20
Reload <= 0Form Input > 3
Key Input > 6
ÆClass Not of Interest [63.0%]

Rule 78:
Scroll > 1
Scroll <= 2
Search Text > 0
Form Input > 1
Form Input <= 3
Key Input <= 1ÆClass Not of Interest [66.2%]
Rule 103:
Navigate Link > 0
Move Back > 0ÆClass Not of Interest [63.0%]

Rule 17:
Search Text > 0
Form Input > 0
Form Input <= 2Move Back > 0ÆClass Not of Interest [70.7%]

instance of inconsistency. Figure 3 shows the percentage ofcorrectly classified instances per participants. Table 6 shows the 2users detail statistical data.
Correctly Classifed Instances

100.00%90.00%

80.00%

70.00%% Correctly Classi

Key Input > 6
Key Input <= 35ÆClass Not of Interest [80.6%]
Rule 117:
Scroll > 1
Reload > 0
Form Input > 1
ÆClass Not of Interest [77.0%]

60.00%

50.00%

40.00%

30.00%

20.00%

10.00%

0.00%
Participant Participant Participant Participant Participant Participant Participant Participant Participant Participant
A
B
C
D
E
F
G
H
I
J
Participants

Figure 3. The percentage of correctly classified instances, byparticipants
Table 6: Summary of Result of Participant A and B
Item

Rule 83:
Scroll > 3
Form Input > 1
Form Input <= 2Move Back <= 0ÆClass Not of Interest [59.3%]

Rule 81:
Scroll > 0
Search Text > 0
Form Input > 3ÆClass Not of Interest [56.6%]

5.1.5 Conclusion
After removing inconsistent evaluations, classification accuracy
improved dramatically. Dramatic improvement can also be seenin the precision and recall data.

Participant A

Participant B

of

297

353

Classified as “of interest”

163

342

Classified
interest”

of

134

11

Correctly
InstancesClassified

225(75.75%)350(99.15%)

Incorrectly
InstancesClassified

72(24.24%)3(0.85%)

Number of “of interest”
rules

4

2

Number of “notinterest” rules

620.0168

Total
Number
Instances
as

“not

of

We found that most of the rules generated were not governed by
Stay Time, which conflicts with the findings of most previous
studies. As Rule 36 shows (with an accuracy of 79.4%), whentime spent on a page is in the range of 2-4 minutes, the web pageis likely to be “not of interest” to the user.

Mean Absolute Error

0.3022

Root Mean Squared Error

0.4492

0.0920Relative Absolute Error

61.02%

26.59%

Root Relative Squared
Error

90.27%

52.91%

5.2 Result B: Discovery of Individual User
Behavior Rules

Precision of “of interest”

0.731

0.991

5.2.1 Overview

Precisioninterest”

It is natural to expect that browsing behavior will vary with theindividual habits of each user. For Result B, the individual
behavior of each user was analyzed and rules were generated. The
experimental data used to generate Result B were the same as
those used for Result A, listed above. The classification learning
process was performed on a per-user basis after removing each

Recall of “of interest”

0.883

1.000

of

0.810

1.000

Recall of “not of interest”

0.604

0.727

of

“not

5.2.2 Rules generated for Participant A

5.2.4 Conclusion

For Participant A, 4 rules of interest and 6 rules of non-interest
were generated by C4.5. The default rule was set to “of interest.”

The individual rules generated for Participants A and B were notgoverned by the time spent on a particular web page. This finding
was common among all the other 8 participants of the experiment
as well.

The detailed rules for Participant A are shown below.
Rule 31:
Navigate Link > 0
Text Copy > 1
Æ Class Interested [90.2%]
Rule 16:
Stay Time <= 7
Search Text > 0Æ Class Interested [84.3%]

Rule 24:
Scroll > 3
Form Input > 1
Æ Class Interested [77.2%]
Rule 9:Scroll > 1
Scroll <= 8Form Input > 1
Form Input <= 2
Navigate <= 0
Key Input <= 1ÆClass Not of Interest [91.5%]
Rule 17:
Search Text < 0Form Input < 3ÆClass Not of Interest [75.8%]

Rule 7:
Scroll > 0
Scroll <= 5
Form Input > 0
Form Input <= 2
Æ Class Interested [88.2%]
Rule 26:
Scroll <= 5
Search Text <= 0Form Input > 1
Key Input(Keyboard) > 8
Key Input(Keyboard) <= 32
Æ Class Interested [78.9%]
Rule 3:Form Input > 0Æ Class Interested [75.7%]
Rule 6:Scroll <= 0Form Input > 0
Form Input <= 2
Navigate <= 0
Key Input(Keyboard) <= 8ÆClass Not of Interest [90.3%]
Rule 32:
Navigate Link > 0
Move Back > 0ÆClass Not of Interest [50.0%]

For Participant A, Rule 31 shows that when navigating to a newpage and performing the “Text Copy” behavior, the web pageviewed is 90.2% likely to be “of interest”. Rule 24 and Rule 3,with accuracies of 77.2 % and 75.7% respectively, show that the
web page viewed is likely “of interest” when Participant A scrolls
more than 3 times and either inputs some text on the page orinputs an online form.

5.2.3 Rules generated for Participant B
For Participant B, a total of 4 rules, (2 “of interest” and 2 “not ofinterest”) were generated by C4.5. The default rule was set to “ofinterest.”
The detailed rules for Participant B are shown below.
Rule 2:Scroll > 3
Text Copy > 1
Æ Class Interested [97.3%]
Rule 3:
Reload <= 0
Key Input <= 1ÆClass Not of Interest [66.2%]

Rule 8:
Reload > 0
Search Text > 1
Form Input > 2
Æ Class Interested [89.1%]
Rule 13:
Scroll < 3
Key Input <= 1ÆClass Not of Interest [33.3%]

The rules generated for Participant B had low accuracy for “not ofinterest” because there were generally very few instancesclassified as “not of interest” by Participant B. As for instances“of interest”, Rule 2 predicts with an accuracy of 97.1% that when
Participant B is viewing a page and has both scrolled more than 3times and copied some text on the page, the web page being
viewed is “of interest.”

6. Discussion
6.1 Browser Usability
After the experimental stage, we asked the 10 test participants toevaluate the browser with 3 simple questions: How is theusability? Does GINIS contain all the features of a browser for
normal daily use? Were you able to browse as if using yournormal favorite browser? We obtained the score of 4.2, 4.3, & 4.3
for these questions respectively (on a 5-point evaluation scale).
This demonstrates that the browser is easy to use and that users
could navigate through their daily browsing without additional
stress.
We choose Internet Explorer over other browsers because it iseasy to log high-granularity data using the provided
Microsoft .Net Framework. Despite the efforts made by otherbrowsers, Internet Explorer still maintains over 70% share ofusage. It is the most commonly-used browser at the moment,
making it easier to recruit participants for experiments.

6.2 Data Cleaning
Two things that affected our clean-up result were Google’s result
list page and the use of online dictionaries. Most of the form input
takes part here over at these pages. It is quite common forJapanese web users to consult online dictionaries. All ourparticipants used a few freely available online dictionaries, whichgave us considerable difficulty in cleaning up the results. The
participants in this study tended to search for a particular word
and then evaluate the answer of the query. In future experiments,
we hope to restrict these sorts of pages from our results. These
restrictions were not applied at the time of this initial experiment,
so we expect more accurate results in the future.

6.3 Classification Rules
One early objective of this work was to locate common featuresof web browsing shared by all users. After all user behaviors weregathered, the removal of inconsistent evaluations yielded atremendous improvement in classification learning accuracy.
From Result A, mean accuracy was 91.4% (SD=4.80), precision
was 0.915, and recall was 0.959 for the rules “of interest,” which
was a very satisfying result for this classification learning.
For the classification “not of interest”, mean accuracy for the 21
rules generated was 74.8% (SD=9.32), with precision of 0.719
and recall of 0.541. Over the experiment, only 364 pages wereclassified as “not of interest”, which is around 18.2 % of the total
data. Since the experiment was not performed in a controlledenvironment, it was difficult to collect an equal number of “ofinterest” and “not of interest” web pages.
Most rules were found not to be governed by the time a user
spends on a web page. This conflicts with the findings of mostprevious studies. We believe that this represents a major finding
for this research.

6.4 Limitations of this Research
At present, our research was limited to searching for patterns ofuser behavior from all the behaviors available during browsing.
We did not make detailed examination of the order of occurrence

of the behaviors themselves. Also, we were not able to take moregranular behaviors into consideration. For example, the user
behavior “Scroll” might actually be broken into “Scroll Up”,
“Scroll Down”, “Scroll Jump Up”, “Scroll Jump Down,” whichmight then be studied in further detail by taking into consideration
the means and situation of occurrence of this behavior (whether
mouse or keyboard was used, etc.).
One more factor limiting this research at the moment is that thelog was collected in detail, but the log cleaning process was
performed semi-automatically. This is because navigation actions
tend to occur simultaneously, often making it difficult todetermine which navigation action is part of which navigation
behavior.

7. Ongoing and Future Work
The scroll action is the most frequently performed action on a
page (after mouse action was removed from the log during ourcleanup); we assume that more detailed study of this scroll action
will result in a better understanding of action/behavior
conversions.

9. REFERENCES[1] Catledge, L.D, Pitkow, J.E.: Characterizing Browsing
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[2] Morita, M., Shinoda, Y.: Information Filtering Based onUser Behavior Analysis and Best Match Text Retrieval, In
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[3] Quilan, R. : C4.5: Programs for Machine Learning,
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A great number of mouse locus were logged during thisexperiment, and these still have to be studied carefully, as there isa strong possibility of finding new behaviors as well as valuable
new research ideas.

[6] Speretta, M., Gauch, S.: Personalizing Search Based on

At present, we have only taken into consideration the frequencyof occurrence of a behavior on a single page during classification.
There is a great need to carefully study the order of appearances,which we think would give us a better understanding of therelationship between user interest and user behavior.

[7] Kim, H., Chan, K.: Implicit Indicators for Interesting Web

We also intend to consider sessions of behavior, such as looking
at the sequence of web pages that led to a user’s successful
outcome, rather than just the final page of a successful outcome.
For example, when a user performs form input (such as making
online applications or purchasing items online), there is a strong
possibility that a user was interested in any pages visited which
led to or helped in the process of deciding to purchase a particular
item or to input in a particular form.
One more interesting issue arising during this work is the need fora standard format for client-side logging, (e.g. a standard XML
format), so that other researchers can collaborate and parse logs
easily and perform other methods of machine learning or data
mining. As with many experiments, the greatest amount of time
was spent collecting and cleaning up data.
Browsing behavior may well differ depending on whether the user
is searching for something in particular or just using the Web forentertainment. This is an area where further research may beparticularly useful.

8. CONCLUSION
We have presented here a method to automatically detect and log
user behavior at the client-side by creating a client-side browser(the GINIS Framework). We used this framework to conduct anopen environment field study (rather than a controlled laboratory
study).
Past research has tended to focus on the time spent on a web pageas the main factor in evaluating implicit user interest. Our
experiment confirms that the time spent on a page is not the only
main factor; scroll action, form action, searching text, copying
text etc. should also be taken into consideration as factors inevaluating user interest.

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[14] Personalized Web Search, Google Labs,
http://labs.google.com/