Copyright is held by the author/owner(s).
WWW2002, May 7-11, 2002, Honolulu, Hawaii, USA.
Web Montage: A Dynamic Personalized Start Page
|Corin R. Anderson
|University of Washington
|Seattle, WA, USA
||Redmond, WA, USA|
Despite the connotation of the words ``browsing'' and
``surfing,'' web usage often follows routine patterns of access.
However, few mechanisms exist to assist users with these routine
tasks; bookmarks or portal sites must be maintained manually and
are insensitive to the user's browsing context. To fill this
void, we designed and implemented the MONTAGE system. A web
montage is an ensemble of links and content fused into a single
view. Such a coalesced view can be presented to the user
whenever he or she opens the browser or returns to the start
page. We pose a number of hypotheses about how users would
interact with such a system, and test these hypotheses with a
fielded user study. Our findings support some design decisions,
such as using browsing context to tailor the montage, raise
questions about others, and point the way toward future work.
Personalization, user modeling, adaptive user
interfaces, adaptive web sites
Despite the exploratory ring of the terms ``browsing'' and
``surfing,'' web usage often follows routine patterns of access.
For example, a graduate student might read a web-based newspaper
the first thing in the morning, then spend a few hours on
software development, with intermittent consultation of online
programming documentation. Following a break at noon for lunch
and to read comics on the web, the student might return to
programming, then take a mid-afternoon break to check news and a
few more comics, and finally consult online transit information
shortly before leaving at 5:30 P.M. Such stereotypical patterns
of web access are common. However, despite the regularity with
which users view content, few mechanisms exist to assist with
these routine tasks. Lists of bookmarks must be authored and
maintained manually by users and are presented in a cumbersome
hierarchical menu. Links and content on personalized portals,
such as MSN.com  or MyYahoo! , are more
easily navigable, but still must be chosen and managed by users
in an explicit manner.
The challenge that we tackled--and in turn share with the web
research community--is to develop tools that assist users with
routine web browsing. Routine web browsing refers to
patterns of web content access that users tend to repeat on a
relatively regular and predictable basis (for example, pages
viewed at about the same time each day, or in the same sequence,
or when working on the same task, etc.). In developing a response
to this challenge, we pose the following three hypotheses:
To test these hypotheses, we designed and implemented the
MONTAGE system. MONTAGE builds personalized web portals for
its users, based on models mined from users' web usage patterns.
These portals both link to web resources as well as embed content
from distal pages (thus producing a montage of web content;
see Figure 1). MONTAGE melds concepts
from research on predictive user modeling (for web content
or adaptive web
sites [1,15]) with the user
interface approaches of automatic
bookmarking [10,11] or web portal
sites [12,17]. After completing the MONTAGE prototype, we fielded the system to evaluate its effectiveness as
a tool and to explore the validity of our hypotheses.
evaluating the hypotheses, we explored methods and constructed a
prototype that make the following contributions:
- Hypothesis 1: Users want ``one-button access'' to their
routine web destinations (i.e., users want to minimize their
effort in retrieving and viewing content);
- Hypothesis 2: Tools for routine web
browsing are enhanced by tailoring links and views to a user's
current browsing context (as opposed to displaying a static set of
content under all circumstances); and
- Hypothesis 3: Past web access patterns can be successfully
mined to predict future routine web browsing.
In the next section, we present the overall challenge in more
detail, followed by an outline of MONTAGE in
Section 3. We discuss implementation specifics
of MONTAGE in Section 4 and present our
experimental findings in Section 5.
Section 6 compares MONTAGE to related work and
Section 7 concludes with a brief summary.
- Multiattribute utility model for web information access. We
leverage a utility model for prioritizing web content
that considers both savings in navigation cost and informational
- Enriched informational context for user modeling. We demonstrate
learning and use of predictive models that leverage a notion of
context captured by a rich set of observations, including evidence
about the time of day, the time since last access, and the recent
pattern of topics of information at a user's focus of attention.
- Automated segmentation and structuring by topic. We
harness a real-time topic classifier to segment and
organize content within and across personalized portal pages,
providing users with views on personalized information that is
structured by a topic ontology.
- Persistent configurable information lenses. We show how a system can
compose personal portal pages by assembling user-configurable
clippings from parent pages that provide persistent views or
``lenses" onto the parent pages.
- Layout of content based on expected utility. We use an
expected-utility search procedure to both select the best subsets
of content to display and to dynamically position this content on
personal portal pages.
- Construction and validation of MONTAGE personalized portal
system. We weave together multiple components to build a working
MONTAGE prototype system that runs on a proxy or on a client,
and we perform a user study to explore the validity of several
As we noted earlier, not all web usage is random or novel; web
users also tend to revisit sites and pages in a regular,
predictable manner. In the example of the graduate student
presented at the beginning of the paper, the pages the student
viewed depended entirely on his current context, where
context is taken as the time of day and the general topic of the
pages viewed previously. More generally, we define the context
of a web browsing session as the set of attributes that influences
(either consciously or subconsciously) the selection of pages a user
views in the next session. Many factors can be included in a
formalization of context. For instance, the context can include
the time of day, the period of time elapsed since the last
session, the general topic of the last session, the most recent
non-browsing computer activity (e.g., the most recently viewed
e-mail message), etc. We define routine web browsing as the
overall pattern of content access that a user performs whenever
in the same or similar contexts. For example, if a user
reviews a stock portfolio at around 1:15 P.M. every day, then
viewing the stock portfolio is a routine behavior--it happens at
about the same time each day. On the other hand, if one day the user
spends an hour searching for information about fishing in
the Pacific Northwest, then this behavior is not routine because
the user does not repeat it in a similar context.
Our intuition strongly suggests that routine browsing is a common
mode for interacting with the web; our informal surveys early in
our study suggested that many people tend to view the same page
or constellation of pages when in similar contexts. The more
important questions that must be answered are whether we can
formulate good notions of context, identify the routine browsing
associated with each context, and leverage this information to
assist the user. These questions are embodied in the hypotheses
we posed in the introduction and are answered with our
experiments with the MONTAGE system.
2. Routine browsing
A web montage is a page that offers ``one-stop shopping'' for
users to find the information they want. A montage combines
content from many different pages, linking to pages or embedding
distal content, saving the user the need to follow even a single
link to view content. Three typical montage views are shown in
Figures 1, 2,
and 3. In Figure 1
is the ``Main Montage'' which displays links and embedded content
grouped by topic. This particular montage has three
topic-specific panes: ``Society, Politics, & News,'' ``Computers
& Internet,'' and ``Entertainment & Media,'' each containing a
cropped view of a distal web page and links to other pages within
the respective topic. Thus, the user can immediately view the
afternoon's current news, the user's most frequently-viewed
programming documents, and the current traffic conditions around
the area. In Figure 2 is a topic-specific
montage, which shows several embedded pages and related links.
Both the topic-specific montage and the main montage are assembled
automatically to fit just within the user's current browser
window, to eliminate the need to scroll the page. The user can
navigate between these montages by following links at the top of
each view. Figure 3 shows a
simplified montage that contains only links. Still other
visualizations are possible: a browser toolbar showing iconic
representations of pages; a persistent display of bookmarks
auxiliary to the browser window; etc. We plan to explore
these alternatives in future work.
3. The Montage system
Main montage. Content is grouped
by topic, and each topic heading links to a topic-specific montage.
A topic-specific montage. This montage embeds
content from several pages.
Building a web montage is a two-step process, similar to that
used in PROTEUS . In the first step,
the MONTAGE system collects and mines web access logs for each
user. From these logs, MONTAGE (a) selects candidate pages to
link or embed on the montage; and (b) builds a model of the
user's navigation patterns and browsing interests. In the second
step, MONTAGE uses this model to calculate the expected
utility  of displaying each candidate page
to the user, and then assembles a montage of the highest-scoring
ones. We detail these two steps next.
A links-only montage.
3.1 Step 1: Mine the user model
The primary source of information for the user model is the
sequence of pages the user requested. MONTAGE records the time
and date of each page visited, its URL, and the topic of the
page's content. The topics can be determined using text
We applied a content classifier trained to assign topics to web
content, developed by Chen and Dumais . The topic
classifier employs the linear support vector machine (SVM) method
each page a probability of being in each category of a static
topic ontology1. We took as the topic of a page the most likely
The result of evidence collection is a sequence of requests tagged
by topic that MONTAGE further refines into sessions. For
MONTAGE's purposes, a session is a sequence of page requests that
begins with a visit to the user's start page--the first
page the browser displays, or the page visited when the user
clicks the ``home'' button. In practice, MONTAGE does not know
which page is the start page and, thus, uses heuristics to
identify when one session ends and when the next session begins.
Section 4 provides the exact details on how
we clean and segment the data into sessions.
MONTAGE uses the page sequences and sessions to compute five
aspects about the user for the model:
- Candidate pages. MONTAGE selects a subset of the
user's previously visited pages as candidates for inclusion in
the montage. MONTAGE places no upper limit on the number of
pages selected, but does set minimum requirements for inclusion
in this set (such as a minimum number of times the user has ever
viewed the page).
- Interest in page. MONTAGE estimates the user's
apparent interest in each page, primarily by how much time the
user spent looking at the page, how many links the user followed
from the page, etc.
- Interest in topic. MONTAGE also estimates the
user's interest in the higher-level topic of pages viewed.
Because, for instance, although the user may show relatively
little of interest in several different pages, he or she may be
strongly interested in the single topic encompassing them all.
- Probability of revisit. MONTAGE estimates the
probability that a user will revisit a page in the next browsing
session, given the user's current context.
- Savings possible. Placing a link or embedding a page
on the montage saves the user navigational effort. MONTAGE measures this savings as the effort the user expended navigating
to the page originally. All things being equal, MONTAGE will
favor including pages on the montage that would be difficult to
3.2 Step 2: Assemble the montage
Equipped with the user model, MONTAGE is ready to assemble the
content montage. Because the montage depends on the user's
current browsing context, MONTAGE builds a new page each time
the user revisits his or her montage. MONTAGE begins the
assembly by calculating the overall expected utility of viewing
each content topic or candidate page. We approximate the value
of the page p to a user as a function of the interest,
I(p), and the navigation savings, S(p). In
the general case, the
value is some combination of these two factors: f(I(p),S(p)).
We treat these factors as independent and have explored both a
weighted additive model and a weighted multiplicative model. In
our experiment, we chose the multiplicative model, and took as
the value of a page,
If we assume
the cost of displaying uninteresting content to be
zero, then the expected utility of a page is the product of the
probability the user will visit the page given the current
context, Pr(p | C), and the value of the page. Thus, we
take as the expected utility of a page:
E[U(p)] = Pr(p|C)(I(p)k1 * S(p)k2
Likewise, we compute the expected utility of a topic T
as the product of the probability that the user
will view any page with topic T in the current
context, Pr(T | C), and the user's interest
in the topic:
Pr(T | C) I(T)
MONTAGE uses these values to place content on the montage to
maximize the total expected utility, subject to the sizes of the
browser window and each of the embedded pages. Effectively,
MONTAGE solves a knapsack problem where the knapsack is the
browser window area and each item is a candidate page or topic
with associated size and utility. In practice, we could provide
users with tools to inspect and tune measures of interest
and navigation savings, and allow users to provide feedback on
the function for combining these factors. For example, for the
multiplicative model, we could assess from users the relative
weighting ascribed to interest versus navigation savings.
For our studies, we considered these factors to be equal and
provided fixed functions for interest and savings. In
Section 4 we describe how MONTAGE mines
the interest, savings, and probabilities from the web usage logs.
3.3 User control of montage clippings
In the previous section, we saw that the content embedded on the
montage was cropped to a web page clipping, a smaller
window than the original page (see Figure 4).
MONTAGE allows the user to have complete control over the size
and position on the distal page of this clipping. By specifying
the length, width, and focal point of the clipping, users create
persistent lenses onto particular portions of the content of
pages. Users can also dictate the frequency with which the
content refreshes itself during the day (i.e., if the user simply
leaves his or her browser at the montage, MONTAGE will
automatically refresh the embedded content with the given
Cropping content. Distal pages are cropped to a
small window when embedded on the montage.
The implementation of MONTAGE follows the framework we
presented in the previous section; in this section we describe
the details of the actual implementation. MONTAGE was coded in
Python and runs on both Windows and Linux platforms using
Internet Information Services (IIS) or Apache web servers.
4.1 Collecting data and mining models
Users of the MONTAGE system direct all of their web browsing
through a proxy that logs each request. In our experiments, we
used a single proxy running on a central server, although the
MONTAGE framework supports running the proxy on individual
users' computers. An important advantage of the individual proxy
installation is user privacy--if the proxy and the rest of the
MONTAGE system all operate on the user's computer, then the
user minimizes the risk of unintentionally sharing private
information with third parties. We chose the centralized proxy
approach for convenience of the experiment.
Before we mine the usage information, we must clean the logged data.
First, MONTAGE removes all requests for embedded web content
(such as requests for images embedded on pages, or frames in
framesets) by parsing the HTML of every page requested and
identifying which URLs are embedded. Second, MONTAGE removes
repeated requests for pages that automatically refresh (e.g.,
cnn.com automatically refreshes every 30 minutes).
MONTAGE computes the statistical mode of the revisit interval for each URL
and, if at least 10% of the intervals belong to the mode,
MONTAGE removes any requests that are made within a small
tolerance of the mode. Thus, MONTAGE effectively removes the
second, third, fourth, etc. request for a page, but leaves the
first request (the actual visit the user made) intact. Finally,
MONTAGE segments the usage data into sessions, placing in the same
session all requests made by following links from other requests
in the session within a fixed time window (10 minutes) of the
With the proxy logs cleaned and sessionized, MONTAGE proceeds
to select the candidate pages and topics. Any page or topic that
has been visited more than once is a candidate. For each
candidate, MONTAGE builds a naïve Bayes classifier to estimate
the probability the user will view the page in a future context.
The model classifies a session as to whether the user will view
the page or topic in that session. The particular evidential
features employed by this model are: the overall rate with which
the user views the page; the rate of viewing the page for each
3-hour block of time in the day (i.e., midnight - 3:00 A.M., 3:00
A.M. - 6:00 A.M., etc.); and the predominant topic of the pages
viewed during the last 4-hour block of time. We evaluated many
other features, such as the time since the previous session and
the last URL visited, but these other features either offered
less lift in predictability or required more training data than
we had available.
The final aspects of the user model are the savings possible when
embedding a page on the montage and the user's interest in a page
or topic. We estimated the savings as the average number of links
followed to reach the candidate page from the first page in each
session the candidate appears. The user's interest in a page is
estimated heuristically as a weighted sum of the average number of
links followed from the page, L(p), and the
average number of seconds spent in sessions starting with the page, D(p):
I(p) = L(p)*0.50 + D(p)*0.03
The constants were chosen to equate an average of two links
followed from p with an average session time length of 30
seconds. The user's interest in a topic T is the sum
of interest over all pages whose topic is T:
I(T) = SUMp in
4.2 Displaying montages
As the browsing context is potentially different each time the
user requests his or her montage, the montage may be rebuilt
frequently. Our implementation requires only a few seconds to
rebuild a montage with the time dominated by solving the knapsack
problem to place embedded content and topic panes in the browser
window. We are confident we could improve this time by an order
of magnitude with judicious optimization. In our experiments, we
rebuilt and cached test subjects' montages only once per hour,
both for convenience, and because the set of features we chose
for browsing context do not change any faster than about once per
As we described previously, we developed two different
visualizations for a montage: the embedded-content montage
(Figure 1) and the links-only montage
(see Figure 3). The links-only montage is
quite simple to display: it is a two-dimensional table and
contains only links to web sites. The link anchors are chosen
as either the target page's
lacking a title, the URL itself.
The embedded-content montage is a bit more complex. It is formed
as a set of nested
<frame>s: the navigation bar, each
topic pane, and the content panes within each topic pane, are all
<frame>s. The hosting
<frameset>s specify the
size of each pane; it is this mechanism that also sets the size
of the cropping window for distal content. To scroll the content
to the appropriate position on the distal page, MONTAGE sets
src of the frame to be the corresponding URL and
additionally adorns the URL with a tag the MONTAGE proxy
intercepts (recall, the user directs all browsing through the
proxy, including requests made for content embedded on the
montage). The MONTAGE proxy passes the request along to the
appropriate server (removing the adornment) and inserts a
back to the user. The proxy makes no other changes to the
its appropriate position as it is loaded by the browser. We note
that an alternative approach would be to pass the URL directly to
MONTAGE and have it fetch the page and modify the content
itself--no need for adorning URLs or intercepting requests with
the proxy. However, because the URL the browser would see is a
MONTAGE page, rather than the actual target site, the browser
will not communicate any cookies to the remote server. Thus, to
ensure the browser believes it really is communicating
directly with the remote site, we chose the URL adornment
The user may change the size and position of the cropping window
on distal content by clicking the ``Customize size & focus''
link in the upper-left corner of any content pane. In response,
MONTAGE opens a new browser window as shown in
Figure 5. The user can control three aspects of
how the content is displayed in the montage. First, the user can
directly change the size and position of the clipping window
simply be changing the size and scroll position of the browser
window. Drag the window larger, and the clipping window becomes
larger. Second, the user can control how text flows on the page
by specifying the width and height (in the text fields in the
figure) of the virtual browser window the page is rendered
in. For example, if the user wants to crop the content very
narrowly, he or she could specify the virtual browser be only
width 400 (pixels) for that page; the page would then be
formatted very narrowly.2 Finally, the user can control how often each
clipping reloads itself in the browser window by setting the
period, in seconds; zero seconds disables
auto-refreshing. By default, MONTAGE sets this value to zero
(refreshing disabled), although MONTAGE could suggest a refresh
interval based on the user's past revisit frequency to each page.
Customizing embedded content. The user can change
the cropping window on the distal page by simply resizing and
scrolling the browser window.
In the introduction we presented three hypotheses about users'
routine browsing behavior. A key step in answering these
questions is actually fielding MONTAGE among users. We
conducted a two-week user study of 26 web-savvy users during
early October 2001. During the study, users directed all their
web browsing through a central proxy running on our ``MONTAGE server'' (which ran the proxy, a web server, and the MONTAGE implementation). In the first week, we only collected usage
information--users browsed the web as they normally would
browse. At the end of the first week, we began building models
of each user, once per day, to use with MONTAGE. Recall that,
although the user's browsing context changes relatively
frequently, the predictive model for the user does not--a single
additional hour or half-day of browsing rarely changes the model
During the second week, we presented users their montages. We
instructed users to make their montage their browser's start
page, and to revisit the page at least a few times each day. We
also added an additional pane to the montage to elicit feedback
whenever the user viewed the page (a simple rating reflecting how
``pleased'' the user was with that montage, ranging from 1
meaning ``Not pleased at all'' up to 7, ``Very pleased'').
During this period, 21 of our 26 users actually viewed their
montages, on average 25.1 times in the week, and provided
feedback 28% of the time. We concluded the study with a
To explore the validity of our hypotheses from
section 1, we tested two variables
influencing the montage. First, we varied the montage
visualization approach: embedded-content or links-only. Second,
we varied the model complexity: the expected-utility,
context-sensitive model (``complex model'') as described in
section 4, or a simple model using only
the overall frequency of revisits to determine the probability of
returning to the page (i.e., ignoring interest in page and
savings). We presented every user in the study with two different
styles of montage, drawn from the four total configurations; one
style in the first half of the second week and the second style
in the second half. We particularly sought feedback on the
embedded-content / complex model style, so each user viewed that
montage, and one other style. Table 1 shows
our resulting study groups.
5. Experimental evaluation
Study groups. Each group also viewed the
embedded-content / complex model montage. We count a participant
as active if he or she viewed and rated at least one montage
during the study.
5.1 General results
Table 2 shows the average feedback score
for each of the four montage styles. Recall that a higher score
means the user was more pleased with his or her montage.
Table 3 shows the scores comparing
only one variable at a time, and
Table 4 displays each study group's
score for their respective styles. Overall, it's clear that the
links-only montage using the model that incorporates context is
the favorite. We shall next analyze how these findings apply to
Feedback scores. Users rated their montages
between 1 (``Not pleased at all'') and 7 (``Very pleased'').
Results by variable.
Results by study group. Each study group viewed two
different montage styles, switching half-way through the second
week of the experiment.
5.2 Hypothesis 1
Users want ``one-button access'' to their routine web
As a result of our study, users could access their routine
destinations in four different ways: follow a bookmark; follow a
link on the links toolbar (a small toolbar that displays only
four to six bookmarks); follow a link on a links-only montage;
and view content or follow a link on the embedded-content
montage. Although we did not directly compare the montage
approach against bookmarks and the links toolbar, users'
qualitative feedback indicate that they value the montage higher
than the manual approaches. Some users suggested a hybrid, where
they can manually add links to the automatically-generated
montage. We are considering such an extension to our system.
Between the two visualization approaches, we were surprised to find
that the links-only montage was unanimously preferred over the
embedded-content montage (see Tables 2
and 3). A priori, we had
expected users to prefer their target content embedded directly on
their montages. Instead, users apparently don't mind following at
least one link to view their destination.
We believe that several factors may have influenced users'
opinions on this issue. The links-only montage loaded nearly
instantly as it is only formatted text. The embedded-content
montage, however, would take up to 30 seconds to load completely
(downloading all the distal content embedded on the page). It's
not clear how much of this delay was caused by our particular
experimental setup (e.g., was the proxy a bottleneck?) and how
much delay is inevitable (e.g., network delays). We plan to
investigate this issue in a future study by prefetching the
content embedded on the montage. We also found that the
potential full value of the embedded-content montage was hard to
attain with the limited screen real-estate associated with
typical screen resolutions. The default size of the web clippings
and the limited browser area allowed only one or two clippings to
fit in a montage unless the user customized the clipping size. We
plan to investigate other means for providing greater numbers of
content clippings, including the approach of rendering
scaled-down views of portions of pages.
5.3 Hypothesis 2
Tools for routine web browsing are enhanced by tailoring
links and views to a user's current browsing context
The test for this hypothesis is the comparison between our
complex model and the simple one. The complex model conditions
the links and content on the montage on the user's current
context, while the simple model ignores context. The latter half
of Table 3 shows this comparison: the
model conditioned on context outscored the simple model by well
over a point. Of course, this result only scratches the
surface--context is clearly useful, but what context
should we use? In a future study, we plan to evaluate how much
predictive lift each feature offers to determine the most
valuable set of features for the user model.
5.4 Hypothesis 3
Past web access patterns can be successfully mined to
predict future routine web browsing
The proof of this hypothesis lies in how often users found their
target content on or using their montage. Based on a post-study
survey, many users agreed that they found the montage helpful in
suggesting where they truly wanted to visit. However, there were
several cases in which MONTAGE suggested pages that were
clearly never going to be revisited. In particular, MONTAGE tends to suggest search engine results pages to users, as
MONTAGE estimates the user's interest in these pages highly
(MONTAGE estimates interest in part as the number of links
followed from the page). We plan to refine MONTAGE's interest
estimate in a future implementation and test this hypothesis
Quantitatively, our study showed that 73% of users felt that
MONTAGE selected appropriate links and content at least
occasionally. We feel that this result supports our initial
approach to mining routine web browsing. Moreover, we can improve
this value in two ways. First, due to a technical issue,
MONTAGE does not suggest intranet content; omitting these pages
accounted for 18% of the sessions in which MONTAGE failed to
suggest the correct target. With the experience of the user
study, we are confident that we can overcome this technical detail
and expand the universe of content MONTAGE can offer. Second,
45% of the missed sessions led to pages that users had visited
sometime earlier in the study. With a longer history of web
usage, and perhaps a more sophisticated user model, MONTAGE could
suggest useful content for many of these sessions.
Overall, it appears that the MONTAGE user modeling components
work well--the context-sensitive model scored higher than the
simpler model. Additionally, users are concerned that their
start pages load quickly; 64% of our users indicated speed of
loading the start page as ``very important.'' This concern is
more important, in fact, than having the target of their session
display in the start page. We are thus interested in
incorporating MONTAGE with a web prefetching system to greatly
improve the load time of the embedded-content montage.
Users appreciated MONTAGE's efforts to automatically select and
place content on the screen, but users still want some manual
authoring mechanism. A number of users point to their links
toolbar as what they feel MONTAGE must compete with. Although it
is true that MONTAGE was able to identify a number of these
preselected shortcuts automatically, it is clear that users would
be more inclined to adopt a hybrid system that also allows for
direct manipulation and authoring of content to include.
The MONTAGE work comes in the spirit of
related work in the
User Modeling community on adaptive personalization of
hypermedia. Prior efforts on adaptive hypermedia have relied
largely on the use of logical rules, policies, and templates to
adapt content, annotations, and user interfaces to different
users and user classes. A review of prior and current research
on adaptive hypertext can be found in .
Among research on personalizing web portals, the most similar in
spirit to MONTAGE is MyOwnWeb . The
proposed MyOwnWeb architecture relies on site
descriptions, which are essentially programs that run on a web
site (following links, filling in forms, etc.) and produce a
block of HTML as output. A system harnessing the MyOwnWeb concept would allow users to select the site descriptions desired
on a start page, and would execute the site descriptions and
concatenate the results for display. MONTAGE improves on this
approach in two ways. First, MONTAGE automatically selects the
content to display, freeing the user from the need to manually
maintain the personalized page. Second, MONTAGE embeds web
clippings--an approach that we believe captures a more
intuitive and robust approach to viewing distal content than
filtering content based on the HTML markup of the page.
Also similar to MONTAGE is work on automatically building
bookmark lists. PowerBookmarks  automatically
builds a Yahoo!-style web directory of the pages each user
visits, selecting which pages to include by how often the user
visits them and by their link structure. The Bookmark
Organizer  is a semi-automated system that
maintains a hierarchical organization of a user's bookmarks while
letting the user keep control of certain aspects (such as
``freezing'' nodes in the hierarchy to prevent them from being
changed). These systems reduce the effort required to maintain
the bookmark lists, but they do not address all the drawbacks of such
lists. PowerBookmarks and the Bookmark Organizer are insensitive to the user's
browsing context, and may require substantial user effort to find
the target link (navigating a hierarchical menu structure or
drilling down through a web directory). In contrast, MONTAGE leverages a sophisticated user model to automatically select
high-quality bookmarks, and to display the most appropriate
bookmarks for the user's current context. MONTAGE also
displays both embedded content and links in a single page,
requiring no scrolling and at most one link to follow.
Building a web montage is related to the more general interest in
building web-based document collections. Systems such as Hunter
Gatherer  assist
users in building collections of web content, either of web pages
or of within-the-page blocks of content. In contrast to these
systems, MONTAGE builds its collection automatically and
dynamically, but is effectively ``hard-wired'' to only one
collection (the ``material for a start page'' collection). The
techniques in MONTAGE and collection building systems nicely
complement each other, and it would be interesting to apply the
user-assisted assembly techniques to MONTAGE. For
example, both MONTAGE and the user could add components (links
and embedded content) to the start page collection, and MONTAGE would automatically group and display the components as well as
it could. The user could provide MONTAGE assistance for
components that are incorrectly displayed or that are added
manually. The participants in our study agreed that this type of
mixed-initiative system would be their likely favorite.
6. Related work
The goal of MONTAGE is to improve the experience for
routine web browsing--the browsing that users tend to repeat
over and over in similar situations. We implemented the
MONTAGE prototype system by coupling proxy-based monitoring and
predictive user modeling machinery with layout and display
techniques that compose web montages. We posed several
hypotheses about routine web browsing and addressed them in a
user study. We found that users appreciated having an automatic
system that could suggest links for them to follow, given a broad
view of their current browsing context. However, we found that
users particularly wanted the system to operate as transparently
as possible. We found that loading the montage page should not
take more than just a second or two, and users wanted some manual
authoring capabilities. These findings encourage us to push
forward in a number of directions with MONTAGE, as we have
highlighted in this paper and outline below.
In one extension of MONTAGE, we are
interested in providing users with more control of the learning
and reasoning. For example, we would like to allow users a means
of adjusting how navigation costs and information value are
combined. We also are exploring the use of dynamic topic leveling
for structuring and segmenting web pages. In this refinement, we
vary the level of detail in a content ontology at which different
topics are represented, based on a user's specified preferences or
on the amount of content in each topic. Beyond employing data
only from a single user, we are
interested in the use of collaborative filtering in MONTAGE.
We can exploit the browsing habits of multiple
users and build different kinds of portals within and across
enterprises. Such montages would allow users to inspect portals of
information that people with similar interests and patterns of
access find useful. For example, there may be value in harnessing
collaborative filtering methods to build an intranet portal for
assisting new employees of an organization by examining content on
human resources that is accessed at different periods of times after hiring,
and presenting montages based on the time that a new employee has been
at a company. We also seek to pursue
user interface design challenges highlighted by MONTAGE. For
example, we are interested in integrating the automated discovery
of favorites with methods that capture user-selected
favorites. Finally, we
are exploring a better understanding of and addressing
potential problems with the instability of layout associated with
adaptive procedures. Solutions to challenges with stability may
center on designs that mix a superstructure of an overall
persistent layout, as specified or locked by users, with
adaptation within particular adaptive regions of the layout.
We are excited about the challenges and opportunities captured by
the research on MONTAGE. We believe that an elegant intertwining
of automated analysis and user control will lead to efficient
methods for building and maintaining information resources based
on a user's browsing history and preferences.
7. Conclusions and Future Work
The authors thank Jonathan Grudin, David Heckerman and Mary Czerwinski for
feedback on the MONTAGE project, and thank the user study
participants for their effort, feedback, and insights. This
paper was improved by comments on earlier drafts by Cathy
Anderson and Zack Ives and by comments from the anonymous
reviewers. This work was conducted during at internship at
Microsoft Research and is funded in part by NSF grant
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- ... ontology1
- An extension to MONTAGE work would be
to adaptively adjust the level of detail in the topic ontology,
specializing into subtopics any topics that are overpopulated,
while leaving large, sparsely-populated topics general. Although
we used high-level categories for our initial user study, our
content-tagging subsystem tags content more finely, employing a
hierarchical ontology of concepts; we intend to explore this area
- ... narrowly.2
- Technically, each embedded
clipping is placed within an
<iframe> on a page sourced
<frame>. The width and height fields the user
enters simply control the size of the
actual browser window size controls the size of the