Future View: Web Navigation based on Learning User’s Browsing Patterns byClassifier Systems
Norikatsu Nagino
CISS, IGSSE, Tokyo Institute of Technology
4259 Nagatsuta, Midori
Yokohama 226-8502, Japannagino@ntt.dis.titech.ac.jp

Seiji Yamada
National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda
Tokyo 101-8430, Japanseiji@nii.ac.jp

Abstract- In this paper, we propose a Future View system that assists user’s usual Web browsing. A Future
View will prefetch Web pages based on user’s browsing strategies and present them to a user in order toassist Web browsing. To learn browsing patterns for auser, Future View uses two types of learning classifiersystems: a content-based classifier system for contentschange patterns and an action-based classifier system
for user’s action patterns. The results of learning areapplied to crawling by Web robot, and gathered Web
pages are presented to a user through a Web browser.
We experimentally show the effectiveness of navigation
using a Future View.

page contents is mainly used in those techniques. However, they are not enough for crawlers to narrow their search
spaces. They do not consider strategic search patterns. For
example, any hyperlinks followed by a user recently are notaccessed soon. Moreover, though those techniques have aneffect on assisting user’s browsing tasks for searching witha specific goal, it is difficult that they assist the user’s browsing tasks changing user’s interest in the short term. There
are also some techniques to assist users on their browsing
tasks by learning accessed Web pages sequences[6, 7, 8].
However they assist a user in a closed space on a same Web
site because they use logs on a Web server. They do notassist users on user’s usual browsing tasks in which theirinterest frequently changes and they visit various Web sites.
It is difficult to assist user’s Web browsing in a open
space because of a wide search space. It is also difficult
to learning user’s browsing patterns. Because a user visit
different Web sites and select Web pages of various topics.
Each accessed Web pages’ sequences are dissimilar as input
data for a learning component. Therefore, no useful techniques have been developed for Web navigation to assist
user’s usual browsing. However, we can make the search
space sufficiently narrow, if we consider not only similarityof Web pages but also user’s strategic search patterns.
In this paper, we propose a Future View system assists
user’s browsing tasks. A Future View learns user’s browsing patterns, and prefetches Web pages according to user’s
browsing strategies by applying learned browsing patterns
for crawling Web pages by Web robot. Web robot will identify Web pages that will be reached by a user in the near
future, and the Web pages are presented for a user through a
Web browser in order to assist user’s browsing tasks. A Future View uses a content-based learning technique to learn
user’s browsing pattern. Moreover, a Future View uses alearning technique based on user’s actions in order to takeprecedence possible Web pages accessed by a user in the
future. These two types of learning are developed with evolutionary learning method, e.g. classifier systems. Gathered
Web pages are ranked according to the possibility of accessby a user and listed in a user interface. If the results correspond to user’s interest and include objective Web pages,

1 Introduction
The World Wide Web is available to gather interesting information for a user. There are Web pages over almost all
fileds. However, finding objective Web pages is very hard
for a user because of the width of the Web, therefore empirical browsing strategies are very important for user’s efficient browsing. Search engines are often used as an available tool for the purpose of information gathering. However, the results returned from search engines may include
useless web pages for a user. Especially, if user’s objective
Web pages is not indexed in the search engine’s database, auser have to do browsing start with the results of search engines. The Web is also available for user’s browsing tasks
that a user crawl according to his/her interests. Users may
crawl to discover interesting Web pages without specificgoal. User’s browsing strategies are also important in sucha case. For example, a user may often start browsing with
Web pages including links for various topics such as Directory Services. A user may also go the round of “What’snew” Web pages which is updated frequently or some digest
news pages.
Many techniques to assist users on their browsing tasks
have been developed. For example, there are many methodsof gathering relative Web pages about some keywords[1, 2],
and recommending next links or relative Web pages for auser from the current Web page[3, 4, 5]. Information of Web

he/she can access to the Web pages directly through a userinterface. On the other hand, if the results do not correspondto interest of a user, he/she must change his/her browsing
strategy.

2 Web Navigation by a Future View
When a user browses on a open Web space, there are a
lot of accessible next Web pages for a user because a user
may follow a link or select his/her bookmarks or visit to asearch engine page. Therefore the number of Web pages
which can be reached by a user increase explosively according to steps of selecting Web pages. In a Future View,
user’s browsing patterns are learned using learning components based on Classifier Systems(CS)[9] to narrow a wide
search space. Figure 1 shows some search spaces. We consider a set of reachable Web pages by a user as a wholesearch space(S1 in Fig.1). It is narrowed by applying results of learning browsing patterns with a classifier system
based on contents of Web pages (Content-based Classifier
System:CCS). The space is showed in the section 4.1.3(S2in Fig.1). Moreover, a Future View narrows the search
space by applying results of learning browsing patterns witha classifier system based on user’s actions (Action-based
Classifier System:ACS). The space showed in the section
4.2.2(S3 in Fig.1), and it is a final search space for a Future
View to gather Web pages preferentially.
S1 : whole search space

S2 : narrowed search spaceby using CCS
S3 : narrowed search spaceby using CCS and ACS: accessed Web page
: future Web page
: other Web page
: transition between
Web pages

current Web page

STEP

Figure 1: Search Space for Future View
To assist user’s Web browsing, a Future View presents
gathered Web pages to a user. Gathered Web pages areranked according to the probability of accessing descend
and presents it to a user in order to reduce reading cost. We
think that if the order is decided with accumulation of eachtransition possibility that a user reach each Web page fromthe current Web page, Web pages that can be reached witha few steps may be presented on a position of a higher rank.
However, a user may be able to predict some Web pages inthis case. And the contents of such Web pages may be sim-

ilar, consequently it may not become interesting for a user.
Thus, a Future View presents Web pages on a higher rank
as more possibility of a transition with the last one step instead of possibility of each transitions from the current Web
pages. We called the Web pages Future Pages. A Future
View learn what the incentives are as patterns. Thereby,a Future View can grasp a set of Web pages that will bereached by a user and compose an interface to access directly for interesting Web pages.

3 Future View Architecture
Figure 2 shows an overview of a Future View. A Future
View consists of two main components. The first comWeb Browser

User
FuturePages

browsing history
Learner(based onclassifier system)

Navigation Frame
Repository

results of learning
WWW
next URL Prefetcher(Web robot)

Future View

Figure 2: System overview
ponent Learner is a learning component based on classifier systems(CS)[9]. It receives information about accessed
Web pages from a Web browser to learn user’s browsing patterns. To obtain the accessed URL, user’s actions (i.e. click
a link, input a URL directly and click the “back” button)
and another information, we use an altered Web browser“Mozilla” provided as open source. The Learner is constructed with two types of classifier systems: a Contentbased CS(CCS) and an Action-based CS(ACS). Figure 3
shows an internal architecture of a Learner. A CCS learns
user’s browsing patterns based on contents of accessed Web
pages. On the other hand, a ACS learns user’s browsing
patterns based on user’s actions. Accessed Web page information is transformed to a value whether it is included ina “Page Class”. A Page Class is abstract representation of
Web pages set based on various features of its. A condition
part and an action part of classifier consist of values for Page
Classes. We call page classes for a CCS “Content-based
Page Class(CPC)” and page classes for an ACS “Actionbased Page Class(APC)”. We show detail of its in the section 4.1.2 and 4.2.1. The second component is a Prefetcher.
Our crawler prefetches Web pages referring the results oflearning classifiers. Its starting point of crawling is the current Web page. A crawler stores the searching states at the
point in time with information of Web pages. And in orderp-6Content-based
Classifier System (CCS)
Content-based Classifiers
CCC
P1 P2 . . . Pn

# 1 0 # 0 : 8 1.2
0 # 1 1 # : 4 2.8
Condition

Action-based
Classifier System (ACS)
Action-based Classifiers
A

A

A

A

P5 P2 P9 # # : P6 1.3
AAA
A
P4 P3 # # P7 : P1 4.8

Condition

Action Strength

step

Browsing History

message (browsing history)

Action Strength

p-5

p-4

p -3

*

pi : accessed page

p-2
*

p-1

p0

current session: browsing session* : over 25.5 min. or clicked "New Session" button.

Figure 4: Browsing Session

AC

Pi : Content-based Page Class (CPC) Pj : Action-based Page Class (APC)

Learner

Figure 3: Architecture of a Learnerto prefetch more depth Web pages, prefetched Web pages
are considered as a part of browsing history. Gathered Web
pages are always displayed as Future pages in the Navigation Frame with a function of a Web browser “Sidebar”,
while browsing.

4 Learning User’s Browsing Patterns
4.1 Learning Browsing Patterns based on Web Page
Contents
4.1.1 Browsing Session
User’s interests may be changed under the influence of various knowledge acquired during his/her browsing tasks. For
example, it occurs at the case that a user have obtained objective knowledge from accessed Web pages, or the casethat a user doesn’t reach objective Web pages at all, or
the case that a user is unexpectedly interested in glimpsed
texts on the other topics. Users surf on various topics togather relative information, and all accessed sequences arerecorded in a repository. Therefore user’s browsing history
consists of some browsing sessions. The “browsing session” means a browsing sequence on a single topic, whether
a user access to related Web pages from many links pages
or using search engines. It is worthy of notice that the relevance between accessed topics before changing user’s interests and after is little. Especially, in a content-based learning, it may influence on effectiveness of learning.
Therefore we notice an end of a session of Web browsing for learning browsing patterns with a CCS. For detecting sessions, we consider all events that occurred over 25.5
minutes apart to be a new session[10]. We also allow a user
to explicitly indicate the end of a session by clicking the“New Session” button. Figure 4 shows browsing sessions
for a Future View.

4.1.2 Design of CPCs
In this section, we show the way of designing CPCs. Whena user browses on a topic, the topic can be characterizedwith appeared words in Web pages on the topic. We can define CPCs based on some viewpoints below using Browsing
Session in order to distinguish the type of words aimed by auser.
• topic continuity– Web pages which include top n words of high
TFIDF values in the title or body or anchor text.
∗ The TFIDF[11] values here for all words
in the current document are calculated withthe other documents accessed before thecurrent session(Da in Fig.5).
– Web pages accessed by following links of whichanchor text include keywords input for searchengines in the current session.
• content difference on a topic– Web pages which include more words with high
TFIDF value and not included the other Web
pages in the same session.
∗ The TFIDF values here for each word in
the current session are calculated with only
documents in the current session(Db in
Fig.5).
– Web pages which the very similar Web pages
didn’t appear in the current session.
4.1.3 Learning with a CCS
Each classifier consists of CPCs for a CCS are represented
as below.
classifier = condition-part : action-partcondition-part = C1C , . . . , CnC = {1, 0, #}naction-part = AC = {P1C , . . . , PnC }A CiC means a i-th component of a condition-part. When a
CPC corresponding to a i-th component is represented as a

PiC and the current Web page is represented as pcurr , eachcomponent CiC means the following.
• If CiC = 1, then a pcurr is included in a page class
PiC .

4.2.2 Learning with an ACS
A Future View uses an ACS to learn user’s browsing patterns based on user’s actions. Each classifier consist of
APCs for a ACS are represented as below.

• If CiC = 0, then a pcurr is not included in a page class
PiC .

classifier = condition-part : action-part• If CiC = #, then whether a pcurr is included in apage class PiC or not.

A
action-part = AA = {P1A , . . . , Pm}

Here, AC means a CPC which the next Web page pnext will
be included in . Whenever a user visit a new Web page,a set of values for matching is constructed with a message(browsing history) (Fig.5) and it is compared with a condition part of a classifier for matching.
step

Browsing History (message for CCS)

p-6

p-5

p-4

p-3

p-2

p-1

Da

A
, #}n
condition-part = C1A , . . . , CnA = {P1A , . . . , Pm

Here, i means the order of access to a Web page, and CiAmeans a APC in which the i-th Web page should be included. The current Web page is included in an APC of CnA ,and a Web page accessed at the previous step is included inA. When the current Web page is represented
an APC of Cn−1
as pcurr , each component CiA means the following.
• If CiA = PjA , then a pcurr is included in a page class
PjA .

p0

• If CiA = #, then whether a pcurr is included in any
page class or not.

Db
P1

C

P2

C

P3

C

...

Pn

C

values for matching : 1

1

0

...

0

Figure 5: Matching values for CCS
A CCS performs updating strength of each classifier using the Bucket-Brigade algorithm in the reinforcement component and GA in the discovery component like standard
classifier systems.

Here, AA means a APC which the next Web page pnext will
be included in . Whenever a user visits a new Web page, a
set of values for matching is constructed with a message(browsing history) (Fig.6) and it is compared with a condition part of a classifier for matching.
Browsing History (message for ACS)

...

p-i
A

values for matching : . . . P5

step

...

p-1

p0

...

P2

A

P7

A

4.2 Learning Browsing Patterns based on User’s Actions
4.2.1 Design of APCs
In this section, we show the way of designing APCs. An
APC represents a set of Web pages based on user’s actions
used in an ACS, and the classifiers consists of the APCs represent a user’s characteristic browsing pattern. We provide
a guideline for defining APCs and samples of APCs below.
• interests to news
– Web pages accessed by following new added
links.
• strategic search– Search engine’s top page accessed by directly
inputting the URL to a browser.
– Web pages which accessed by following a link
near by the previous followed link and were notfollowed today yet.

Figure 6: Matching values for ACS
An ACS updates strength of each classifier based on the
Bucket-Brigade algorithm in the reinforcement component
and GA in the discovery component like standard classifiersystems in the same way to an CCS. In addition, we use thefollowing techniques for learning efficiency.
• Partial Matching[12]A Future View performs partial matching sequence
instead of exact matching.
• Using page classes and instances for classifiers
A Future View uses not only page classes but also
URLs of Web pages as instances. If components
in condition-part of classifiers are instances, when
URLs of instances correspond to URLs of a message,
the classifier will be matched. A classifier including
more instances are given priority to be fired.

• Cover (detector) operator[13]In a discovery component of an ACS, new classifiers
are created a matching classifiers out of the current
message when there is no classifiers match the current
message.

5 Prefetching Future Pages
A prefetcher gathers Web pages as Future Pages start withthe user’s current Web page applying learned browsing patterns, and stores them to a repository. A Future View gather
Future Pages parallel with learning browsing patterns. A
prefetcher search Future Pages with a similar standard bestfirst search algorithm with a evaluation function using values of the last applied classifier’s strength. The table 1
shows a procedure of gathering Future Pages.

6 User Interface
Future Pages prefetched by a prefetcher are presented toa user through a browser. Fig.7 show a user interface. A
left upper frame display URLs as a browsing history andthe “New Session” button to split browsing sessions. A left
lower frame indicates a list of Future Pages with a title, a
URL string, a thumbnail image of the Web page, a button
to display a trail from the current Web page and a part ofcontents of the Web page. A user can access the Web pageby clicking a URL link.

7 Experiments
In order to investigate effect of a Future View, we evaluate
the results of using a Future view by 8 users. They are
not researchers, and don’t have knowledge of evolutionary
computation and any other areas. After they used a Future
View for three days, they evaluated many Future Pages. We
show Future Pages which are presented by a Future View
and the results of evaluating qualities of presented Future
Pages by a user.
We use the settings showed in Table.2 for CCS and ACS.
7.1 Example of Future Pages
We show a few typical learned patterns and presented Future Pages.
• “come and go” pattern
Each user have used a search engine sometimes. After they have inputed query keywords for a search engine, they have come and gone the list page including the results of search query and the next pages followed any links.
Now a user visited Web pages P1 , P2 , P3 in Fig. 8,
and now reading the Web page P3 . Then Web robot

1. Initializing: Let p0 is a current Web page, s0 is
a browsing sequence to the current Web page, theinitial value for the current trail g0 = 0, the estimation value h0 = θstrength + α and initialize asearch queue list as L = [(p0 , s0 , g0 , h0 )].
2. Selecting the best page: Pick up an element withthe highest heuristic value fi = gi + hi in a search
queue list L, and delete it from a L. We represent
the picked up element (pi , si , gi , hi ). And the element into an opened list OL.
3. Detecting a set of applicable classifiers: For CCS
and ACS, a mesage from a browsing sequence simatch classifiers, and a match set is made. A
prefetcher detects all classifiers from each match
set as a set of applicable classifiers which have astrength value more than threshold θstrength andare applicable to the current state.
4. Prefetching Web pages: A prefetcher obtains all
Web pages as the results of applying each action of applicable classifiers. Each element e =(pi+1 , si+1 , gi+1 , hi+1 ) is made for an obtained
Web page. Here, pt+1 is a obtained Web page,gi+1 = fi = gi + hi is a value for each trail, hi+1
is the larger of the strength values of last applied
CPC and APC, and a si+1 is a new browsing sequence that a page pi+1 is added into a previous
browsing sequence si . All elements are added into
a search queue list L.
5. The termination condition: If a search queue become empty (L = [ ]) or the number of opened elements in OL reach the max search numbers, thisprocedure is terminated, otherwise return to 2.
Table 1: Procedure of gathering Future Pages
visited Web pages P2 , P4 , P2 , P5 , P2 , and Web Pages
P4 , P5 are presented as Future Pages.
In this pattern, CCS tended to have brought the goodresult. For example, Web pages P4 , P5 actually related to search keywords. Some advertisement links
were excluded.
• “daily fixed” pattern
A user usually browsing start with his bookmarks
page as a hub page. The page include some old links,
and he don’t select the links recently. If he visit his
bookmarks page at first of the day, Web robot follows
valid links excepting old links.
In this pattern, ACS tended to have brought the goodFigure 7: User Interface
result. Because fixed urls of Web pages and the ordersof visiting have been learned by ACS.

P1 : top page of a search engine
input keywords

P2 : search result page

follow a linkfollow afollow: user's action: Web robot's action: Web page

back
link

b
a link ack
back

P3 : linked page

We also show an interesting pattern. A user has performed the following browsing occasionally.
• He has used a search engine sometimes.

P4 : linked pageP5 : linked page

Figure 8: “come and go” pattern• After he has inputed words to a search engine, he has
selected some results from the search engine one by
one from the top. (So far, he has perform browsing
same as “come and go” pattern.)• After he has selected some results from the search engine, he has has accessed to a News site which havebeen often used.

settings of CCS and ACS
Population size of a set of classifiers: 500
Length of a message: 10
The number of CPCs: 10
The number of APCs: 12
Frequency of running GA: 1 time per 30 step
Selection algorithm: Elite
Modifying strengths of classifiers: Backet Brigade
Initial strength values: 1.0
Limited maximum strength values: 4.0
Max. depth of searching(prefetching): 7
Max. number of gathering distinct Future Pages: 10: user's action: Web robot's action: Web pageP1 : top page of a search engine
input keywords

P2 : search result page

follow a linkfollow afollow

P3 : linked page

back
link

b
a link ack

back

Pk : top page of a New site

follow a link

Pi : linked page
Pj : linked page
Pl : linked page

follow a backlink

Table 2: Settings for CCS and ACS
Figure 10: Example of interesting pattern• He has selected some new articles.
A user visited a Web page about “Linux OS”, and he broke
off the current session by pushing “New Session” button,
and he accessed to the top page of search engine “google”.
Now he have just selected some results from search engine and back to the results pages of search engine(P2 in
Fig. 8 and Fig.7). Figure 9 shows the accessed list of him.
Then, Web robot visited Web pages Pj , P2 , Pk , Pl , Pk (Fig.

Figure 9: Example of browsing history8), and Web Pages Pk , P2 , Pl , Pj are presented as Future
Pages(Fig. 11).
7.2 Qualities of Future Pages
We investigated qualities of Future Pages in order to confirm effectiveness compared with the other systems; there
are not appropriately comparable systems considered theopen search space, however. Therefore we compare Future
Pages with Web pages searched by using “random walk”.
And we also investigate the difference of effects between an
ACS and a CCS.
In order to investigate the different effects between an
ACS,a CCS and “random walk”, 8 subjects evaluated up to

10 Future Pages(if there is a few Web pages for presenting the user, the number of Future Pages may less than 10
pages) for 5 current Web pages at each case that a prefetcher
is applied the results of learning of “ACS and CCS”, “onlyACS”, “only CCS” and “random walk” for the comparison(150 or less Future Pages for a subject in total). Here,“random walk” is that Web robot select a link from all oflinks in followed Web pages at random. In addition, “random walk” exclude some useless links for pretreatment.
Each Future Pages is evaluated with 3–1 points: 3 forinteresting Web pages, 1 for indifferent Web pages and 2
for intermediate value. The Table3 shows the average values of the total values of evaluations for 8 subjects. We
also compare the results for two types of user’s browsing,with the specific goal(searching) or without the specificgoal(browsing).

CCS and ACS
CCS only
ACS only
random walkbrowsing
0.99 (6.2)
0.27 (3.3)
0.58 (5.6)
0.54 (5.2)

searching
1.0 (7.3)
0.39 (2.9)
0.56 (6.7)
0.51 (6.9)m(n): m is the average values of the total values of evaluations for
8 subjects, and it is normalized by the value for ec,a for browsing.
n is the average value of the number of presented pages.
We compare the evaluation value with gathering Future Pages withboth CCS and ACS, only CCS, only ACS and “random walk” forbrowsing and searching.

Table 3: Evaluation of Future Pages
In the results, CCS tended to have brought the highevaluation value for searching than for browsing. On theother hand, ACS tended to have brought the high evlauation
value for browsing than for searching. Moreover, when weeffectiveness of presented Future Pages by a Future View.

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