The Journal of the Society for Art and Science, Vol. 14, No. 5, pp. 170-179

An exploratory apparel product search interface
adopting shopping psychology
Eriko Koike1)

Takayuki Itoh1) (Member)

1) Ochanomizu University{aco, itot} at itolab.is.ocha.ac.jp

Abstract
Many people (especially women) tend to take a long time for shopping. From the results of our preliminary
questionnaire, we conclude that many female students look many products to find the best product, while
enjoying the shopping processes themselves. This paper presents a user interface for exploratory apparel productsearch inspired by the questionnaire results. Our study supposes that each product is assigned with a pre-defined
keyword, and prepares icons associated to the combination of the keywords. The interface displays various icons
in a display space to show the diversity of the products. The icons are generated in the preprocessing. They aredesigned specific to this study, as the overlay of images representing shapes, colors, and texture patterns of the
products. When a user selects an interested icon, the interface switches the display to a set of images
corresponding to the selected icon, such that the user can visually compare the similar products. It acts real
shopping behavior because we often firstly look over the shops to understand the diversity of products, and thenclose up the particular groups of the products. The interface also estimates the preferences of users from theirselection of icons, and applies an evolutionary computation algorithm which adjusts the selection of icons totheir preferences. This paper introduces a user experiment to demonstrate the effectiveness of the presented
interface, and discusses how the interface adopts to (especially women’s) shopping psychology.

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can freely look at various products based on their preferences or
curious without making explicit queries. This system realizes"Exploratory Search" [17] which supports interactive search processes
for users who have ambiguous requirements on shopping.
We already presented the processing flow of the presented system at
an international conference as a short paper [19]. We provide
additional figures, references and evaluation results in this paper. We
also discuss the adaptation of this interface to women’s shopping
psychology [11].

1. Introduction
Shopping is fun for many people. They, especially women, tend to
take a long time for shopping. Economists mentioned [11] that
following are major reasons of such long time for shopping:[Reason 1:] Requirements of consumers are often ambiguous.
[Reason 2:] Curiosity of consumers is not limited to the target
products which they want to buy at the moment. Many customers
want to large number of products, not limited to ones they demanded.
[Reason 3:] Customers often want to continue finding their bestproduct taking a long time.
[Reason 4:] Customers often enjoy shopping processes themselves.
We conducted preliminary questionnaire to female university
students. We had a question "do you always select particular products
to buy before visiting shops?", and 64% of the subjects answered "no".
We then had a question "what do you do if you find your favorite
products?", and 93% of the subjects answered "I will keep is as a
candidate, but still continue to look for better products". We also had aquestion "what are major purposes of your shopping?", then 71% of
the subjects answered "enjoy the shopping time" or 64% answered"refresh". From these results we conclude that many female students
look many products to find the best product, while enjoying the
shopping processes themselves.
This paper presents an exploratory apparel product shopping
interface. Based on our questionnaire results and above mentioned
analysis of economists, the presented system aims to satisfy thefollowing three requirements:[Requirement 1:] It does not always require exact query so that users
can enjoy shopping with ambiguous needs.
[Requirement 2:] It firstly shows groups of similar products, and thenfocuses to individual products in the selected groups, as users often
look over the inside of the shops first and then focuses to particular
products while the real shopping.
[Requirement 3:] It repeats to display preferable numbers of various
product categories reflecting the behavior of users' browsing processes,
such that users can be interested in various products after looking atlarge number of products.
The system firstly displays various icons representing a certain
combination of categories. The icons are generated in the
preprocessing. They are designed specific to this study, as the overlayof images which represent shapes, colors, and texture patterns of the
products. When a user selects an interested icon, the system displays
images of individual products. Users can press "prefer" or "delete"buttons to input their preferences to particular products. They candisplay a set of preferred products so that they can decide which
products to finally purchase. Users can also replace a set of icons to be
displayed by pressing "renew" button. The system adequately selects a
set of icons to be displayed in the next stage by applying an interactive
evolutionary computation algorithm to reflect behavior of users. Users

2. Related Work
Various issues on on-line commerce business have been discussed as aresult of recent evolution, and actually several survey papers on theseissues have been published. For example, one of the survey papers[18] mentions that "personalization" and "positive experience" areimportant issues. On the other hand, development of on-line
commerce systems based on women's shopping behavior or
psychology has not been significantly discussed. Our study presented
in this paper addresses these issues.
Recommendation is an important and effective technique for electriccommerce and contents viewing. Collaborative filtering [1,9] is one of
the most famous and commercially successful recommendation
techniques. We agree that collaborative filtering would work well forapparel shopping or several other kinds of commerce rather thancontent- or knowledge-based recommendation techniques. However,
it is doubtful for us that such recommendation systems are always
satisfactory for apparel shopping. One reason is that lifecycle ofapparel products tends to be short, and therefore cold start systems
such as collaborative filtering do not always work well. Anotherreason is that customers may not want the systems to recommend theapparel products which were already recommended to other similarcustomers including their friends. Consequently, we concluded todevelop a more visual and interactive system so that users can enjoy along time for shopping, and the system can gradually learn
preferences of the users while their enjoyable time.
Interactive evolutionary computing is an effective technique toquickly learn preferences of the users and reuse the acquired
knowledge to various applications. For example, interactive genetic
algorithm has been applied to online-shopping [7], collaborative
filtering [3] and music recommendation [12]. On the other hand, in
this study we preferred to apply the relevance feedback [17] with
Rocchio's algorithm [13], because we expect this algorithm
continuously recommends slightly different sets of apparel products
over and over. We expect this behavior look similar to that we often
look similar products over and over in our real shopping.
Icon synthesis is an important portion in this research, because we
expect users enjoy shopping while selecting their interested icons.
There have been several techniques for semantics-based icon
synthesis [14] and feature-based icon synthesis [6]. Our icon creation
policy is quite similar to [14]; however, it is more specific to product
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keyword representation.
Rectangle placement problem is important for information
visualization. Various visualization techniques actually applies
rectangle packing, including tree visualization [5], graph visualization[10], and label placement [2]. The technique we applied for the icon
layout [4] has been originally developed for graph visualization; italso has preferable properties for our design of icon layout.

"delete" button for the particular products.
The technique supports two display modes: "icon display mode" foroverview of the variety of product groups shown in Figure 1, and"product display mode" focusing on particular groups shown in Figure
2. This policy is analogous to real shopping: we often look over theinterior of shops to feel diversity of the products, and then focus onparticular groups of products to compare the similarity. Figure 3
illustrates this mechanism. This design policy also satisfies[Requirement 1] and [Requirement 2] described in Section 1.

3. Presented System
This section describes overview and processing flow of the presented
technique. The technique firstly displays a set of icons as shown in
Figure 1. Here, the icons represent certain combinations of keywords
related to appearance and categories of products. Users caninterchange the set of icons to be displayed by pressing "renew"
button. Repetitively displaying the sets of adequate numbers of
icons, users can enjoy the search of their preferable products, as they
enjoy real shopping by looking at many products over and over.
We prefer to show icons rather than real product photographs in theinitial display. This design policy relates to [Reason 1]. We aimed tostimulate abstract interests to particular product categories, notparticular products themselves, in the initial display. It is especiallyeffective when requirements of users are ambiguous and therefore
abstract interfaces are more effective. It also relates to [Reason 4]
because cute or funny icon design may stimulate enjoyableness of theusers.

Figure 2. Display of products corresponding to the selected icon.
Diversityof distant products

I' m attracted
by those!

Similarityof near products

Which productis the best?

Figure 3. Diversity and similarity of products in a shop.
Figure 1. Snapshot of our interface featuring icon display.

3.1 Icon creation
Here, we suppose the combination of the above mentioned various
keywords are also assigned to the all products. Our implementation
divides all products into a certain number of groups according to the
combinations of keywords, and associates icons to the product groups
one-by-one. When a user clicks one of the icons, the techniqueswitches to display images of individual products belonged to theparticular group corresponding to the clicked icons, as shown in
Figure 2. Users can focus to each of the images, and press "prefer" or

We suppose owners of this system prepare a set of icon images
representing the combinations of predefined keywords. Figure 4
shows an example of icon creation process. The icon in this figure
represents the combination of keywords, "T-shirt", "red", and "dot".
This design policy brings unified impression through the application,and intuitive understanding of product groups corresponding to the
combinations of the keywords. We suppose that keywords are defined
mainly based on shapes, colors, and texture patterns of the products,
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because many customers of apparel products mainly select the
products based on their visual design.

Tops(T-shirt)

Red

Dot

[Selection of keywords:] The right side of the window shown in
Figures 1 and 2 features a set of checkboxes to select various
attributes including gender of the user, type, price, size, and categoryof the products. Evolutionary computation algorithm described in
Section 3.4 selects icons matched to the user-selected keywords. If theuser selects no keywords, the algorithm selects icons to be displayed
from the every icons. Even if the user does not have any specifictarget products and therefore does not press any checkboxes, he/shecan enjoy the shopping because the system shows arbitrary sets of theicons.
[Icon selection and product display:] When a user clicks a particular
icon by pressing the left button of the mouse, the system displays a setof products in the group corresponding to the clicked icon, as shownin Figure 2. The user can return to the icon display by pressing"return" button featured at the top of the Figure 2.
["Prefer" and "Delete" buttons:] While displaying a set of icons asshown in Figure 1, users can select particular icons by pointing the
cursor and pressing the right button of the mouse. Similarly, users canselect particular products while displaying them as shown in Figure 2.
Then, the system shows a small dialog window featuring "prefer" and"delete" buttons. When the user presses the "prefer" button, the icons
those assigned keywords are similar to the selected products are
preferentially displayed later. On the other hand, when the user presses
the "delete" button, the icons those assigned keywords are similar tothe selected products are not often displayed later. These buttons
contribute to satisfy [Reason 3] because this mechanism makes easier
to continue shopping while focusing on preferable product categories.

Tops/Red/Dot

Figure 4. Example of icon creation.

3.2 Icon layout
This technique displays adequate number of icons in a display space
as shown in Figure 1, where the icons are selected by an evolutionary
computation algorithm described in Section 3.4. We expect users canbe interested in wide range of products by looking at the variety oficons. On the other hand, we need to keep the number of displayed
icons appropriately, because users may be confused or dithered if too
many icons are displayed at the same time.
Our implementation represents the numbers of products
corresponding to the icons by their sizes. Users can intuitively
understand how many products are prepared in each of the productgroups corresponding to the icons.
Here, we suppose the following three conditions for the naturally
looking icon layout results:
1) Place similarly looking icons closer.
2) Optimize display space utility. (=Yield less gaps.)3) Yield no overlaps among the icons.
Here, layout satisfying 1) is especially effective when users want tovisually compare similar product categories. Meanwhile, conditions 2)
and 3) are effective when users want to access to product categories as
many as possible. Also, the condition 2) makes easier to satisfy[Requirement 3] described in Section 1 because the number ofdisplayed icons can be controlled by optimization of display space
utility.
To satisfy the above conditions for icon layout, our implementation
applies a hybrid force-directed and space-filling algorithm [4],originally developed for graph visualization. The former part of thealgorithm connects icons which common keywords are assigned byedges, and supposes attractive forces to the edges. It also supposes
repulsive forces among icons which are not connected by the edges.
As a result of iterative force calculation, similarly looking icons areclosely placed in the display space. The latter part of the algorithm
applies a rectangle packing technique so that display space utility is
optimized while icons do not overlap each other.

Checkbox for
keyword selection
Icondisplay modeLeftbuttonReturnbuttonRightbuttonProduct
display mode

Renew
buttonPreferbuttonDeletebuttonReflect to evolutionary computation
Figure 5. Processing flow with user interface widgets.

3.3 User Interface

3.4 Icon selection algorithm

Our implementation features the following user interface widgets.
Figure 5 shows a processing flow with the widgets.
[Selection of the number of icons:] We featured a menu to select the
number of icons to be displayed.

This system selectively displays a set of icons when a user presses"renew" button, as above mentioned. The displayed icons areautomatically selected by the following algorithm. If a user has certain
preferences to purchase items, the algorithm mainly selects theuser-preferred icons while intensively selecting several other icons so
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that users can be interested in other kinds of items. Meanwhile, thealgorithm gradually learns preferences of users while displaying
variety of icons, because users may confuse if icons are too randomly
selected. Our technique applies an interactive evolutionary
computation algorithm to select preferable sets of icons. This section
calls previously displayed icons "seed icons", and selected icons to be
displayed "derived icons". Moreover, this section formalizes thealgorithm with the following variables:n : Number of keywords.

randomly selects (m-ms) icons and registers to A, such that the
number of icons in A gets m.


If ms is equal to m or larger than m, the system randomly
extracts m icons from S and registers them to A.

Step 2: Display the icons registered in A. Users can freely click theicons, look at the groups of products corresponding to the clicked
icons, and press "preferred" or "deleted" buttons for the displayed
products.

m : Number of icons to be displayed simultaneously.

Step 3: The system applies Rocchio's algorithm [13] which ared  {d1 ,..., d n } : n-dimensional vector consisting of binary

originally applied for Relevance feedback. It renews the value of thevalues representing the assignment of the keywords for a
product. Here, d x  1 if the x-th keyword is assigned,otherwise d x  0 .


preference vector q i by applying the following equation, where idenotes how many times the user pressed "renew" button.

q i  q i 1 e  {e1 ,..., e n } : n-dimensional vector consisting of binary
values representing the assignment of the keywords for an icon.

 d   d    ed  Did  Die  Ei

Here, e x  1 if the x-th keyword is assigned, otherwise

D  i : Set of products which the user pressed "preferred" button after

ex  0 .

the user pressed "renew" button i times.

D  i : Set of products which the user pressed "deleted" button afterq  {q1 ,..., q n } : n-dimensional vector representing thethe user pressed "renew" button i times.

preference of a user for each keyword. This section calls thisE  i : Set of displayed after the user pressed "renew" button i times.
 : Constant value (   1.0 )value "preference vector". Here, q i denotes the value of thepreference vector q when a user pressed "renew" button i times.

The system preferentially selects the icons which similar sets of

A: The set of icons currently displayed.

keywords are assigned as the products the user pressed "preferred"

S: The set of preferentially displayed icons.

button, by renewing q i by the following equation. Conversely, the

C: The set of seed icons.

system does not willingly selects the icons which similar sets of
keywords are assigned as the products the user pressed "deleted"

The processing flow for the icon selection is described below. Here,
Steps 1 to 6 are executed when a user presses "renew" button.

button.
Step 4: Insert the icons, those assigned keywords are entirely same as
the products which user pressed "preferred" button, to the set of seedicons C.

Step 0: Initialize the preference vector q 0 by the following
procedure.
Step 5: Specify n s , the number of icons derived from a seed icon

Reset values of q 0 as 0 if it is for the first time for the user toregistered in C. Our current implementation specifies n s as a

use this system.
constant number proportional to the number of currently displayed

Inherit the previous values if the user has a history of using thisicons in A.
Step 6: Execute the following procedure for each seed icon c  C .

system.
Step 1: Let the set of seed icons C and icons currently displayed A
empty.
If the user selected particular keywords: The system firstly select m/3
icons, and insert the selected icons to S. Also, the system add
pre-defined values to the particular dimensions corresponding to theuser-selected keywords of the preference vector q.
Selection of the icons to be registered in A:
If ms, the number of icons registered in S is smaller than m, thesystem registers all the icons in S to A. Then, the system(1)

Copy the e x value of c, and collect the set of keywords thosecorresponding values e x for c is zero.

(2)

Select the y-th keyword from the set of keywords collected in(1), and let e y  1 . Here, let the possibility to select the y-th
keyword as proportional to the corresponding value of thepreference vector q y . Then, let the values e z zero, if the z-th
keyword or the y-th keyword selected in (1) is exclusively
assigned to the icons.

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Figure 6. Evolutional computation for icon selection.
(3)

feeling close to the real shopping. The algorithm also contributes tosatisfy [Reason 3] because this mechanism makes easier to continue
shopping while focusing on preferable product categories.
On the other hand, this system often mixes small number ofrandomly selected icons, if ms is smaller than m in Step 1. Keywordsof the randomly selected icons are not always quite common with the
keywords of the products which the user pressed "prefer" button. Thismechanism contributes to mainly show users' preferred groups of
products, while sometimes show wide variety of other types of
products. We expect this mechanism makes users to satisfy theircapricious curiosity to the variety of products, without losing theirambiguous preferences or targets.

Specify the icon corresponding to e z values specified in (2).
This process specifies an icon which one of those assigned
keywords is different from c. Register the specified icon to theset of derived icons S.

(4)

Repeat (2) to (3) n s times, for each derived icon in S.

(5)

Repeat (1) to (4) for each seed icon in C.

Figure 6 shows the overview of the presented icon selection
algorithm. In the upper-left portion of Figure 6, icons bordered in red
denote the seed icons, and icons in i=k+1, connected from the seedicons in i=k, denote the derived icons.
This algorithm preferentially selects icons those assigned keywords
are common with the products which user pressed "prefer" button.
This system repeats the selection of the next icons as the repetition ofevolutionary computation processes. This process selects icons those
one of the assigned keywords are different from currently displayed
icons. This mechanism is close to the feeling of real shopping,because usually apparel shops arrange similar products closely. We
continuously look at similar products which have different colors or
textures in apparel shops. We expect this mechanism brings users the

4. ExperimentThis section introduces our user experiments which demonstrate theeffectiveness of the presented system. All participants of theexperiments were female university students.

4.1 Effectiveness of user interfaces
We asked the participants to browse the presented system and an
existing EC (electric commerce) Web site for 15 minutes respectively.
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We did not specify any purposes of the test to the participants; we
asked them to browse products as they preferred. We recorded theiraccesses, and counted the numbers of the browsed products. Also, we
asked them to comment how they felt during the browsing of products.
Section 4.3 introduces comments of the participants.
We aimed to design the user interface to make users browse large
number of products, based on [Reason 2] in the first paragraph of
Section 1. Therefore, we counted the numbers of products browsed byten participants. Figure 7 shows the result where the x-axis denotes thenumber of browsed products, and the y-axis denotes the names ofparticipants (described as A to J). This result denotes that many
participants (especially A, E, F and I) browsed significantly larger
number of products while using our system rather than using theexisting Web site, as we aimed to develop the system so that users canenjoy to look at many products.

existing systems. On the other hand, the proposed system can show
variety of product categories even if the targets of the users areambiguous and therefore it is difficult to specify appropriate keywords.
We suppose our system is especially effective in such cases.
Though this result demonstrates the effectiveness of the presented
system that users were interested in variety of products, we need to
discuss that the result might depend on preferences of icon design ofthe participants. We heard that several participants clicked particular
icons because they felt these were cute. On the other hand, several
participants skipped to click particular icons because they did notprefer colors of them. We found that icon design was an importantfactor for the effectiveness of the presented system, and needed to
discuss how to customize the icon design based on preferences ofusers.
J

J

I

I

H

H

G

G

F

F

E

E

D

D

C

C

B

Proposed
Existing

B

Proposed
Existing

A

A

0

0

100

200

300

400

500

5

10

15

20

Figure 8. Numbers of keywords assigned to at least one of the
products which the participants pressed "prefer" button, divided
by the number of keyword-related operations.

Figure 7. Numbers of browsed products by the participants.
We also counted the numbers of keywords assigned to at least one ofthe products which the participants pressed "prefer" button. Here, we
aimed to design the user interface to make users browse variety of
products without querying with specific keywords. Therefore we
divided the numbers of keywords assigned to the preferred productsby the number of keyword-related operations. Figure 8 shows theresult, where the y-axis denotes the names of participants similar to
Figure 7. This result denotes that the participants are interested in
more variety of products while using the presented system rather thanusing the existing Web site.
Here, several participants (especially A and E) had great differencesof results between proposed and existing systems in Figures 7 and 8.
We suppose such differences can be observed when the purpose of
product browsing is really ambiguous. In such situation, it may bedifficult to query products with appropriate keywords while using the

4.2 Effectiveness of icon selection algorithm
Next, we prepared two versions of the presented system. One of them(version A) featured the icon selection algorithm as presented in thispaper. The other (version B) featured a random icon selection
mechanism. We evaluated if the presented icon selection algorithm
worked as we expected to mainly show users' preferred products,
while sometimes show wide variety of other types of products.
In this experiment we focused how participants were interested in
products corresponding to the randomly selected icons, not the total
number of preferred products, while playing with the presented
system. Also, we focused how they found larger number of preferred
products if they did not have any particular target products.
Experiment supposing particular targets.
We asked the participants to play with the two versions of the176

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presented system for 15 minutes respectively, after selecting several
keywords. Also, we asked them to comment how they felt during thebrowsing of products.
Tables 1 and 2 shows the results of the five participants. In the tables,"person" denotes IDs of the participants, "renew" denotes thefrequency of pressing "renew" button, "assigned" denotes the numberof products which user-selected keywords are assigned and theparticipant pressed "prefer" button, and "other" denotes the number of
products which no user-selected keywords are assigned and theparticipant pressed "prefer" button. This result denotes all theparticipants were interested in the products which no user-selected
keywords were assigned, and pressed "prefer" button with some of the
products, while playing with the version A. From this result, it iseffective to display icons which no user-selected keywords are
assigned, because the users may be interested in such unexpected
products. On the other hand, the participant E pressed "prefer" button
for five products which no user-selected keywords were assigned
while using the version B. We need to discuss why our icon selection
algorithm was not very effective for the participant E comparing withthe random icon selection.

"prefer" button. Figure 9 shows the ratio between the numbers of
products which ten participants pressed "prefer" button and thenumbers of browsed products, where the y-axis denotes the names ofparticipants similar to Figures 7 and 8. This figure denotes that eightof the participants archived the larger ratios while using the version A.
It demonstrates that users can find larger number of preferable
products while applying the presented icon selection algorithm, even
if users do not have particular target products and therefore they do
not select any keywords.

Table 1 Frequency of renew and number of preferred products
while using the version A of the presented system.
personrenew
assigned
other

C

A

3

6

3

A

B

1

12

2

C

2

12

1

D

3

21

3

E

5

3

4

J
I
H
G
F

1

3

0

B

1

17

0

C

2

10

0

D

4

18

0

E

6

7

5

VersionA

D

B

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Figure 9. Ratio of numbers of products which "prefer" button
was pressed by the participants.

Table 2 Frequency of renew and number of preferred products
while using the version B of the presented system.
personrenew
assignedotherA

VersionBE

4.3 Feedback
Several participants mentioned that they felt "I liked these products
though I did not expect such ones", or "I was going to miss such
attractive products" during the experiments. They enjoyed theunexpected discoveries and satisfactions while using the presented
system. Or, another participant mentioned her experience that she
firstly browsed green products based on her curiosity and thenconfirmed that she did not want the green ones. The presented systemenables such behavior easier.
After the experiment introduced in Section 4.2, we received many
positive comments for the version A. Many participants mentioned "I
was not tired by this system because it suggested variety of products"
or "the system motivated me to browse products we usually did not
focus on." On the other hand, a participant mentioned "I was surprised
because several icons I did not prefer were displayed". We would liketo solve this problem by developing additional indication whichsuggests users to browse products corresponding to the icons selectedby the algorithm which no user-selected keywords are assigned.

Experiment without supposing particular targets.
We also asked the participants to play with the two versions of thepresented system without selecting any keywords. Here, it is difficult
for the presented system to precisely select user-preferred icons in ashort experimental test without selecting any keywords, because thissystem is based on a cold start algorithm. Therefore, we asked them tofirstly play with the two versions for 15 minutes. We recorded thepreference vector q, and then asked them to play with the two
versions for 15 minutes again. The system loaded the preference
vector when the participants started playing with it again.
We counted the numbers of products which the participants pressed
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4.4 Discussion with Shopping Psychology

apparel shopping, but the presented mechanism is not limited tofemale users and apparel products. We would like to have moreexperiences with male participants and other kinds of products.

Our original motivation for this research was to satisfy the psychology
of women's shopping activities. There have been many publications
on women's shopping activities [6,11,15,16]. We especially remarked
the conclusions described in [11] regarding characteristics of women's
shopping, summarized as [Reason 1] to [Reason 4] introduced in
Section 1.
The presented system aims to satisfy [Reason 1] and [Reason 2] bydisplaying various icons associated to various products in a single
display space, and [Reason 3] by providing an interface to iteratively
display sets of icons and select interested ones. We received many
positive feedback from participants of the user experiments that they
enjoyed to use this system, which corresponding to [Reason 4].
The reference [11] also mentions the following women’s
preferences:[Reason 5] Many women tend to select products according to abstract
imaginations rather than detailed pictures or specifications.
[Reason 6] Many women tend to prefer wide and planar designs
rather than depth-effective 3D designs.
Our interface firstly shows abstract icons, not real pictures, which are
expected to stimulate abstract imaginations, in a 2D display space.
This design policy is close to [Reason 5] and [Reason 6].
The system presented in this paper does not limit the customers only
to women; however, we expect the system is especially preferred by
women based on the above discussion.

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This paper presented an interactive exploratory search system
featuring a relevance feedback algorithm, and its implementation forapparel shopping. Our study is inspired by the research of thepsychology and behavior of women's shopping. The presented system
firstly displays a variety of icons corresponding to the groups ofsimilar apparel products, so that users can keep wide range of interests.
It then displays the photographs of the products associated with theicons selected by the users. This two-step user interface is analogous
to diversity and similarity of the products in a shop. This
representation is close to our real shopping: we often look over the
shops to understand the diversity of the products, and then close up to
particular groups of similar products to narrow down the products wewant to purchase. Moreover, the system automatically selects theicons to be displayed in the next stage, by applying a relevance
feedback algorithm reflecting the behavior of users. Consequently, thesystem mainly suggests groups of products based on users'
preferences, while it sometimes suggests other kinds of products tosatisfy the capricious curiosity of the users. We also expect users canenjoy a long time to select their favorite products while the systemquickly learn their preferences to solve the cold start problem of therecommendation systems.
This paper introduced experiences with female participants and[8] P. Kolhoff, J. Preub, J. Loviscach: Music Icons: Procedural
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231-238, 2008.
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ISBN-978-4-569-69428-3, 2007.
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The Journal of the Society for Art and Science, Vol. 14, No. 5, pp. 170-179[14] V. Setlur, C. Albrecht-Buehler, A. Gooch, S. Rosoff, B. Gooch,Semanticons: Visual Metaphors as File Icons, Computer
Graphics Forum, 24(3), 647-656, 2005.

小池 恵里子2013 年お茶の水女子大学理学部情報科学科卒業．2015 年お茶の水女子大学大学院人間文化創成科学研究科理学専攻情報科学コース博士前期課程修了．インタラクションと推薦アルゴリズムに興味をもつ．

[15] S. L. Taylor, R. M. Consenza, Profiling later aged female teens:mall shopping behavior and clothing choice, Journal ofConsumer Marketing, 19(5), 393-408, 2002.
[16] H. R. Woodruffe, Compensatory consumption: why women go
shopping when they're fed up and other stories, Marketing
Intelligence and Planning, 15(7), 325-334, 1997.
[17] R. Yong, T. S. Huang, M. Ortega, S. Mehrotra, Relevance
feedback: a power tool for interactive content-based image
retrieval, IEEE Transactions on Circuits and Systems for Video
Technology, 8(5), 644-655, 1998.

伊藤 貴之
1990 年早稲田大学理工学部電子通信学科卒業．1992 年早稲田大学大学
院理工学研究科電気工学専攻修士課
程修了．同年日本アイ・ビー・エム(株)入社．1997 年博士(工学)．2000 年米国カーネギーメロン大学客員研究員．2003年から2005年まで京都大学
大学院情報学研究科COE研究員（客員助教授担当）．2005 年日本アイ・
ビー・エム(株) 退職，お茶の水女子
大学理学部情報科学科助教授．2011
年同大学教授，同大学シミュレーシ
ョン科学教育研究センター長，ACM,
IEEE Computer Society, 芸術科学会，
他会員．

[18] L. Zhou, L. Dai, D. Zhang, Online shopping acceptance model a critical survey of customer factors in online shopping, Journalof Electronic Commerce Research, 8(1), 41-62, 2007.
[19] E. Koike, T. Itoh, An interactive exploratory search system foron-line apparel shopping, International Symposium on Visual
Information Communication and Interaction (VINCI’15),
103-108, 2015.

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