International Journal of Knowledge-based and Intelligent Engineering Systems 8 (2004) 149–160
IOS Press

149

A system for generating user’s chronologicalinterest space from web browsing history
Harumi Murakamia,∗ and Takashi Hirata baGraduate School for Creative Cities, Osaka City University, 3-3-138, Sugimoto, Sumiyoshi, Osaka 558-8585
Japanb
Japan Self Defense Force, Japan

Abstract. We propose a method that helps users to understand their own interests by extracting terms from selected link texts andgenerating a new browsing history, and arranging those terms and Web-page icons on to the user’s interest space in chronologicalorder. We have implemented a prototype system based on this method. The system’s performance was evaluated in two
experiments, which revealed that (1) Japanese interest terms can be extracted from the user’s selected link texts, (2) the interestspace browser displays the user’s interest space on browsing the Web, and (3) the users can use this system to better understandtheir own interests, recall their past, and reorganize previously browsed Web pages.

1. Introduction
We often hear people say “Do what you like, and dowhat you are interested in.” Many say that it is important to understand ourselves and do what we like for asense of achievement or happiness. Psychologists havecreated numerous methodologies to help us understandourselves. Vocational Preference Inventory (VPI) is
one of such examples that help people to understandourselves in terms of vocational preference. Thesemethodologies are mainly based on a given set of questions. However, it is difficult to find something which is
not written in the questions. In addition, methodologies
using computers have not been thoroughly examined.
The long-term goal of our research is to define methodologies of “understanding ourselves” using computersand develop systems using these methodologies.
As one element of this research, in this paper we
aim to develop a system for understanding our interestsby accumulating data related to our various activities
on computers. That is, we examine Web browsing
activities that take place in our daily lives.
∗ Corresponding author. Tel./Fax: +81 6 6605 3375; E-mail:harumi@media.osaka-cu.ac.jp.

Web browsers such as Microsoft Internet Explorer(hereafter IE) [1] or Netscape Navigator [2] have a“Web browsing history,” which records URLs accessedby the user with dates and titles of the Web pages. Users
can access previously accessed Web pages using thishistory.
It may be possible to develop a system to understandthe user’s interests by using this history. However,there are two problems. (1) Titles sometimes lack information on the contents of the Web pages. (2) Existing browsers only present a list of URLs of Web sites
accessed one day and more ago, and it is difficult todetermine what kinds of Web pages the user accessed
before opening the URLs. In the former case, it may
be more useful to see the “link text” or “anchor text”(hereafter, “link text”) that the user selected than thetitle of the Web page. In the latter case, it may be more
useful to see some keywords expressing the contents of
the Web pages than the URL lists.
In this research, we focus on the user’s link selecting
activities and suggest two hypotheses. (1) It is possible
to extract terms that express the user’s interests fromlink texts selected by the users. (2) It is possible todevelop a system to support users in understanding theirinterests by presenting these terms chronologically.
Based on the above hypotheses, we present a methodof extracting terms from link texts and saving these

ISSN 1327-2314/04/$17.00  2004 – IOS Press and the authors. All rights reserved

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H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

terms as a new browsing history (hereafter “new history”) with URLs and dates, arranging terms and “Webpage icons” that express Web pages in two dimensions,and presenting these terms and icons chronologically.
We call the 2D space in which terms and Web-pageicons are arranged “interest space,” and the system thatenables users to access previously browsed Web pages
through the chronological interest space an “interestspace browser”.
We have developed a new Web browser (hereafter“new browser”) that generates a new browsing history
and the interest-space browser, and performed two experiments.
We will describe the proposed method in Section 2,
and the experimental results in Section 3. Related work
will be discussed in Section 4.

New browser
Display

Display(Prime Minister Koizumi called the Security
Council of Japan urgently = he considers sending
an international rescue team – World Trade
Center Attack (Jiji Press))

Step 5

Step 7

Step 1

New history
Date

URL

Wed Sep 12
16:35:33 …

[
URL1(World Trade(yahoo.headlines…) Center and Pentagon Attack) ]List of extracted keywords

Wed Sep 12
16:35:58 …

[
URL2(World Trade(yahoo.headlines…) Center and Pentagon Attack) ](Note) same as
URL1

Wed Sep 12
16:36:03 …

[(New York 11th
URL3
(aircraft) ][(yahoo.headlines…) Jiji)][(continuously) ]…

Wed Sep 12
16:37:50 …

URL4(World Trade[(yahoo.headlines…) Center and Pentagon Attack) ]Wed Sep 12
16:38:06 …

URL5(www.jda.go…)

Wed Sep 12
16:38:20 …

URL6[(yahoo.headlines…)[
team) ][

(international rescue(sending) ](Prime Minister Koizumi) ][(Security Council of Japan) ][(called urgently =international rescue team) ][(sending) ][(considers –World Trade Center Attack) ][(Jiji Press) ]2. Method
Step 22.1. Overview(World Trade Center and

The proposed method is constructed from Step 1 to
Step 7. Figure 1 shows an overview of the method.
– Step 1: When the user selects a link using a newbrowser, “date, URL designated by the link, terms
extracted from a line including the link” are saved
as a data set in a new history.
– Step 2: When the user executes “generate interest
space” on an interest-space browser, the systemtreats “terms that occur more than twice in the newhistory (referred to as “interest terms”) as category data, and treats URLs that have those interest
terms as “sample data”, and Hayashi’s quantification theory III [3] (hereafter “Hayashi’s theoryIII”) is then applied to this data.
– Step 3: When the user executes “display interest
space” on the interest-space browser, the interest
space is displayed based on the results of Step 2.
– Step 4: When the user operates a “date bar” thatchanges the date and a “period bar” that changes
the period of display, the display of the interestspace browser changes based on the date information.
– Step 5: When the user double-clicks Web-pageicons that express Web pages on the interest-spacebrowser, the new browser connects to the Internet
and displays the designated Web pages.
– Step 6: When the user executes “open new history”
on the interest-space browser, the system displays
the list of the history for the current date.

(sending)

Pentagon Attack)Hayashi’s
Quantification URL1, URL2 1
URL41
Theory III

Step 6

0
0

URL5

0

1

URL6

0

1

Step 3
Interest-space browser

Wed Sep 12 16:38:30 JST 2001
(World Trade Center and Pentagon Attack)(World Trade Center andPentagon Attack)(World Trade Center and Pentagon Attack)(International rescue team)(sending)(Prime Minister
Koizumi)(sending)(sending)(Security Council of Japan)

Step 4

Note: The text in this example was originally Japaneseand has been translated into English.
Fig. 1. Overview of the proposed method.

– Step 7: When the user double-clicks a piece ofdata in the history, the new browser connects to
the Internet and displays the designated Web page.
2.2. Accumulating the new history on selecting links
When the user selects a link on a new browser, date,
URL designated by the link, and terms extracted froma line including the link are saved as data set in a new
history.

H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

URL

headlines.yahoo.co.jp/hl?c=int&t=lheadlines.yahoo.co.jp/hl?a=20040203-00000924-reu-int<title>–(Yahoo! News - Overseas)</title><a href="http://headlines.
yahoo.co.jp/hl?a=20040203…"><title>Selectinglink–(Yahoo! News –Overseas - Reuters)</title>(The SecretaryGeneral of the United
Nations will meet the US President todiscuss Iraq incident etc.)</a>(Reuters)

Linked Web page(e.g. Newspaper article)

Web page containing link text
(e.g. List of newspaper headers)Date URL

Date URL

Title–
Date headlines.
(Yahoo!
yahoo.co.
jp/hl?c=in News – Overseas)t&t=lDate headlines.
yahoo.co.
jp/hl?a=2 (Yahoo!0040203 Overseas–
Reuters)…

compare–Web browsing history in existingbrowsers

Keywords

Date headlines.yahoo. (The Secretaryco.jp/hl? General of thea=2004 United Nations)
0203…
(Iraq
incident)(US
President)(meet)(Reuters)

New history(Web browsing history in thisResearch)

Note: The text in this example was originally Japaneseand has been translated into English.
Fig. 2. Comparison of histories in existing browsers and in the newbrowser used in this research.

Figure 2 shows the difference between the “history”
of existing Web browsers and the “new history” in thisresearch. The existing browsers save a date, a URL of
Web page, and text extracted from title elements as adata set in the history. In contrast, our new browser
saves a date, a URL designated by anchor elements,
and terms extracted from a line including the selected
link as a data set in the new history.
For example, consider that the user selects a link“
(The Secretary-General of the United Nations will meet with
the US President to discuss Iraq incident etc.)” from
“Yahoo!
–– (Yahoo! Japan News –Overseas)”. The history of the existing browser saves“Yahoo!
––– (Yahoo!
News – Overseas - Reuters),” which is extracted froma title element of the Web page. Our new history saves“(The Secretary-General of the United Nations), “(Iraq incident),” “(US
President),” “(meet),” and “(Reuters),”
which are extracted from a line including an anchor
element of the previous Web page.
There are two methods for extracting terms fromJapanese texts: (a) extracting mainly nouns using morphological analysis, and (b) extracting mainly non-151

hiragana characters by judging the characters. We use
the latter method because it is easy to extract terms
whose parts of speech are unknown.
In this research, we will focus on (1) extracting terms
consisting of two or more characters (excluding hiragana or special characters), and (2) deleting unnecessary terms according to several heuristic rules and stop
word lists created by the users.
The “judging unnecessary term” heuristic judges unnecessary terms from a text. We implement three kindsof heuristics: (a) numerical unnecessary term judgment, (b) date unnecessary term judgment, and (c) otherunnecessary term judgment. For example, using (a),characters “10” “200” are judged as number unnecessary terms; using (b), characters consisting of “
(year)” “ (month)” “ (day)” and numbers are dateunnecessary terms; and using (c), characters ending
with [ (-like)] are judged as other unnecessary terms.
The details of the heuristics are described in [4].
The user can select unnecessary terms displayed on
the interest-space browser and record them in a stop
word list.
In an example of Step 1 in Fig. 1, when the user
selects a link “=(Prime Minister Koizumi called the Security Council of Japan urgently= he considers sending an international rescue team– World Trade Center Attack),” the following extracted terms are saved in the new history: “(Prime Minister Koizumi),” “(Security Council of Japan),” “=(called, urgently = international rescue team),” “(sending),”“(he considers – World Trade Center
Attack),” “(Jiji Press)” at URL6. If the user
registers “(Jiji Press)” in the stop word list, it
will not be saved in the history.
2.3. Generating and displaying interest space
The user can select objects from “interest terms only”
or “interest terms and Web-page icons” for display on
to an interest space. This is intended to help users to
explore the interest space quickly by selecting “interest
terms only”; when the user finds the date of interest,
he can select “interest terms and Web-page icons” toaccess the browsed Web pages.
When the user executes “generate interest space”
after selecting objects to display in the interest-spacebrowser, the system treats terms that occur two times
or more in the new history (interest terms) as category

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H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

data and URLs that have interest terms as sample data,and calculates Hayashi’s theory III.
Hayashi’s theory III is a kind of principal component
analysis for qualitative data that quantifiers binary elements, classifies samples or categories, and investigatescharacteristics of the data. By using Hayashi’s theory
III, similar data can be arranged close together: sample data with sample data, category data with categorydata, and sample data with category data.
An example of Step 2 in Fig. 1 shows that(World Trade Center and Pen“
(sending)” are treated as intagon Attack)” and “
terest terms and category data, and that URL1, 4, 5, 6,
which contain these terms, are treated as sample data.
If the URLs are the same, they are merged as one URL.
For instance, URL1 and URL2 are treated as URL1
and the frequency of URL1 becomes 2. Next, regarding URL1, “1” is entered in the “(World Trade Center and Pentagon Attack)” field because URL1 contains this term, and “0” is entered in(sending)” field because it does not containthe “
the term.
When the user executes “display interest space”, theinterest space is displayed. The default date is the latest
date in the new history and the default period is 30
days. Therefore, interest terms and URLs that occur
within 30 days of the latest date are displayed. The 1st
array becomes the X array and the 2nd array becomesthe Y array, based on the results of calculation using
Hayashi’s theory III. The details of the calculation of
1st array and 2nd array are described in [4].
Interest terms are displayed as terms surrounded bysquares. Web-page icons are displayed as icons of theearth. The Web-page icons change their size according
to the frequency of URLs in the new history: large(three times or more), middle (twice), and small (once).
They change their color according to the number ofdays counted from the current date: blue (three daysor less), dark gray (seven days or less), and pale gray(more than seven days).
When the user sets a mouse cursor to a Web-pageicon on the interest-space browser, extracted terms are
displayed around the icon as “Web-page keywords” tohelp users to understand the contents of the Web page
before opening the Web page.
An example of Step 3 shows that when the user
executes “display interest space”, “(Word Trade Center and Pentagon Attack)” and “(sending)” are displayed as terms in a square, and four
different URLs are displayed as blue Web-page icons.
The user can find Web pages referring to interest terms

DateInterest term

Web-pageicon

Web-page
keyword

Period bar

Date bar

Fig. 3. An interest-space browser.

because the similar interest terms and Web-page icons
are displayed close to one another. The user can findfrequently browsed Web pages because the size of icons
changes according to the frequency of selection. For
example, the Web-page icons of URL1 are displayedlarger than other Web-page icons.
Figure 3 shows a screen example of the interest space
for the first author on 16:38:30, 12th of September,(World2001, in Experiment 1. “Trade Center and Pentagon Attack)”, “(Japan Association of Library and Information Science)”, “(TV announcer Ms. KUBO,
Junko)”, and “(maternity leave)” are displayed asinterest terms. The Web-page icon whose Web-page
keywords are marked is URL1 and 2 of Fig. 1. The
first author noticed that she often checked news about
the World Trade Center and Pentagon Attack, recalledthat she was writing for the annual meeting of Japan
Association of Library and Information Science to beheld in October, and noticed that she was interested ingossip about female TV personalities.
2.4. Displaying previously accessed Web pages
There are two methods for displaying previously accessed Web pages from the interest-space browser.
One is as shown in Step 5 in Fig. 1. When the user
selects a Web-page icon (URL6) referring to the Web(Prime Minister Koizumi)”,page keywords “H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history“(Security Council of Japan)”, and “(sending)” on the interest-space browser, a new browser
will display the Web page of URL6.
Another is as shown in Steps 6 and 7. When the user
executes “open new history”, a list for the new history
that centers on the current date will be displayed, so thatthe user can select URL6 and display the Web page.

3. Experiment
The implemented new browser has some display
problems,1 which are problems of implementation andnot of algorithms.
We then judged that it is difficult to perform experiments in which users are asked to use the browser as
though it were an existing browser, and planned two
experiments described below.
– Experiment 1: The authors, who know the systemvery well, become subjects, perform simulationsof using browsers in everyday life, and discussproblems and the utility of the method.
– Experiment 2: We limit Web sites in order thatusers can operate the implemented system, anddiscuss the usefulness of the method and the system.
3.1. Experiment 1
3.1.1. Method
Subjects were the first author and the second author(hereafter Subject A and B). Subject A was a 37-yearold female, and a university lecturer in information
science in Osaka, Japan. Subject B was a 31-yearold male, a member of the Japan Ground Self Defense
Force, and a doctoral course student in information
science.
They performed a simulation of using a new browser
in everyday life, except for the following conditions.

153– Subjects can use IE to display Web pages when
they feel ashamed if the history is checked by theother subjects.
– Subjects generate and display the interest space
every day that they use the system.
The period of the experiments were: April 9th toDecember 27th, 2001, for Subject A, and May 27th toNovember 13th, 2000, for Subject B. The experiments
were done during working hours on weekdays in thelaboratories, in principle.
3.1.2. Results and discussion
Overview For Subject A, the number of selected
links was 1,485, with the following contents: Yahoo!
Japan (yahoo.co.jp): 611 (41%); Osaka City University(osaka-cu.ac.jp): 94 (6%); Osaka University (osakau.ac.jp): 27 (2%); other: 753 (51%). The subject often
viewed Yahoo! Japan and Web sites of universities
in Osaka. The number of extracted terms was 2,488.
The average number of terms per link was 1.68. The
number of terms registered in her stop word list was
117.
For Subject B, the number of selected links was
808, with the following contents: Asahi Shinbun(www.asahi.com): 549(68%); Yahoo! Japan (yahoo.co.jp): 72 (9%); goo (goo.ne.jp): 28(3%); other:
159(20%). He viewed newspaper Web sites and thenewspaper pages of Yahoo! Japan. The number ofextracted terms was 2,112 (2.61 terms per link on average), and 748 terms 2 were registered in his stop word
list.
Extracting terms We judged whether interest terms
are adequate as words: about Subject A, 91% wasadequate and 93% about Subject B.
Problems of extracting terms are classified into twogroups, as follows.

– Subjects can use IE when an urgent need arises.
– When a Web page cannot be displayed, the page
and the following selecting links can be displayedusing IE.
– Subjects can use IE to display Web pages when
they notice displaying problems.

1. Problems concerning recall rate in Japanese. (a)Terms including hiragana characters cannot be(Tsukuba City)”). (b)extracted (e.g. “Terms consisting of one character cannot be extracted (e.g. “ (forest)”).
2. Problems concerning precision rate in Japanese.
(c) Unnecessary symbols are attached (e.g. “→ (result→)”). (d) Symbols or parts of symbols
are extracted (e.g. URL). (e) Terms tend to be long
because of continuation of parts of speech. (f)1 For example, some web pages in particular using CGI and framescannot be displayed, misaligned or garbled, or it takes a long time todisplay these pages.

2 However, his stop word list was recorded for a different reasonfrom this research and it is unclear how much of the stop words outof the 748 applied to this research.

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Hiragana characters are cut from terms that end(reexamine)”.
with hiragana characters (e.g. “(g) Terms are cut for unknown reasons.
Problems (a) and (b) cannot be solved by only thepresented method by judging characters, and therefore morphological analysis or other methods should be
added. Problems (c), (d), (e), and (g) can be solved bydeleting unnecessary characters. Problem (f) is not major problem because users can understand the meaningof the terms.
Extracting interest terms Here, we consider how
our method is useful in extracting interest terms byinvestigating ten high-frequency terms.
The top ten high-frequency terms for Subject A,
without using her stop word list, were: “(message)” (258), “(first)” (130), “(last)”(list)” (89), “. . .” (49), “(128), “(shopping)” (42), “http” (39), “(mes(entertainsage list)” (39), “(home)” (32), and “(travel)”ment)” (32), “(32).
For Subject B, the top ten high-frequency terms
without using his stop word list were: “Asahi” (229),(news)” (147), “Yahoo” (76), “(list)”“(61), “(test)” (59), “goo” (56), “(page)”(Mainichi test(33), “Mainichi” (32), “(sports)” (19).
data)” (24), and “
These do not express the subjects’ interests very well.
The top ten terms using her stop word list for Subject(Japan)” (22),A contained the following terms: “(economy)” (12), “(local information)”“(12), “(district)” (12), “(computer)”(university)” (11), “(travel)” (10),(11), “(education)” (10), ““Java” (10), “(Internet)” (9), “(corporation)” (9), “(re(sports)” (9), and “
search)” (9), “(business)” (9). These express the subject’s interests asa university faculty member in the field of information
science.
Top ten terms using his stop word list for Subject(China)” (13), “(North Korea)”
B were: “(11), “(Prime Minister Mori)” (11), “(Russia)” (9), “(Yasser Arafat)” (6),(Israel)” (6), “(Presi“dent Clinton)” (6), “(President Putin)”(prime minister)” (5), “(Fiji)” (5), and(6), “(Minister of State for Defense)”(5). These“
express Subject B’s interests in international politics asa member of the military.
The above example suggests that high frequency
terms include unnecessary terms, but these can be re-moved by using stop word list. Based on the results
obtained for Subjects A and B, the percentage of unnecessary terms compared to extracted terms is projected
to be between 5% and 30%.
Terms that do not express the user’s interest are
mainly terms included in link texts for Web pages thatthey often access, such as those included in the Yahoo!
(message)”,
Japan message board (e.g. “(first)”, “(last)” and those included in the Ya“hoo! Japan top page (e.g. “(shopping)”,“(travel)”, and “(music)”).
The above results suggest that the Japanese interest
terms can be extracted from the user’s selected linktexts by investigating extracted high-frequency terms.
Displaying interest space Figure 3 shows that theinterest space can be successfully displayed by using
the presented method. However, the following problems were observed. (a) The interest space cannot be
displayed when no term occurs two times or more. (b)Response is slow when the history is large. (c) Interest
terms and Web-page icons including interest terms areoften displayed close together, but either similar interest terms or similar Web-page icons are not displayed
close together, intuitively. (d) It is difficult to identify
the meaning of X and Y arrays. (e) Interest terms that
occur one time, and Web-page icons that have no interest terms, cannot be displayed. (f) Interest terms andWeb-page icons sometimes overlap and it is difficult to
operate them.
Regarding (a) to (e), we need to improve the methodof generating the history, or to try other displaying
methods such as a self-organizing map. Concerning(b), creating a divided history file can be useful. For(f), scattering displayed objects should be introduced.
The results of Experiment 1 – for example, that the
number of extracted terms for Subject B is larger thanthat of Subject A, and that the contents of the extracted
terms for Subject B are more concrete than those for
Subject A – suggest that better interest terms are extracted when users view newspaper Web sites. In addition, we found that the new browser can successfully
display certain newspaper Web sites. Therefore, weplanned an experiment targeting general users using
newspaper Web sites (Experiment 2).
3.2. Experiment 2
3.2.1. Method
Subjects The subjects were eight women, aged 24
to 57 years old (referred to as Subject A, B, C, . . . H).
Subject A, C, and F were housewives, Subject B was

H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing historya systems engineer, Subject D was a secretary, Subject
E and H were undergraduates students, and Subject G
was an office worker.
Browsing We downloaded newspaper articles(HTML files) over a three-month period (June to August, 2000) from the newspaper Web site “MainichiInteractive [5]” The subjects were asked to browse any
article they wanted to see, at least one page per day.
In the Mainichi Interactive site, “a line including
a selected link” is the same as the link text, and isalso the same as the article header. The title of thearticle is one such as “(Mainichi Interactive Full-text Article).” It contains
no information about contents of the article. In thiscase, it is evident that link texts are better than titles as
resources for term extraction.
Here, we investigate which resource is better: linktexts (i.e. headers of articles) or Web pages (i.e. contentsof articles including headers).
After browsing the article, we asked the subjects toevaluate interest terms as follows.
1. To investigate which more effectively expresses
interests of users–term extracted from link texts or
those extracted from Web pages–we showed twolists of “extracted terms ordered by frequency”
from both link texts and Web pages, and asked
the subjects the following question: “These twolists are interest terms using a different methodfrom your browsing activities. Which list do you
think intuitively to express your interests?”
2. To investigate whether “extracted terms from linktexts that occur two times or more” are appropriate for interest terms, we asked the subjects
to evaluate each extracted term that occurs twotimes or more in the light of “Do you think theterm expresses your interests in your browsing?”
The evaluation is categorized as follows: 5: agreestrongly, 4: agree moderately: 3: neutral; 2: disagree moderately; and 1: disagree strongly. The
average of evaluation values of each term is regarded as “interest rate X” in link texts. In contrast, we also asked subjects to evaluate terms extracted from Web pages whose number was thesame as the number of terms with a frequency of
two or more extracted from link texts. The average of these evaluations is considered interest
rate X for that Web page. 3
3 It was possible to ask subjects to evaluate terms that occur twotimes or more extracted from web pages, but the number of terms
was enormous and the difference in the burden on subjects was large,
so we did not adopt this method.

155

3. To investigate the general quality of the highfrequency interest terms extracted from link texts
and Web pages, we asked subjects to conduct anevaluation as described in 2. above for the tenterms with the highest frequency, and took thisvalue as interest rate Y.
4. It seems that the quality level of interest terms
is different. To investigate which is better forresources to extract interest terms of good quality, we asked the subjects to select the ten terms
that best express interest terms with the greatest
frequency from extracted terms for interest rate
X.
Evaluation of interest space browser To investigate the usefulness of the interest-space browser, after
we asked subjects to “generate interest space” and to“display interest space”, we gave them the following
instructions.
The displayed window automatically shows a space
generated from your Web browsing history. Terms
surrounded by a square are terms extracted as yourinterest terms.
An “earth” icon shows a Web page you browsed.
Gray icons shows past browsing, and larger icons
indicate the frequency of the browsing.
Blue text displayed on an icon when you move thecursor shows terms extracted from the Web page.
There are two slide bars at the bottom of the window, a date bar and a period bar. Please move thedate bar from left to right; this will change the display from past items to current items. Move theperiod bar as you like, and watch the window.
Please move objects (terms and icons) using themouse when they are difficult to see.
Four questions were asked: (Q1) Does the interest
space indicate your interest space when you actuallybrowsed Web pages? (Q2) Is the system useful for understanding your interests? (Q3) Is it useful for reorganizing Web pages?, and (Q4) Is it useful for recallingyour past?
The evaluations were categorized as follows: 5:
agree strongly; 4: agree moderately; 3: neutral; 2: disagree moderately; and 1: disagree strongly. The subjects were asked to describe reasons for each evaluation.
The experiment was done in December 2000 for Subjects A-D, and from July to December 2001 for Subjects E-H.

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Table 1
Rates of interest for extracted terms

Terms

Link texts
1,524 (SD = 842)

Selected links
Terms/Selected links
Interest rate X
Interest rate Y

335 (SD = 256)
3.63 (SD = 0.19)
3.30 (SD = 0.52)
3.89 (SD = 0.34)

Web pages
11,209(SD = 6,870)
As left
33.49 (SD = 3.77)
3.22 (SD = 0.47)
3.19 (SD = 0.47)

Note: Interest rate X for link texts: average of evaluation of extracted
terms that occur two times or more; Interest rate X for Web pages:
average of evaluation of extracted terms whose number is the sameas the number of terms with a frequency of two or more extracted
from link texts; Interest rate Y: average evaluations for ten interest
terms that appeared with the highest frequency.

3.2.2. Results and discussion
Overview Table 1 lists the results: 335 links wereselected, 3.63 interest terms per link were extracted
from link texts, and 33.49 were extracted from Web
pages on average.
Evaluation of extracting interest terms The subjects were asked whether terms from link texts or fromWeb pages matched their interests more closely. All
subjects said that link texts were more helpful.
Table 2 shows the most frequent extracted term fromlink texts and Web pages. It can be seen at a glance thatin the case of high-frequency terms extracted from linktexts, there were more terms expressing proper names
and current topics than in the case of Web pages, andthat it is easier to tell where the user’s interests lie.
Interest terms occurring two times or more were evaluated for each subject. For link texts, the average score
was 3.30, while for Web pages the average score was
3.22 (Interest rate X)[t = 0.19, df = 7, ns]. Average
evaluations for ten interest terms that appeared with thehighest frequency were 3.89 for link texts and 3.19 for
Web pages (Interest rate Y). An analysis of variancerevealed a significant effect for link texts [t = 2.82,
df = 7, p =< 0.05].
Interest rate X is 3.30 and Interest rate Y is 3.89
in link texts. Another analysis of variance showed asignificant effect for Interest rate Y in link texts [t =3.05, df = 7, p =< 0.05]. Interest rate X is 3.22 andInterest rate Y is 3.19 in Web pages [t = 0.19, df = 7,ns].
We asked the subjects to select the ten terms in which
they had the most interest from among the group ofterms with interest rate X. The terms that held the mostinterest were selected from among the link texts by all
subjects, and all terms were in the top one or two interms of frequency rate.
From the above results, we confirmed that in thecase of newspaper articles, it is better to target linktexts than Web pages for interest terms, and to target
high-frequency terms rather than low-frequency terms
for interest terms when using a simple algorithm basedon term frequency. We believe that this indicates the
merits of a method that focuses on links, as describedin this research, and the effectiveness of the proposed
method. Furthermore, we found that when only threemonths worth of newspaper articles are used as theinformation source, interest terms can be extracted fromhigh-frequency terms without the use of stop word lists.
Evaluation of interest-space browser Table 3
shows the results of the evaluation. We consider the
results positive.
Concerning display of interest space (Question 1),
the results were positive. Subject B wrote “Terms express my interest well.” Subject D wrote “I can easilyfind what I saw many times.” These comments wereabout interest terms. Negative comments include “I
couldn’t see the meaning of the space” (Subject B).
Among the comments about understanding of selfinterests (Question 2), “I recognized my interests and I
could recall what I was interested in” (Subject G) and“I notice biased thought” (Subject G) were positive, but
the comment “I cannot see the classification and I don’tknow how the system can be useful” (Subject E) was
negative.
About reorganization of Web pages (Question 3),
positive comments suggest the possibility of reorganizing Web pages according to users interest, like “If I amvery interested in the contents and I want to recall themlater, the system should be useful” (Subject B) and “I
think it is easy to reorganize Web pages by using thesystem because I can find what I am interested in andI can find Web pages that I saw many times” (Subject
C). However, some problems were pointed out, such as“I cannot find the meaning of the space” (Subject A)
and “It is difficult to view the display” (Subject H).
About recalling the user’s past (Question 4), comments such as “The system is useful for recalling pastinterests and news” (Subject H) and “I can recall what
I was interested in before, but I’m not sure that I can
use this to remember my past” (Subject H) suggestthat the system is useful in recalling past interests andnewspaper articles.
In Experiment 2, it was suggested that the interestspace browser displays the user’s interest space onbrowsing the Web, and that the user can understandtheir interest, recall their past, and reorganize previously browsed Web pages by using the system. We
believe that these comments indicate that it is possible
to develop a system for supporting an understanding ofuser interests by targeting newspaper article Web sites.

H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

157

Table 2The highest frequency extracted interest terms
Subjects

Link texts

Web pages

A(Summit)” (3), ““(NY(Sydney Olympic) (3)”, “NY(PC)” (3)stock)” (3), ““(prospect)” (11)B“[

(Sydney Olympic)]” (7)“(confirmation)” (28)C

(Russian nuclear submarine“[
accident)]” (22)“(death)” (112)D“(arrest)” (14)

E

“
(7)

(forgery)” (7), “[

F

“[

G

“[

H

“[

“

(death)” (47)““(manufacture)” (30),(investigation)” (30)(tampered drink)]” (17), “(voluntary recall)” (17)“(manufacture)” (74)(news of someone’s death)]” (6)“(at home)” (20)“(explanation)” (58)(soccer)]” (25)(explosion)]”Note: Terms in brackets [ ] are terms of greatest interest to Subjects.

Table 3
Evaluation for an interest-space browser
Questionnaire(Q1) Does the interest space display your interest space when you actually browsed Web
pages?
(Q2) Is the system useful for understanding
your interests?
(Q3) Is it useful for reorganizing Web pages?
(Q4) Is it useful for recalling your past?

Mean
4.13

SD
0.83

4.25

0.50

3.75
4.00

0.71
0.93

4. Related work and discussion
The prototype system described in this paper is one ofcomponents of a system called Memory-Organizer [6],which accumulates human externalized memory on
computers. Memory-Organizer provides users with an
integrated environment for intelligent information activities; not only Web browsing, but also integratingbookmarks and memoranda, overwriting Web pages,
and reusing search results of search engines. This paper described a method for generating a new history
and an interest-space browser in Memory-Organizer.
Much research concerning visualization of Web
browsing history deals with existing history such as IE,
and there is not much research on methods for generating new history. The typical approach is that “visualization of history helps users in navigating the Web,”in which Web pages or sites are expressed as “nodes”
and the relation between nodes is displayed as a tree
or graph structure. Examples of these can be seen in
MosaicG [7] and PadPrints [8]. This approach is usefulfor “going back” to recently browsed Web pages, as in
the case of existing browsers. However, it is unclear

whether this approach is useful for displaying users’ interests, and for helping people to access browsed pages
they viewed more than one month ago.
WebWatcher [9] and Letizia [10] learn user’s interests for navigating and information gathering; in otherwords, the goal of these systems is to help users access Web pages they might be interested in in the future. However, they do not present what the users saw
visually.
Although different to visualization of browsing histories, there are systems that categorize similar Web
pages or sites and display these groups on a map alongwith terms (e.g., WEBSOM [11], which uses selforganization). The map display can also express theuser’s interests. The question of which method is more
effective–a map display or a 2D space display–is one
that will require further investigation in the future.
There has been research into providing support for
information searches, communication, and the formation of ideas by expressing the user interest space ina 2D space (e.g., [12]). This is similar to the current
research in that user interest is expressed in a 2D space.
However, our work differs in terms of the goals of theresearch (i.e. understanding the user’s interests in ourwork), the information source (i.e. Web browsing history), and the method of display (i.e. displaying theinterest space chronologically).
According to the well-known “Johari’s Window,”
there are four regions in human mind; A: a region inwhich things are known by the self and others, B: a region in which things are known by others but not by theself, C: a region in which things are known by the self
but not by others, and D: a region in which things are158

H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

unknown by the self and others. It is considered that ina well-developed personality, A should be larger, andD should be smaller.
In which region can this system help users to understand their interests? Some comments such as “Terms
express my interest well” (Subject B) or “I can recognize what I am interested in” (Subject H) in Experiment 2 suggest that the system can help mainly in theregion that the user already knows. However, as the
subjects knew that the history would be checked by experimenters, the region in which the subjects intended
to hide their interests is unclear. The comment “I noticebiased thinking” (Subject G) suggests that the system
can help in regions that the user does not know.
In this research, we have set two hypotheses. (1) It
is possible to extract terms that express user’s interestsfrom the link texts. (2) It is possible to develop asystem to support users in understanding their interests
by presenting the terms chronologically. We think that
hypothesis (2) has been proved by two experiments.
Hypothesis (1) has been partially proved in the caseof Japanese, with some limitations. We will describe
future work below.

5. Future work
As discussed above, due to problems involved related to Web browser implementation, we were unable to conduct experiments using the system in anatural setting targeting all types of Web sites. The
practical problem revolves around the extremely timeconsuming work involved in installing the system in a
Web browser on a practical application level, an operation that falls outside of the scope of this research.
Below, we will discuss the issues that can be verified
with the framework of this research.
5.1. Resources for extracting interest terms
We have found that it is better to extract interest
terms from link texts than Web pages by using a simple
method based on the term frequency in a newspaper
Web site. However, there are other methods for extracting terms from Web pages, and we need to compare theresults. This experiment did not cover all types of Web
pages, and it is thus unclear as to whether extraction ofterms from link texts is always better than extraction
from Web pages.
The intent of this research is not to attempt to replace
existing Web browsing histories, but rather to add “in-formation extracted from link selection behavior” to existing Web browsing histories. In the future, rather thanconducting extensive investigations into which text approach is better, we intend to focus our attention on
studying methods for handling both types of text effectively.
5.2. Term extraction method
We adopted a character-based method for extracting
terms from text. In the future, in order to resolve theproblems that became apparent in Experiment 1, we
will study ways of improving upon the current method,and of combining this method with morphological analysis.
5.3. Coverage of interest terms
In the interest-space browser, we were unable to examine terms with a frequency of one, or Web pages withno interest terms. It would be possible to compensate
for this using a function for opening the history itself
making reference to the interest-space browser, but wefeel that an even better approach would be to resolve
this problem within the framework of the interest-spacebrowser method. We will pursue this as a research
theme in the future.
5.4. Display method and user-interface
There are problems involved in using an interest
space display method based on a combination of the
new history and Hayashi’s theory III, and it will be necessary to investigate improvements to the history creation method and trials for space arrangement methods
other than Hayashi’s theory III. Regarding the problemof objects being grouped together in the center, we must
examine improvement measures including appropriate
scattering methods; regarding the problem of the timerequired to display results when the volume of history
increases, we must study methods such as calculating
histories in segments.

6. Conclusions
We proposed a method that helps users to understandtheir own interests by extracting terms from selected
link texts and generating a new browsing history, andthen arranging those terms and Web-page icons in theuser’s interest space in chronological order.

H. Murakami and T. Hirata / A system for generating user’s chronological interest space from web browsing history

The features of this method are: (a) the creation ofa new history based on the user’s link selecting activities, and (b) a displaying method that arranges terms
extracted from the user’s link selecting activities, alongwith Web-page icons, in a two-dimension space, andpresents these results chronologically.
We have implemented a prototype that consists of a
new browser and an interest-space browser.
The system’s performance was evaluated in two experiments, which revealed that (1) Japanese interest
terms can be extracted from the user’s selected linktexts, (2) the interest-space browser displays the user’s
interest space on browsing the Web, and that (3) theuser can use this system to better understand their own
interests, recall their past, and reorganize previouslybrowsed Web pages.
There are areas requiring improvement in each of thealgorithms, including those for interest term extraction
methods and display methods, which will be adopted
as research themes for the future. We will also conduct
studies focusing on different information sources.




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cm
Matrix D is calculated as follows, from matrixes C
and R and the original matrix M .
D = CM T RM C(M T is a transposed matrix of M )Eigenvalues λ1 , λ2 , . . . , λm−1 for Matrix D are derived. At this time, elements with an eigenvalue of 1
are omitted.
D − λ2 E = 0 (E is an unit matrix)
Furthermore, let us assume that V is the matrix whose columns are comprised of eigenvectors
v1 , v2 , . . . , vm−1 corresponding to each eigenvalue.
Finally, category quantity X is calculated as:
X = CVand sample quantity Y is calculated as:
1
RM X
λCategory data A value that corresponds to the factor
loading for principal component analysis. It is possible
to infer by analogy the relationship of category variables using only a magnitude relation between thesevalues, but by selecting two desired category quantities and drawing a distribution graph, it is also possible to study the relationship between those two categories. Using this plot, it is possible to infer by analogy
what characteristics the category quantities themselves
extracted from the category variables.
Sample data A value that corresponds to the principal component value in principal component analysis. It is possible to infer by analogy the relationshipof category variables using only a magnitude relation
between these values, but by selecting two desired sample quantities and drawing a distribution graph, it isalso possible to grasp the relationship between those
two samples. By combining this plot with the category
quantity plot, it is possible to learn how the characteristics of the category variables are reflected in thesample.
Y =Appendix: Hayashi’s Quantification Theory III
Hayashi’s Quantification Theory III is a method forquantifying elements appearing in the form of binary
data or frequencies, sorting samples and categories, andusing this data to investigate characteristics.
Let us assume that M is a series of binary elements.

d11 d12 . . . d1m
 d21 d22 . . . d2m 
M = . . .
.  .. .. . . .. 
dn1 dn2 . . . dnm
The sums of each column c j and the sum of eachrow rj are derived. At this time, if n < m, the rows
and columns are reversed.
cj =

n


dij (j = 1, 2, . . . , m),

i=1

rj =

m


dij (i = 1, 2, . . . , n)j=1

Next, a diagonal matrix is created using (c j )−1/2 and(rj )−1 as the respective constitutional elements.

159

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