Visualizing implicit queries for information management and retrieval.
In this paper, we describe the use of similarity metrics in a novel visual environment for storing and retrieving favorite web pages. The similarity metrics, called Implicit Queries, are used to automatically highlight stored web pages that are related to the currently selected web page. Two experiments explored how users manage their personal web information space with and without the Implicit Query highlighting and later retrieve their stored web pages. When storing and organizing web pages, users with Implicit Query highlighting generated slightly more categories. Implicit Queries also led to faster web page retrieval time, although the results were not statistically significant.
Figure

1. Data Mountain with Implicit Query results
shown (highlighted pages to left of selected page).
graphics (e.g., a company logo) that users might associate
in memory with that page. To take advantage of this, we
present a visualization that allows users to manually create
a spatial layout of the thumbnails of their documents in a
3D environment.
As an organizational aid to the user, we use document
similarity metrics and visual highlighting cues to indicate
that web pages are semantically related in this personal
information space. This paper will compare two such
metrics, one user-driven and one content-driven, used to
determine web page similarity relations during sensemaking
tasks.
Current web browsers try to alleviate the sensemaking
problems raised above through the use of bookmarks or
favorites mechanisms, wherein users store the URLs of
interesting web pages in order to build a personalized
information space. Despite these user interface mechanisms,
a 1998 survey of over 10,000 web users revealed that one
of the most common problems users have with the web is
organizing the information that they gather there [7]. In
CHI 99 15-20 MAY 1999 Papers
related research, Abrams, et al. [1] studied the bookmark
archives and personal Web habits of users and made
recommendations for improving the design of existing
favorites management systems. Abrams surveyed 322 Web
users, and analyzed the bookmarks of 50 Web users in
detail. He found that bookmarks were used to reduce the
cognitive load of managing URL addresses (by aiding
memory and keeping history), to facilitate access, and to
create information spaces for personal and group use.
Bookmarks were often added sporadically-perhaps not
surprisingly when too many favorite pages were piling up in
a user's list. Almost 40% of those studied used no
organization and simply left web pages in the order they
were added to the favorites list; 50% used a hierarchy of
one (30%) or more (20%) levels. Most users organized at
the time they created a bookmark and cleaned up only
occasionally. The initial use of folders began after a user
had about 35 bookmarks. Abrams also found that 50% of
the bookmarks had been visited in the last 3 months; 67%
in the last 6 months; and 97% in the last year. Some ease of
use recommendations provided by Abrams included
providing aids in the browser for semi-automatic filing,
time- or usage-based orderings, and much better tools for
helping users in their organizing task. These findings
provided the primary motivation for the research described
in this paper.
We describe a new interaction that helps users quickly
recognize and use the categorical structure they need to
organize their favorite web pages. The interaction technique
includes the Data Mountain [13], a novel visual
environment for laying out personal web pages in a 3D
space (described below), and an Implicit Query technique
which shows the user which items are related to their
current interest. Our Implicit Query algorithms determine
similarities among web pages, and present the results in a
visual format that has been observed to be useful and
usable. This approach allows users to focus on relevant
items instead of searching through large numbers of pages
in the space. We have initially applied this idea to
interaction with documents on the Web, although the
interaction technique could be applied to any electronic
document management task.
The Data Mountain is a (3D) document management
system (see Figure 2). The design and implementation of
the Data Mountain is described in [13], so only a short
overview of the environment will be provided here.
Currently the Data Mountain is being used as an alternative
to a web browser's favorites or bookmark mechanism. It
should be understood that other forms of documents should
work equally well in the new environment.
The Data Mountain uses a planar surface (tilted at
degrees; see Figure 2), on which documents are dragged. A
document being dragged remains visible so that the user is
always aware of the surrounding pages. The user can place

Figure

2: Data Mountain with 100 web pages.
her web pages (or documents) anywhere on the surface. In
practice, the user creates meaning by organizing the space
in which she lays out these documents. In our study, each
user was allowed to freely choose an organizational method
and adjust it at any point throughout the study.
When the user clicks on a page stored on the Data
Mountain, the page is animated forward to a preferred
viewing position, as shown in Figure 1. When in the
preferred viewing position, another click will put the page
back on the Data Mountain in its last known location. In
practice, a click on a URL would allow the user to follow
that link. Also, a stored page can be moved at any time by
dragging it with the mouse. Since the page is visible during
the move, the user knows where the page will be when the
drag is terminated. The movement is continuous and
constrained to the surface of the Data Mountain.
When a user moves a page around on the Data Mountain, it
is likely to bump into other pages. Objects are not
allowed to intersect, and the user's dragging action is not
constrained. Instead, we handle collisions by displacing
previously placed pages, thus continually maintaining a
minimum distance between all pages, and transitively
propagating displacement to neighbors as necessary. The
user dragging the page continually sees what state will
result when the drag is terminated (i.e., there is no animated
settling time). This displacement technique also ensures
that pages never get fully obscured.
There are a number of cues designed to facilitate spatial
cognition. The most obvious are the 3D depth cues of
perspective view, accompanying size differences, and
occlusion, particularly when pages are being moved.
Simple, circular landmarks on the surface of the Data
Mountain also offer spatial cues, which may or may not be
utilized during page placement or retrieval. Less obvious,
but also quite important, are the shadows cast by web pages.
Papers CHI 99 15-20 MAY 1999
Subtle but pervasive spatial audio cues accompany all
animations and user actions to reinforce the visual cues.
The sound effects are highly dynamic. For example while
dragging a page, the user hears a humming sound that
changes pitch based on the speed at which the page is
moved. With careful use of the timbre, stereo and
attenuation of the humming sound, users are thus provided
an additional spatial location indicator. Finally, as the user
moves a page, other pages that move out of the way as
needed are accompanied by yet another distinctive sound.
For identification purposes, we provided a pop-up labels
similar to tool-tips to display web page titles. The title
appears as soon as the mouse moves over a page.
In order to support users' information management and
retrieval tasks on the World Wide Web (WWW), we use
what we call Implicit Queries. Implicit Queries are
generated automatically, based on the current focus of the
user's attention. In our system, the web page that the user
has currently selected is the basis for queries launched
implicitly in the background. Much richer models of users'
interests are easy to incorporate as well [6], but were not
needed for this experiment. Web pages that are similar to
the currently selected page are then highlighted to aid users'
organization and use of their web information space.

Figure

1 shows an example where the selected page has
something to do with entertainment. For this particular
user, most of the entertainment-related pages are on the left
side of the layout and are highlighted so that the pattern is
easily seen, yet not distracting. In color, this highlighting is
very obvious-it is much less obvious in a black and white
reproduction, so Figure 1 has been retouched to make clear
where the highlighting is.
Prior work on similarity metrics in the user interface
A number of previous systems (for example, Spire [18] and
Galaxy of News [11]) have used document similarity
metrics as a way to organize (or lay out) documents,
bringing similar documents closer together. Information
access in these systems is achieved primarily by navigation.
The Data Mountain differs fundamentally by allowing the
user to determine the layout of the documents, and the
Implicit Query mechanism highlights related documents in
the context of the user-defined organization. By
maintaining spatial consistency with the user's organization,
our system is able to more effectively leverage human
spatial memory [13].
Many systems show the results of explicit queries from
users, usually as ranked lists. Some systems post the results
of an explicit search against a known structure [4]. These
structures may be expert-generated hierarchies or
automatically derived (e.g., from the link structure of a Web
site or by a statistical analysis of inter-item similarities).
The resulting structures are shown using a variety of
representations - clusters, 2D and 3D spaces, tree-maps.All of these systems take a known structure and show how
search results fit into that organization. While these
approaches are similar to ours in that respect, it is important
to note that the organizing structures used by these systems
are generated by domain experts or statistical methods and
not by the user.
In contrast to these systems that use explicit queries, we
have developed an interaction technique which relieves the
user from having to generate an explicit query and so
reduces the amount of cognitive processing required to
retrieve related information. Much less work has been done
on Implicit Queries and interfaces to them. The
Remberance Agent by Rhodes and Starner from MIT [12]
described an approach similar to our general Implicit Query
idea. They implemented a continuously running process
which monitored what a user was reading and automatically
sent this information to a backend server which indexed
personal information such as email, notes and papers. The
query consisted of whatever information was being read or
typed into the current emacs buffer. The results were
presented as a ranked list of titles in a separate window, and
were thus very limited compared to our Data Mountain.
Schilit et al. [15] developed the XLibris system to support
what they call active reading. Reading was enhanced by
computation using a kind of Implicit Query mechanism.
Highlights made by the user via pen markings were used to
issue an automated query for related materials. It is not
clear that the use of highlighting to drive search is optimal,
since people highlight and annotate for many different
reasons [9]. The results are shown as thumbnails in the
margin, which provides a local context, but no global inter-item
similarities are shown as in the Data Mountain.
Our work on visualizing the results of Implicit Queries
combines two ideas in a novel and a powerful way. We use
a rich visual representation of objects and their inter-relations
on the user-determined spatial layout of the Data
Mountain along with Implicit Queries for information
management. In addition, we empirically evaluate the
usefulness of our system to support users in information
organization and retrieval tasks.
Previous research on highlighting techniques
Much research has been performed on the usefulness of
highlighting in attracting attention to relevant information
in a display [17]. For instance, it is well known that the
techniques of reverse video, color, boldness (or brightness),
underlining, and flashing are all effective highlighting
techniques, although some of them can actually be
disruptive if applied inappropriately. For our Implicit
Query highlighting, we examined over 20 highlighting
techniques, some inspired by previously reported research
[5, 10]. Based on our informal observations of which
techniques were more most effective without causing undue
disruption, we chose to implement a simple green outline to
display which web pages were related to the currently
selected web page.
CHI 99 15-20 MAY 1999 Papers
Finally, it should be noted that we have chosen a binary
approach to highlighting similarities in the web pages
stored on the Data Mountain, as opposed to the continuous
approaches we considered. We chose a binary approach
purposefully, as we were concerned about the possibly
distracting effect that Implicit Query highlighting would
have on the user's primary task. We also assumed that a
binary approach would lessen the decision-making burden
on users. However, we have prototyped many designs that
show the relevance score along a continuum in the
visualization.
USER STUDIES OF
We tested our visualization and Implicit Query user
interface ideas in two studies. The first study examined
how users managed and stored 100 favorite web pages with
and without Implicit Queries during their web page
interactions. It was our hypothesis that the Implicit Query
algorithms and highlighting would provide a useful guide to
users during their web page organization. Although users
were not required to follow the query recommendations in
any way, we further hypothesized that users would indeed
group together the highlighted pages. In addition, it was
hypothesized that Implicit Query highlighting would
improve memory of where pages were stored spatially on
the data mountain, due to subjects' having spent more time
attending to related pages and considering the suggestions
proffered by the system. This hypothesis was based on the
theoretical notion of levels of processing [2]. According
to this theory, information can be processed more or less
deeply, ranging from a shallow analysis (attention to
surface features) to a deeper, semantic analysis.
Information that is processed more deeply has been shown
to be more likely to be remembered over time.
In these studies, we also explored two similarity metrics to
drive the Implicit Queries. Looking at the content (word)
overlap of pages generated one metric, while the other was
derived from a group of subjects' previous organizations of
the same web pages. We expected that the Implicit Query
suggestions coming from subjects' previous organizational
strategies might be the best case for a similarity metric,
and so hypothesized a performance advantage for this
algorithm.
In the second study, we examined whether or not this
system guidance during web page organization would
actually benefit subsequent retrieval of those previously
stored favorite web pages. Any retrieval time advantages
will be realized over and over again as subjects repeatedly
revisit the page later.
Methods
Subjects
Thirty-five subjects of intermediate web ability and who
were experienced Microsoft Internet Explorer" 4.0 (IE4)
users at work or home participated in the experiment. All
users were required to successfully answer a series of
screening questions pertaining to web browser and Internet
knowledge in order to qualify for participation. The number
of females and males was balanced. 15 subjects organized
their web pages with no Implicit Query mechanism. 20
subjects were aided by one of two Implicit Query
algorithms subjects used algorithm 1 or IQ1; 11 used
algorithm 2 or IQ2). The experimental sessions involved
two studies, an organizational phase and a retrieval phase.
For clarity, methods and results of the two phases will be
described separately as Experiment 1 and Experiment 2.
The Methods for Experiment 1, the organizational phase,
are described below.
Material
One hundred web pages were used in this study; 50 of the
pages came from PC Magazine's list of top web sites (and
so were likely to have been seen by at least some of the
participants) and 50 pages were selected randomly from the
Yahoo!" database. The web pages were downloaded onto
a web server located on the computer the subject worked at.
We used two algorithms to generate a set of matching pages
for each web page in the study, a co-occurrence algorithm
and a content-based algorithm. IQ1 - co-occurrence
similarity. The first similarity measure was derived from a
page-page co-occurrence matrix based on seven previous
subjects' categorizations. Only subjects whose
categorizations were relatively clear and discrete spatially
were used for this algorithm's derivation. We counted the
number of times that a pair of pages co-occurred in the
same cluster - this number varied between 0 and 7. This
algorithm essentially tells the user, Other people thought
these pages were related. IQ2 - content-based similarity.
For the content-based similarity computations, the popular
vector space model from information retrieval was used
[14]. Documents were pre-processed to remove the HTML
markup. Words on a standard stop list of common words
along with 10 web-specific words were omitted, and white
space or punctuation was used to delimit words. Each
document was represented as a vector of words with entries
representing the frequency of occurrence of a word in that
document. The similarity between documents was
measured by taking the dot product of the document vectors
divided by the lengths of the vectors.
Once we generated these two measures of similarity, we set
a threshold for each algorithm. Only web pages that
matched the target web page at a level of similarity above
the threshold were recommended as related to the user.
We wanted the two algorithms to recommend roughly the
same average number of matches per page. The thresholds
we chose generated, on average, 4.2 and 4.3 matches per
page respectively. In the co-occurrence algorithm (IQ1),
this threshold produced 39 pages that had no match above
the threshold. There were 28 pages that had no match above
the threshold for the content-based algorithm (IQ2).
To indicate which pages were identified as matching the
page being viewed, we highlight the related pages with a
Papers CHI 99 15-20 MAY 1999
bright green frame as shown in Figure 1. Highlighting
automatically occurs when subjects are presented with a
new page for storage during the first phase of the study.
Selecting any page on the Data Mountain causes its related
pages to be highlighted.
Procedure
Subjects were shown 100 web pages sequentially (order
was randomized for each subject) and asked to store them
on the Data Mountain. They were allowed to create any
organization they wanted and were encouraged to create a
personally meaningful structure that mimicked how they
stored favorite web pages at home. Subjects were told that
they would have to use their organization for a retrieval task
in the second half of the test.
For the subjects who were in the Implicit Query conditions,
related pages were highlighted according to the IQ1 or IQ2
algorithms. We briefly interrupted each subject in the
Implicit Query conditions to discuss the green highlighting
after 10 minutes of their organization phase had elapsed.
We asked them if they had noticed the highlighting, and
what they thought it was for. We then explained its purpose
and informed participants that they were free to use or
ignore the suggestions. Some subjects noticed the
highlighting, figured out its purpose and requested
validation of their assumption before 10 minutes had
passed, which motivated an early intervention. When this
occurred, we discussed it with the subjects at the time of
their request. The discussion time, on average, took less
than 1 minute.
After all 100 pages had been saved on the Data Mountain,
the subjects were given time to fine-tune their organization
until they were personally satisfied with it.
The main independent variable of interest in Experiment 1
was the between subjects variable of which Implicit Query
matching algorithm was used (no Implicit Query, IQ1, or
IQ2). The number and type of categories, organization time,
and subjective satisfaction ratings were the dependent
measures of interest.
Results
Influences on information management behavior
Most subjects adopted an organization based on semantic
categories. Some of these subjects augmented this with
temporal or alphabetic cues as well. Five users stored files
alphabetically and one used no apparent organization.

Table

1 shows the number of subjects following each of the
observed organizational strategies. For subjects who
categorized using a strategy other than alphabetic, we asked
them to circle and label their categories.
Number of categories
The average number of categories for each group is shown
in

Table

2. Subjects in the Implicit Query conditionscreated slightly more categories than did subjects without
the Implicit Query highlighting during the organization
phase of the study, although this difference was not
statistically significant, F(2,27)=1.31, p=.28. Because we
had to remove subjects who alphabetized from this analysis,
the test was not very sensitive. If we pool these results with
those of another condition (not reported here) that used the
same organizational procedure and stimuli, but different
retrieval cues, there were reliable differences in the number
of categories,
Implicit Query
Condition
Semantic Alphabetic Unknown
Implicit Query
IQ1-: Co-
occurrence
based
IQ2: Content
based

Table

1. Number of participants that used a particular
organizational strategy while storing web pages.
Implicit Query Condition
Avg. # of Categories
(Standard Deviations in
parentheses)
IQ1: Co-occurrence based 15.8 (5.8)
IQ2: Content based 13.6 (5.9)

Table

2. Averages and standard deviations for the number
of categories observed in each Implicit Query condition.
Overlap of category concepts
Subjects' organizations were analyzed for their amount of
overlap with each other. In order to do this, we used the
layouts that subjects had circled and labeled for us. We
identified 20 categories that subjects used very frequently,
and reduced their category structures into these 20
categories. In order to check our data reduction procedure,
we compared two authors' classification efforts on the same
layouts for inter-rater reliability. On average, we obtained
90% agreement across Implicit Query conditions for two
independent observers. One subject was primarily
responsible for the inter-rater disagreement, and for this
subject the 2 raters only agreed 50% of the time.
Once it was determined that the categorization scheme was
a reliable one, subjects' clusters were analyzed for
consistency. We found no reliable differences in how often
subjects agreed with each other in terms of categories
maintained during the storage phase of the study
F(2,26)=.164, p=.85. In other words, having an Implicit
Query mechanism did not result in more or less agreement
between subjects and their organizational schemes.
CHI 99 15-20 MAY 1999 Papers
Organizing Time
Although subjects were not encouraged to be efficient
during the organization phase of the experiment, we did
record organization times across Implicit Query conditions.
Organization time includes both the time used to initially
place and organize web pages and the time to reorganize
pages after the initial storage. The average organization
times were 52 minutes for IQ0, 71 minutes for IQ1 and 81
minutes for IQ2. The effect is marginally reliable
2.88, p=.07. This is not surprising, given that Implicit
Query users often considered whether or not to follow the
system recommendation for where to store a web page, and
they created somewhat more categories, both of which
could have taken extra time.
Most of the total organization time was spent during initial
placement (an average of 53 minutes across all conditions)
and substantially less time during reorganization (an
average of 15 minutes across all conditions). The variance
in reorganization time was quite large, due primarily to two
subjects. The subjects who alphabetized in IQ1 and IQ2
each took over an hour to reorganize their alphabetized
layouts, which is an interesting finding in itself and warrants
further research. One subject moved thumbnail images
more than 1000 times! In our prototype it was difficult to
reorganize because no grouping of objects was allowed. If
subjects who alphabetized are removed, the organization
time differences between conditions decrease. (The
alphabetizers in IQ0 did not take exceptionally long during
reorganization.)
Questionnaire measures
Participants provided a variety of subjective ratings about
the user interface and the interaction techniques at the end
of the study session. One subject is not included in the
analysis of the Implicit Query condition 2 due to her not
filling out any answers on the questionnaire. A multivariate
ANOVA (using Implicit Query condition as a between
subjects factor and each questionnaire item as a multivariate
response) revealed a reliable interaction between Implicit
Query condition and only one questionnaire item,
F(2,31)=7.09, p=0.003. The questionnaire item that drove
this effect, I was satisfied with my organizational scheme;
1=Disagree, 5=Agree, accounted for over 31% of the
variance in the data. Scheffe post-hoc analyses showed that
subjects in the no Implicit Query (IQ0) and the co-
occurrence-based algorithm (IQ1) groups were not different
from each other on this questionnaire item (average ratings
3.6, SD=0.22 and 4.0, SD=0.28, respectively). Subjects in
the content-based algorithm (IQ2) condition, however, were
less satisfied than the other two groups (average rating =
2.6, SD=0.25).
A few questionnaire items pertained only to the Implicit
Query visualization, and so only groups IQ1 and IQ2
responded to these questions. Analyses of these results
revealed that the co-occurrence algorithm built from
previous subjects' organizations of the 100 web pages
was rated as significantly less distracting than the
content-based algorithm (IQ2), t(18)=-2.04, p=.01, two-
tailed. Subjects' ratings of the IQ1 highlighting as more
useful than IQ2 reached borderline significance, t(18)=1.8,
two-tailed. No other significant effects emerged
from analysis of the questionnaire data.
Discussion
The results of the organization phase of this study were
mixed, and suggest that good Implicit Queries in the user
interface in the storage of information during web
interaction might lead to slightly more detailed
categorization at the cost of significantly longer storage
times. According to Levels of Processing Theory [2], the
increased time spent in information management could
result in a deeper encoding of subjects' web page
organizations on the Data Mountain and might therefore
subsequent web page retrieval in both speed and
accuracy. Questionnaire data suggested that subjects were
equally satisfied with their organizations with no Implicit
Query or with co-occurrence based Implicit Query, but
were significantly less satisfied with content-based Implicit
Query.
In Experiment 2, we assessed whether or not the presence
of the Implicit Query highlighting during web page storage
improved web page retrieval performance. We compared
the IQ0, IQ1 and IQ2 groups on average retrieval times, the
number of incorrect pages retrieved, and the number of
failed retrieval attempts for the 100 web pages. It is
extremely important to note here that the Implicit Query
highlighting was disabled for this phase of the experiment.
In other words, a subject's target web page did not highlight
during its retrieval trial, nor were web pages that might be
related to the target highlighted. While such highlighting
would be desirable in practice, we felt it would be too
beneficial for experimental purposes.
Methods
Subjects, Materials and Procedure
The same thirty-five subjects participated in the second
study. After a short break following the organizational
study, the subjects started retrieval. For the second study,
participants were shown a retrieval cue consisting of the
textual title of the web page, and asked to find the
corresponding page on the Data Mountain. The retrieval
cue was presented in a small, rectangular window below the
display window of the Data Mountain. The cues were
presented in a random order for each subject. If a subject
could not find the target page within two minutes, the
subject was instructed to proceed to the next retrieval trial.
Since the Implicit Query highlighting was not enabled
during this phase of the experiment, there are no visible
differences in the user interface among the experimental
conditions in this phase.
Papers CHI 99 15-20 MAY 1999
The three primary dependent measures used in Experiment
were web page retrieval time, the number of incorrect
pages selected, and the number of failed attempts to retrieve
a web page. Retrieval time (or reaction time) was defined
as the time to select the correct item. Incorrect selections
referred to the total number of pages selected that were not
the target page, not including failed retrieval trials. Failed
retrievals occurred when the subject either took longer than
two minutes for retrieval or chose to stop searching for an
item.
Results
Retrieval Time
Only trials in which subjects found the correct web page
within the allotted two-minute timeframe are included in the
reaction time analysis for both sessions. Figure 3 shows the
retrieval time results. For each subject we computed a
median response time. The average retrieval time across
subjects was 9.5 seconds (SD=8.5) for the subjects with no
Implicit Query algorithm, 6.8 seconds (SD=2.01) for the
co-occurrence algorithm, and 7.3 (SD=2.14) seconds for
the content-based algorithm. The difference between the
three conditions was not significant, F(2,32)=0.73, p>.05.
The high variance in IQ0 was primarily responsible for the
failure of this effect to be reliable.
Effects
As expected, there is large variability in the time required to
retrieve individual web pages. The average RT for
individual web pages ranges from 4.9 seconds (for the
NASA Home Page) to 24.3 seconds (for the Mercury
Center Home Page). In general, the pages that were fastest
to retrieve were either very distinctive semantically or
visually. Popularity of a web page may have had additional
effects, but the effect was not large (average retrieval time
for 50 popular web pages =12.7, randomly chosen web
pages average=12.9).
Incorrect Selections
On average there were very few visits to incorrect pages.
The average number of incorrect retrievals in the no
Implicit Query algorithm condition = 3.5 (SD=3.6), the IQ1
and for the IQ2 algorithm the
average number of incorrect retrievals = 4.7 (SD=3.6).
There were no significant differences in the average number
of incorrect pages retrieved across the 3 conditions,
F(2,32)=.78, p=.47.
Failed Retrievals
There were an average of 4.8 (SD=3.1) trial failures in the
no Implicit Query condition, compared to an average of 2.0
(SD=2.4), on average, in the IQ1 algorithm and 5.0
(SD=6.1) in the IQ2 algorithm, and this difference was once
again not statistically significant, F(2,32)=1.6, p=.2.Web Page Retrieval TimeAverage RT (seconds)106
IQ 22Implicit Query Condition

Figure

3. Average retrieval times (including
standard error of the mean) for the 3 IQ conditions.
Discussion
It is important to reiterate that no Implicit Query
highlighting was used during retrieval in Experiment 2.
(We suspect retrieval times in IQ1 & 2 would be reduced
even further if highlighting were used during retrieval).
Results obtained in Experiment 2 suggest that Implicit
Query highlighting during storage can facilitate subsequent
retrieval, but the effects were not reliable. Retrieval times
were variable both across items and subjects, and additional
experimental power will be necessary to fully understand
this tendency. Retrieval time differences are important
because typical uses of any one piece of information will
have one storage incident and many subsequent retrievals.
By reducing retrieval time at the expense of storage time,
we can shift and reduce overall cognitive load for the user
when finding previously viewed information for the task at
hand.
A concern about the generality of these results is that we
tested subjects' retrievals on the same day that they stored
the web pages, an unlikely scenario in everyday web page
access. In subsequent studies in our laboratory, we brought
subjects back 6 weeks and 6 months after they stored their
web pages on the Data Mountain. There was no decrement
in performance when subjects returned and were asked to
retrieve their web pages after either a 6-week period or a 6-
month period of disuse. Details of these further studies are
forthcoming.
CONCLUSION
This paper reported two studies that examined users' web
page organizations and later retrievals using a combination
of two interaction techniques, the Data Mountain and
Implicit Queries. Previous research [13] had already
demonstrated enhanced performance for web page retrieval
in the Data Mountain, when compared to current browser
favorites mechanisms (one-dimensional visual text lists).
The use of a passive and subtle Implicit Query aid
CHI 99 15-20 MAY 1999 Papers
influenced subjects at organization time. They took longer
and created somewhat more categories. There are
indications of influence at retrieval time, even when no
Implicit Query cue is present.
Future work will focus on user interface visualizations for
queries in much larger information spaces to see if more
powerful results can be obtained in those domains. We will
also continue to investigate the influence that Implicit
Queries have on individual users' organizational styles. For
instance, there was some indication that users without
Implicit Queries tended to use an alphabetic organizational
strategy more often. We will examine the use of alternative
highlighting mechanisms, and explore the use of Implicit
Queries at the time of retrieval as well as storage. This
future work will be carried out using alternative 3D visual
metaphors for visualizing the query results.

ACKNOWLEDGMENTS

We thank the User Interface Research Group at Microsoft
and Kevin Larson for their help and valuable comments on
this paper. We thank Paul Jaye for his help in data analysis.


--R

Information archiving with bookmarks: Personal web space construction and organization
Levels of processing: A framework for memory research.
Textual information retrieval.

Clusters on the World Wide Web: Creating neighborhoods of make- believe

GVU's 9th WWW User Survey
Information Seeking in Electronic Environments.
Toward an ecology of hypertext annotation
Generalized pointing: Enabling multiagent interaction
Galaxy of news: An approach to visualizing and understanding expansive news landscapes.
A continuously running automated information retrieval system.
Data Mountain: Using Spatial Memory for Document Management

Beyond paper: Supporting active reading with free form digital ink annotations
Sorting out searching: A user-interface framework for text searches
Screen Design.
Visualizing the Non-Visual: Spatial analysis and interaction with information from text documents
--TR
Galaxy of news
Information seeking in electronic environments
Cat-a-Cone
Sorting out searching
Information archiving with bookmarks
Beyond paper
Generalized pointing
Toward an ecology of hypertext annotation
Clusters on the World Wide Web
Data mountain
Introduction to Modern Information Retrieval
Visualizing the non-visual

--CTR
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classification;similarity;categorization;information retrieval;information management;information visualization