JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 3, NO. 1, FEBRUARY 2011

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Investigating User Behavior in Document
Similarity Judgment for Interactive
Clustering-based Search Engines
Minghuang Chen
Tokyo Metropolitan University, Tokyo, Japan
Email: chenminghuang@gmail.com

Seiji Yamada
National Institute of Informatics, Tokyo, Japan
Email: Seiji@nii.ac.jp

Yasufumi Takama
Tokyo Metropolitan University, Tokyo, Japan
Email: ytakama@sd.tmu.ac.jp

Abstract—This paper investigates the behavior of users
judging the similarity of documents in order to examine theuser’s feedback cost for interactive document clustering.
Modern web search engines employ linear-style SERPs(search engine result pages). In order to make use of information on continuously growing web, various search engines for the next generation have been studied, among
which clustering-based search engines are expected to bepromising. It is also important to introduce interactive user
feedback mechanism into search engines. The aim of thispaper is to study the effective interface design that is suitable for interactive clustering-based search engines. An experiment is conducted with 21 test participants, who wereasked to judge the similarity of document pairs based on
three conditions: viewing snippet, topic terms, or original
text. Those conditions are compared in terms of judgment
time and accuracy with ANOVA and chi-square analysis.
The typical judging behaviors of the participants are also
investigated by eye-tracking system. The results will contribute to the design of interface for interactive clusteringbased search engines for the next generation.
Index Terms—web search interfaces, interactive clustering,
document similarity judgment, eye-tracking

I.

INTRODUCTION

This paper investigates the behavior of users judging
similarity of documents. Recent years with the growth of
information, Web search engines has become a very important applications that can help our information access.
However, modern search engines have room for improvement, in order to make use of information on continuously growing Web. Therefore, various search engines
have been studied and developed for the next generation.
Most of the modern search engines are designed basedon a query, and user can find desired pages from SERPs(search engine result pages). Compared with such engines,
some new types of searching performance has been developed, which have more advanced functionalities. For© 2011 ACADEMY PUBLISHER
doi:10.4304/jetwi.3.1.3-10

examples, some engines such as “Baidu Zhidao”1 employ
a FAQ like search facility. Users can pose their questions
to the system and let other people to answer it. Some other search engines just like “Yippy”2 divide the searching
results into several clusters, from which users can choose
the cluster close to their searching purpose.
In our opinion, modern search engines have two points
to be improved. One is the limitation on the style of
SERPs. That is, information about retrieved pages, suchas the title, URL, and snippets, is linearly ordered in a
SERP. Therefore, when a query is ambiguous and has
several meanings, web pages of various topics are contained in a SERP in a mixed manner. For example, when
we input a term “Olympic” as a query, various topics,
such as independent Olympic Game, forthcoming game,summary of winter and summer Olympic Games, various
records of successive games, will be contained in the
SERPs. From existing SERPs, it is difficult to discriminate such topics and find required pages. In order to solve
such a problem, one promising approach is to employ
clustering algorithm, such as the above-mentioned Yippy
Search.
Another point to be improved is insufficient feature ofuser feedback mechanism. Obtaining feedbacks fromusers are very important for improving search engine’s
performance through interaction. In the field of document
retrieval, relevance feedback [1] has been studied as
feedback mechanism. It obtains the result of user’s relevance judgment of documents as the feedback for improving the retrieval performance.
Regarding the web search engines for the next generation, this paper focuses on interactive clustering-based
search engines. By introducing user feedback mechanism
in clustering-based search engines, its potential is expected to be increased.
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http://zhidao.baidu.com/
http://search.yippy.com/

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Introducing feedback information from a user to clustering process can be done by constrained clustering [2].
It uses two kinds of constraints; must-link that indicates
two documents connected with the link must be in thesame cluster, and cannot-link that indicates two documents having the link cannot be in the same cluster. By
converting the user feedback into those links, the feedback mechanism can be incorporated into clusteringbased search engines.
When obtaining feedback from users, the workload ofusers providing feedback should be considered. Althoughmuch feedback information improves the performance ofsystems (i.e. search engines), it forces heavy burden on
users. In order to solve this tradeoff, the concept of Minimal User Feedback (MUF) [3] has been proposed. The
MUF employs two approaches: minimizing the quantityof feedback information and minimizing the cost of generating each of feedback information (i.e. relevance judgment for a single document). A related work with theformer approach is active learning [4, 5], in which alearner actively gathers training data effective for learning.
The aim of the paper is the fundamental study of interface design for interactive clustering-based search engines, which relates with the latter approach of the MUF.
The effectiveness of a Web search engine is not only determined by its retrieving mechanism such as ranking
algorithm and crawlers, but also by the design of interface and interaction with users, such as the design of
SERPs.
When user feedback mechanism is introduced in clustering-based search engines such as the Yippy Search, theprimitive task of users is not to judge a relevance of web
pages as in the case of existing document retrieval, but tojudge the similarity of web pages. We call such a task
“document similarity judgment” in this paper. This paper
studies the effect of providing information (clue for the
judgment) on the user behaviors in document similarity
judgment. The study on users judging relevance of objects has been investigated as introduced in Section 2.
However, to our best knowledge, the study on users judging similarity of objects has not been investigated.
In this paper, test participants are asked to judge thesimilarity of two documents. Given a pair of news articles,a participant judges whether those articles relate with thesame topic or not. As the clue for judging similarity, threekinds of information: original text, snippets, and terms,
are mutually provided. As less work has been done forstudying similarity judgment, it is not clear what terms or
snippets are effective for the judgment. In this paper, we
suppose that information identifying the difference andcommonality of documents is effective. Therefore, common and specific terms / snippets are presented to test
participants in a separate manner.
The judgment accuracy and judgment time are compared between these 3 conditions (text, snippet, term).
The result shows participants viewing terms could judge
the similarity of documents more quickly than viewing
other conditions, whereas the improvement of accuracy© 2011 ACADEMY PUBLISHER

Figure 1. A screenshot of gaze plot by T60 Eye-Tracking Systemwith experience was observed when a snippet or originaltext is presented.
Behavior of test participants in judging document
similarity are also recorded using eye tracking device. By
analyzing AOI (area of interest) and focusing time,
typical viewing behavior is investigated.
This paper is organized as follows. The studies of user
behavior in relevance judgment are summarized as therelated work in Section II. Section III describes theoutline of experiment, which includes the used document
set and algorithm for generating clues for judgment.
Experimental results are shown in Sec. IV.
II. USER BEHAVIOR IN RELEVANCE JUDGMENT
As noted in Section I, relevance judgment and similarity judgment are essential tasks for a user to provide feedback information. Compared with similarity judgment,much work has been done for studying users behavior in
judging relevance of documents, which include users’viewing behavior in SERPs and web pages [6, 7, 8] andstudy on the effect of snippet on relevance judgment [9,10, 11]. Among them, Chen et al. compared accuracy of
relevance judgment and judgment time between the condition of providing snippet and that of providing originaltext [11]. Most of the studies does not only investigate
judgment accuracy and judgment time, but also analyze
users’ behavior by using eye-tracking systems.
An eye-tracking system can record user’s eye
movement on a computer screen. This paper uses T60
from Tobii technology to investigate the user behavior in
document similarity judgment. It can record eyes
movement and focusing area (AOI) on the screen. The
focusing time is also recorded. By using the Tobii’s
accessory software Tobii Studio, the recorded data can bevisualized. Figure 1 shows a gaze plot, which shows the
trajectory of focusing point. In the gaze plot, the size ofnode represents the focusing time. Figure 2 is called aheat map that is also visualized by the Tobii Studio. A
heat map is a graphical representation of data where theaccumulated focusing time is represented as colors. The
heat map can be used for displaying areas of a Web pagethat are frequently scanned by readers [9, 10]. The mostfrequently focused areas are highlighted by red colors.

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Figure 2. A screenshot of heat map by T60 Eye-Tracking System

Figure 3. A screenshot of the experiment system

Such eye-tracking systems have been widely used forstudying users’ behavior in viewing Web search results.
Cutrell [9, 10] has investigated the effect of task type,snippet length, and the position of the best result in MSN
SERPs on users’ viewing behavior. Interesting results
were obtained, such as that snippet length has different
effect between navigational and informational tasks. Thatis, for informational task, longer snippet improved click
accuracy while reducing the task time, but opposite effectwas observed for navigational task. This result was explained based on the analysis of eye-tracking record thatuser performing informational task tended to rely onsnippets.
Rodden [7] has explored the relationship between
mouse movements, and eye movements when performing
a search task with using Google. Various interesting patterns are observed, such as keeping the mouse still while
reading and using the mouse as a reading aid.
Lorigo et al. [6] has investigated users’ search andevaluation behavior based on the analysis of scan path
recorded by using eye-tracking system. The results haveshown that users tended to make decision on performing
new search before viewing entire page of retrieved result,and that they tended to reexamine top 1 and 2 results
frequently.

when snippet is displayed. The document pair is extracted
randomly from 42 documents. In the rest of this section, adocument set used in the experiment and the method forextracting topic terms and that for generating snippet aredescribed.

III. OUTLINE OF EXPERIMENTThis paper investigates users’ behavior in similarity
judgment. The task of test participants is to judge thesimilarity of two documents. Given a pair of documents,
they are asked to judge whether those documents relatewith the same topic or not.
For the experiment, we implement an experiment
system that is written in VB language as ASP pages.
Figure 3 shows the screenshot of the experiment system,
which can be accessed with ordinary web browsers. In
these pages, information about 2 documents is arrangedside by side.
As noted in Sec. III.B and III.C, two kinds of topic
terms (snippet): common and specific terms (snippets) arepresented. The common terms (snippets) are displayed in
the upper part of the screen, and specific one is displayedin the lower part. Topic terms are highlighted with red© 2011 ACADEMY PUBLISHER

A. Document set
The documents and topics are selected from Reuter
Test Collection3. It includes 21578 documents with 135
topics. In the experiments, we prepared the document set
by selecting a few topics and randomly picking up thecorresponding documents. If the document of different
topics is obviously different, test participants can judge
the similarity of documents without carefully reading
displayed information. Therefore, topics that are to be
used in the experiments should relate with each other.
Based on this consideration, we selected the following 3
topics, which are overlapping each other, i.e., several
documents belong to two of those topics: Coffee (Topic1), Cocoa (Topic 2), and Corn (Topic 3).
Fourteen documents that belong to only one of those
topics are collected from each topic, and total 42
documents are used in the experiment.
B. Extraction of topic terms
Terms that represent the topic of the document aresupposed to work as a clue for judging similarity of documents. In particular, the terms indicating the difference
and commonality between documents should be presented to a user. Based on this consideration, we classify
the topic terms into common and specific terms, which
are extracted with the following two steps:
Step 1: Extraction of topic terms from a document
Step 2: Extraction of common and specific terms
In step 1, given a set of documents D (42 documents
used in the experiment), terms that have high TF-IDF
values are extracted as topic terms. Among various
definitions of TF and IDF, we employed the following
equations.

3

http://www.daviddlewis.com/resources/testcollections/

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TFi , j 

ni , jnk

IDFi  log

.

(1)

D

d : t i  d .

(2)Where ni,j is the number of occurrences of the considered term (ti) in document dj, and the denominator of Eq.
(1) is the sum of the number of occurrences of all terms
in document dj. The denominator of Eq. (2) shows thenumber of documents in which the term ti appears. It
should be noted that we calculate TF-IDF score for only
the terms appeared at least once in D. To be more exact,
all of the terms in D are extracted and the TF-IDF values
are calculated except the terms that are contained in astop word list. In the experiment, we employed the stop
word list available from Wikipedia4.
In step 2, for a pair of documents that are to becompared, the topic terms that occur in both of the
documents are selected as common terms, whereas theterms exclusively occur in either of the documents are
selected as specific terms.
C. Snippet generation
One of the most important features of modern searchengines is a snippet, which is a fragment of a documentthat represents its contents. In particular, the snippet generated based on the topic can help users to make a judgment easily on whether to read the corresponding documents or not. This merit is supposed to be valid for similarity judgment.
Based on the same consideration as noted in Sec. III.B,
two types of snippets, common and specific snippets, areemployed in this paper. The snippets are generated by thefollowing steps:
Step 1: Extraction of topic terms (Sec. III.B).
Step 2: Score calculation for each sentence.
Step 3: Extraction of a set of sentences as a snippet.
In step 2, the score of a sentence is calculated based on
the TF-IDF values of specific/common topic terms thatare contained in the sentence.
In step 3, a set of sentences with the highest score isselected as a snippet. The snippet that consists of thesentences containing specific (common) terms is called
specific (common) snippet.
IV. EXPERIMENTAL RESULTS
In the experiment, 21 participants including 18 graduate / 2 undergraduate students and 1 researcher (19 males
and 2 females) in engineering field took part in the experiment. A participant is asked to judge 3 document pairs
for each type of information (snippet, text, term). A pairof documents is generated randomly from the document
set containing 42 documents as described in Sec. III.A.
The experimental results are analyzed by two
approaches. First, the performance of participants’4

http://en.wikipedia.org/wiki/Stop_words© 2011 ACADEMY PUBLISHERTABLE I.
EXPERIMENTAL RESULT IN THE 1ST TRIALk, j

AVG (s)STDEV (s)Correct
Mistake

Text
73.31
40.00
12
9

Snippet
40.07
27.62
11
10

Term
36.41
27.80
10
11

TABLE II.
EXPERIMENTAL RESULT IN THE 3RD TRIAL
AVG (s)STDEV (s)Correct
Mistake

Text
58.54
41.87
17
4

Snippet
43.59
24.02
16
5

Term
32.27
19.46
11
10

similarity judgment is compared among 3 types of
information in terms of judgment accuracy and judgment
time. After that, the behavior of the participants inperforming the experiment is analyzed based on therecord of eye-tracking system.
A. Performance of similarity judgment
When a user performs similarity judgment in an actual
application, the judgment has to be repeated several times.
Therefore, user’s adaptability is one of important factors
for evaluating the type of providing information. In theexperiment, a participant judged the similarity of
documents 3 times for each type of information. In order
to consider the participants’ adaptability, we separately
analyzed the results in the 1st and the 3rd trials.
Table I and II show the experimental results in the 1st
and the 3rd trial, respectively. These tables containaverage judgment time (AVG) and its standard deviation(STDEV), and the number of correct answers andmistakes. The columns correspond to the type of
information.
The difference of snippet, text, and term in judgment
time is in the 1st trial is analyzed using one-factor repeated measures analysis of variance (ANOVA). As aresult, we found statistically significant differences in themean judgment time among snippet, text, and term
(F(2,40) =16.52, P=5.9E-06).
In the case of the 3rd trial, the assumption of equalityof variance was rejected. Therefore, we conducted nonparametric test (Kruskal Wallis Test) and confirmed thedifference is statistically significant (χ2=7.023, P=0.030).
As for the difference between the 1st and the 3rd trials,
p-value of the 1st trial is much smaller than 3rd trial. We
think that in the 1st trial, participants did not get used tothe experiment including the type of information, which
affected the variance.
In order to examine the effectiveness of each type of
information, multiple comparison tests are conducted. In
the case of the 1st trial, assumption of equality of variance could not be rejected. Therefore, Tukey’s test andFisher’s LSD is used. In the paper, * and ** indicate thesignificant level of 5% and 1%, respectively. Table III

JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 3, NO. 1, FEBRUARY 2011

summarizes the result. The result shows that the participants could judge the similarity of document using snippet and terms more quickly than reading original text.
As already noted, the assumption of equality ofvariance was rejected in the case of the 3rd trial.
Therefore, we conducted nonparametric tests: Scheffe test
and Steel-Dwass test, of which the results are shown in
Table IV. In this case, only the difference between text
and term is statistically significant. From both the resultsof the 1st and the 3rd trials, it is shown that providing
terms is more effective in terms of the time cost ofsimilarity judgment.
A chi-square analysis on the number of correct answers
and mistakes as shown in Table I are performed in order
to investigate the effect of type of information on the accuracy of similarity judgment. Although we found no
significant difference among 3 types of information inboth of the 1st (χ2=0.382, P=0.826) and the 3rd trials(χ2=4.672, P=0.097), we can see the tendency that thedifference in the 3rd trial is larger than the 1st trial. In
particular, the judgment accuracy in the 3rd trial whensnippet and text are provided gets improved from the 1sttrial. However, the judgment accuracy when terms areprovided is low both in the 1st and the 3rd trials.
We suppose this result indicates that snippets andoriginal text are easier for the participants to adjust thanterms. In order to consider it in more detail, typicaljudging behavior of the participants is investigated in Sec.
IV.B.
B. Analysis of behavior in similarity judgment
By analyzing the eye-tracking data, we found that theposition and topic of documents affected participants’viewing behavior. We also found the viewing behavior
that is specific to the case when terms are presented.
These findings are described in the rest of this subsection.
It is noted that all of the 3 trials for each type of information per participant are analyzed in this subsection.
1) Effect of document position on participants’viewing behavior
Figure 4-6 show the distribution of AOI per participant,in which focusing time is accumulated for right-hand andleft-hand document areas, respectively. It is noted that
TABLE III.
MULTIPLE COMPARISON TEST IN THE 1ST TRIAL
Level1

Level2

SnippetSnippet
Text

Text
Term
Term

P-value (Tukey)
0.0042**0.92870.0014**p-value(LSD)
0.0015**0.71520.0005**TABLE IV.
MULTIPLE COMPARISON TEST IN THE 3RD TRIAL
Level1

Level2

SnippetSnippet
Text

Text
Term
Term

© 2011 ACADEMY PUBLISHER

P-value(Scheffe)
0.53930.31120.0307*p-value(Steel-Dwass)
0.42640.2206
0.0341*

7

Figure 4. AOI distribution per participant (Snippet)

Figure 5. AOI distribution per participant (Text)

Figure 6. AOI distribution per participant (Term)among 21 participants, 6 participants were excluded fromthese analysis because of stability problems with the eye
tracking, leaving us with 15 participants.
These figure show that participants spent more time on
looking at left-hand document area when either text orsnippet is presented. This tendency is stronger when text
is presented than snippet. Figure 7 is the heat map thatshows this tendency when snippet is presented.
We think this tendency can be described by two
assumptions about our behavior when reading documents.
First, it is supposed that we usually read a document fromleft to right. Second, when we compare two things, we
use one as a basis and try to find the difference fromanother one. As a result, we suppose participants read theleft document first and used it as a basis.

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Figure 8. More focus at specific snippet in different topic conditions

Figure 7. Heat map of typical behavior of viewing snippet

TABLE V.
SUMMARY OF FIRSTLY-GAZED AREA
Left
Right
Sum

Text
42
3
45

Snippet
39
6
45

Term
32
13
45

Sum
113
22
135

TABLE VI.
EFFECT OF TOPIC ON VIEWING BEHAVIOR
Topic

SnippetTerm

CommonSpecific

CommonSpecific

Same

22

5

19

12

Different

7

11

4

10
Figure 9. Frequent switches of AOIs when viewing terms

In order to examine this assumption, we analyzed
which document area (right-hand or left-hand) the
participants gazed first. Table V shows the frequency ofgazing at right-hand / left-hand document areas per typeof information. From the table, we can see the sametendency as Fig. 4-6, i.e., participants viewing text werethe most likely to gaze at left-hand document area first,
and those viewing snippet were second most likely togaze at it first.
2) Effect of topic on participants’ viewing behavior
Table VI shows the number of participants who
focused on the corresponding (common or specific) area
more frequently than another areas. It is counted in two
categories: when documents of the same topic arepresented and those of different topic are presented.
The table shows that in the same topic condition
participants more frequently looked at common snippet /terms than specific ones. On the contrary, in the different
topic condition, specific snippet / terms were more
frequently focused by participants than common ones.
Figure 8 shows typical heat map when documents ofdifferent topics are presented. It can be seen that theparticipant more gazed at specific snippet (lower part)
than common snippet (upper part).
From the result, it is supposed that participants first
assume whether given document pair relate with same
© 2011 ACADEMY PUBLISHER

topic or not, then examine the assumption by reading thecorresponding information. That is, participants would
carefully read specific terms / snippet when they assume
the documents relate with different topic from each other.
This result suggests participants need different kind of
information according to assigned task. In that sense,
separately providing common/specific information(snippets and terms) as employed in the paper is effective
for supporting the task of similarity judgment.
3) Viewing behavior specific to terms condition
Figure 9 shows a gaze plot of viewing behavior that is
specific to the case when terms are presented. The figure
shows that the participant frequently switches AOIs
between left-hand and right-hand document areas. Thisbehavior was frequently observed when terms arepresented. On the contrary, as shown in Fig. 10 and Fig.
11, such switching behavior was less observed when text
or snippet is presented.
In order to investigate this tendency in more detail, we
calculated the number of “switches”: a switch occurs
when participants changed AOI from left-hand to righthand documents or vice versa. Table VII shows the average switching frequency that is counted per type of information. It shows that participants most frequently
switched when terms are presented.

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9

V. CONCLUSIONSThis paper investigates the behavior of users judging
similarity of documents. The aim of the investigation is toobtain the hint for minimizing the cost of users judging
similarity of documents, which is an essential task forusers when performing interactive document clustering.
An experiment system was implemented, with using
which 21 test participants were asked to judge the similarity of given pair of documents. Regarding the clue forthe judgment, 3 types of information: original text, snippet, and term, are compared. The judgment accuracy andjudgment time are analyzed, and the result shows that
presenting terms is the best in terms of time cost, whereas
judgment accuracy when snippet or text is presented improved through experience.
The behavior of participants was also recorded by T60
eye-tracking system. Analyzing the recorded data foundtypical viewing behaviors that can be the evidence of thedifference between term and other 2 clues.
Our future work includes the development ofinteractive clustering-based Web search engine. The
obtained result will contribute to realization of interface
that can minimize the user’s feedback cost for document
similarity judgment.

Figure 10. Fewer switches when viewing snippet

ACKNOWLEDGMENTS
This work was partially supported by a grant from
National Institute of Informatics (NII).
REFERRENCE
Figure 11. Fewer switches when viewing text

TABLE VII.
AVERAGE SWITCHING FREQUENCY
Average frequency
STDEV

Text
4.49
3.28

Snippet
7.06
3.54

Term
12.34
6.82

It seems that each term can be examined independently,
whereas participants have to read a sequence of terms
when snippets or original text is displayed. We think suchdifference between terms and others is one of the reasonsthat participants reading terms can judge the similarity of
document more quickly than other condition as shown insection IV.A. On the other hand, the improvement of
judgment accuracy with experience was not observed
when terms are presented. This implies that the context
formed by a sequence of terms is important to grasp thecontents of a document.
Although it is beyond the scope of the paper, the factthat term and snippet have different merits from eachother would contribute to the design of an interface fromMUF viewpoint. That is, both of the similarity judgment
time and judgment accuracy would be improved byhighlighting terms more clearly, such as by using larger
fonts than other terms.

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Minghuang Chen, China, 1983. 2002-2006, Xi’an University of Technology,
China, Bachelor degree. 2008--present,
Tokyo Metropolitan University, Japan,
Master student.
Membership of Japanese Society for
Artificial Intelligence.

Yasufumi Takama, Japan, 1971.
Dr. Eng, University of Tokyo, TokyoJapan, 1999.
He was a JSPS (Japan Society for the
Promotion of Science) Research Fellow
from 1997 to 1999. From 1999 to 2002
he was a Research Associate at
Interdisciplinary Graduate School of
Science and Engineering, Tokyo
Institute of Technology in Japan. From 2002 to 2005, he was an
Associate Professor at Department of Electronic Systems andEngineering, Tokyo Metropolitan Institute of Technology,
Tokyo, Japan. Since 2005, he has been an Associate Professor
at Faculty of System Design, Tokyo Metropolitan University,
Tokyo, Japan. He also participated in PREST (Precursory
Research for Embryonic Science and Technology), JST (Japan
Science and Technology Corporation) from 2000 to 2003. His
current research interest includes Web intelligence, information
visualization, and intelligent interaction.
Dr. Takama is a member of IEEE, JSAI (Japanese Society of
Artificial Intelligence), IPSJ (Information Processing Society ofJapan), IEICE (Institute of Electronics, Information and Communication Engineers), and SOFT (Japan Society for Fuzzy
Theory and Intelligent Informatics).

© 2011 ACADEMY PUBLISHER