DEIM Forum 2014 B9-3

An E-commerce Recommender System Based on Degree of Specialties in
Online Shops
Motoki ZAIZEN† , Daisuke KITAYAMA†† , and Kazutoshi SUMIYA†† the School of Human Science and Environment, University of Hyogo
1–1–12 Shinzaikehoncho, Himeji, Hyogo, 670–0092 Japan†† Faculty of Infomatics, Kogakuin University
1–24–2 Nishi–shinjuku, Shinjuku, Tokyo, 163–8677 Japan
E-mail: †nc10k064@stshse.u-hyogo.ac.jp, ††kitayama@cc.kogakuin.ac.jp, †††sumiya@shse.u-hyogo.ac.jp
Abstract Use of online shopping sites, such as Amazon and Rakuten, has increased in recent years. Many shops
participate in these sites. The categories of shops represent various intended uses for listed items. For example,
a flashlight is often used for camping or emergency items, so some shops use a category such as “Outdoors” or“Emergency Supplies” for that item. In this paper, we aim to build a recommender system for specialty shops
based on the viewpoints of items browsed by users. We first extract viewpoints of browsed items by using category
structures of online shops. Through this, we analyze the category structures and selection of goods to determine
specialty shops.
Key words Online shopping, A recommender system, Category structures

1. Introduction

ticipating shops to infer viewpoints among browsed items.
Speciﬁcally, we use parent categories having child categories

Use of online shopping sites, such as Rakuten ichiba andwith browsed items in participating shops, because theseAmazon has increased in recent years. These sites have cate-categories might represent intended uses of items. For ex-

gory structures for classifying items based on their intended

ample, when the browsed items are ﬂashlights and retort-

uses. For example, a ﬂashlight used for camping has the cat-packed food and there are parent categories “Outdoor Gear”

egory “Outdoors.” Online shopping sites recommend various

or “Emergency Supplies” having these items in participat-

items based on users’ item browsing histories, using a Col-ing shops, we infer that the viewpoints among these browsed

laborative Filtering method. However, in this method, otheritems are outdoor leisure and disaster preparedness. Then,items are often recommended from the same category as thewe analyze category structures in participating shops, in or-

category of an item browsed by a user, but items have view-

der to determine specialty shops based on these viewpoints

points.

and recommend these specialty shops. Figure 1 shows an

Many shops participate in online shopping sites. The sites

example of a recommendation. A user has browsed “Flash-have not only their own category with all items but also

light” and “Retort-packed food.” We present parent cate-many speciﬁc category structures in the participating shops.

gories “Outdoor Gear” and “Emergency Supplies” as view-

These categories represent some of the intended item uses.

points for these items. Then, we also present child categories

In this work, we recommend participating shops and items

such as “Outdoor Gear > Camping” or “Emergency Supplies

in shops based on viewpoints for browsed items, considering> Foods” under the parent categories. Choosing a category,these shops’ viewpoint specialties. A user has a purpose ina user can browse recommended items suitable for the user’s

browsing items. Using speciﬁc category structures in partici-

purposes.

pating shops, we assume intended browsed item uses. For ex-

2. 2 Related Work

ample, we recommend specialty shops having the categories

Kato et al. [1] and Duc et al. [2] proposed methods for“Outdoors” and “Emergency Supplies” for ﬂashlights.

2. Our Approachsearching objects based on the relational similarity between
words from their emergence distribution on the Web. Thesemethods are similar to our work in that they use relations

2. 1 A Recommender System for Specialty Shops

among objects, but our method diﬀers in extracting relations

In this work, we use speciﬁc category structures in par-among objects based on online shops’ category structures.

Browsed items

MagLiteST3D016 3-D
Cell LED
Flashlight, Black

Flashlight

S & B Curry
Golden Retort
Medium Hot,
8.1 Ounce

Retort-packedfood

Outdoor Gear

CampingCamp KitchenCamp Chef Dutch Oven /Rayovac Sportsman
Camp Table - 16IN x
LED Lantern (SE3DLN)
38IN

Coleman Instant Tent
4 PersonEmergency
Supplies

Sleeping Bags

Foods

Survival Kits

Fire Safety

Slumberjack Latitude
20 Degree
SyntheticSleeping
Bag

Datrex Emergency
Survival Water Pouch(Pack of 64), 125ml

Sony ICF38
Portable
AM/FM Radio (Black)

Kidde FA110 Multi
Purpose Fire
Extinguisher 1A10BC

…

…

…

…

Ambient Weather
Emergency Solar Hand
Crank AM/FM/NOAA
Digital Radio, Flashlight,
Cell Phone Charger

Emergency
Fire Blanket Kit…

Coleman Willow Creek House Retort Curry
KOKUMARO Curry
Retort Medium Hot 210g Warm-Weather Scoop Sweet (net weight 200g)
Sleeping Bag(Import from Japan)…
Figure 1 An E-commerce Recommender System based on Degree of Specialties in Online
Shops

Seki et al. [3] and Cao et al. [4] proposed a method for recommending suitable items for a user’s context. It is similar

3. 1 The Degree of a Shop’s Specialty Based on aClassification Method of Items

to our work in recommending suitability for a user’s view-

We consider an item classiﬁcation method to determine thepoint, but our method diﬀers in considering specialties todegree of shop specialty. Shops having detailed categories forrecommending shops based on a user’s viewpoint.

classifying items and using the categories properly are spe-

Rakuten ichiba [5] ranks participating shops according tocialty shops. The degree based on the classiﬁcation method

opening day, number of items, and number of reviews. How-

C Score(X, i) is calculated using the following expression:

ever, that site does not consider shop specialties.

3. A Method for Determining Degrees of
A Shop’s Specialty Based on a Viewpoint
In this section, we explain methods for calculating degrees
of a shop’s specialty in regard to viewpoints among browsed
items. We deﬁne a shop’s specialty as the result of a calculation using an item classiﬁcation method, item selection, andthe main target genre of items in the shop.

C Score(X, i) = α × Detail(X, i)+(1 − α) × U nif ormity(X, i)(1)where the function Detail returns a degree of detail of acategory structure in a shop i based on browsed items X, andthe function U nif ormity returns a degree of detail of using
the category structure properly. Detail and U nif ormity ofshop i based on items X are calculated as follows:
Detail(X, i) = W (X, i) × D(X, i)(2)category structure of “C1 ” with width four and depth three.

Shop A

Shop A has more uniformity in quantities of items belonging

C1
C2

to categories than shop C.

C3

3. 2 The Degree of a Shop’s Specialty Based on

C4

Selection of Items

C5

C6

C7

C8

C9

C10

I1 I2
I3 I4

I5 I6
I7 I8

I9 I10I11 I12I13 I14I15 I16I17 I18I19 I20I21 I22I23 I24

We consider selection of items to determine a degree ofshop specialty.

Shops with a large range of items are

specialty shops.

The degree based on selection of items

S Score(X, i) is calculated using the following expression:

Shop BC1
S Score(X, i) =

C2

β×

C5

C6

C7

C8

I1 I2
I3 I4

I5 I6
I7 I8

I9 I10I11 I12I13 I14I15 I16

β=

C4

C5

C6

C7

C8

C9

C10

I1 I2
I3

I4 I5
I7 I6
I9 I8
I11 I10I12

I13 I14I15 I16I17 I18I19 I20I21 I22I23 I24γ=

Rare (X, i) −Rare (X, n)m∑m
Cover (X, i) >
=

−1

n=1

)2

)2
(5)

Cover(X,i)

)

m

(6)

(other)

(


 1


n=1

(


 1


Cover (X, n)
m

n=1

∑m

(

C1
C3

Cover (X, i) −+γ ×Shop C

C2

∑m

(

C3

∑m
Rare (X, i) >
=

−1

n=1

Rare(X,i)

)

m

(7)

(other)where the function Cover returns a degree of the quantities of all items, and the function Rare returns a degree of
selection of hard-to-ﬁnd items of a point of view based on

Figure 2 Classification by category structures based on viewpoints

browsed items X in shop i. Cover and Rare of shop i basedon items X is calculated as follows:

where W and D are, respectively, the number of last cate-| G(X, i) |Cover(X, i) = ∪m
| n=1 G(X, n) |(8)gories of shop i’s category structure and the number of layers
in the category structure of a parent category with items X.

where G is a set of items of a viewpoints based on items
X in shop i. The function Cover returns the ratio of thenumber of G of a viewpoint based on itemsX in shop i to1
U nif ormity(X, i) =
1+σ

(3)

v
u p (
∑p
)2
u1∑
C (X, i, l)
l=1t
σ=
C (X, i, l) −
p

l=1

p

the number of G of the viewpoint based on itemsX in all
shops. Intuitively, Cover means the degree of quantities of
items of a viewpoint based on browsed items X.

(4)where C is the number of items belonging to category l.
This category l is one of last categories of shop i’s parent

Figure 3 shows examples of shops’ degree of specialty basedon Cover. Because Shop D’s “C1 ” category has more items
than Shop E’s “C1 ” category, Shop D is more specialized fora viewpoint as “C1 ” based on Cover than Shop E.

category structure with items X. σ is a standard deviation of the number of items belonging to end categories ofRare(X, i) =U nif ormity means the degree of uniformity in quantities ofof a shop’s degree of specialty based on the item classiﬁ-R(X, o)(9)o∈G(X,i)shop i’s parent category structure with items X. Intuitively,
items belonging to end categories. Figure 2 shows examples∑{
R(X, o) =|S(X∪o)|− log( |S(X)| )−1e(| S(X ∪ o) |<= δ× | S(X) |)0(other)(10)

cation method. Shop A’s category structure of “C1 ” with

where G is a set of items of a viewpoint based on items X

width six and depth three is more detailed than shop B’s

in shop i. S is a set of shops having items X and item o theShop D

Shop E

Shop F

Shop G

Shop H

C1

C1

C1

C1

C1

C2

C3

C2

C3

C2

C5

C6

C7

C8

C5

C6

C7

C8

I1 I2

I5 I6

I9 I10I13 I14

I1 I2

I5 I6

I9 I10I13 I14

I3 I4

I7 I8

I11 I12I15 I16

I

1

I

C3
2

I3 I4

I5

C2

C3

C2

C3

I1 I2

I3 I4

I1 I2

I3 I4

Figure 4 Rarity of items

Figure 3 Quantity of items based on a viewpoint

trast, Shop maxshare, Shop royal3000, etc. have a low degree
same as one of G(X, i). Using S , we determine an item o ’s
rarity R(X, o). R returns a item o ’s rarity. When numberof S(X ∪ o) is δ (0.0 <=δ<= 1.0) of number of S(X) and over,R(X, o) is 0.
Figure 4 shows examples of items’ rarity. Because items I1 ,2

3

4

I , I and I belong to Shop A, Shop B, and Shop C, they are
not rare. However, item I5 is rare, because it belongs only
to Shop A. Intuitively, Rare means the degree of selection ofroyal3000’s category structure of “Emergency Supplies” areeach composed of only one category, so these shops are notspecialty shops in terms of the classiﬁcation method. Shopbousaianshin has the most detailed category structure. However, this shop could not deal eﬀectively with the category
structure. Therefore, this shop is not a specialty shop in
terms of the classiﬁcation method and its low degree of
C Score is reasonable.

hard-to-ﬁnd items that most shops do not have.
3. 3 The Degree of a Shop’s Specialty Based on

Shop bousaianshin has a high degree based on selection of
goods ( S Score ), and its Cover is very high. Thus, thisthe Main Target
We consider that all of a shop’s items match a viewpointof browsed items. Shops having only items of a viewpointof browsed items are specialty shops. The degree based on
the main target of a shop is calculated using the following

shop has enormously many items based on the viewpoint“Emergency Supplies.” In addition, selection of hard-to-ﬁnd
items (Rare) in this shop is very high. Similarly, because
Shop mitsuyoshi, Shop r-kojima and Shop bousaiss have relatively many items based on the viewpoint, these shops also

expression:log | G(X, i) |P recision(X, i) =log | N (i) |of C Score. Shop maxshare’s category structure and Shop

have high degrees of Cover and Rare. It may be suspected(11)that Rare tends to be high if Cover is high. However, this isan undesirable outcome. If a shop has many items, the de-where G is a set of items of a viewpoint based on items X ingree of Rare must be low when the shop does not have many

shop i. N is a set of items in shop i. The function P recision

hard-to-ﬁnd items. We suspect that the cause of the problem

returns the ratio of the number of G to the number of N .

is a method of determining whether a item in a shop is had

4. Examples of Calculating Shops’ Specialties Based on Viewpoints

other shops. The Rakuten Item Search API can not retrieve
JAN (Japanese Article Number). Instead of using JAN, we
use item names qualiﬁed by each shops to determine whether

We calculated the degree of specialty of shops in
rakuten.co.jp based on the three viewpoints “Emergency

a item in a shop is had other shops. Therefore, accuracy ofthe method is low. We need to modify the method.

Supplies,” “Outdoor,” and “Kitchen & Dining” (see TABLE

Shops having a high degree of P recision are Shop bousaiss,

1). We collected 50 shops to each viewpoints by using the

Shop bouhanbousai, Shop bousai-web, etc. All items in these

The Rakuten Item Search API In the experiment, we cal-shops are of the viewpoint “Emergency supplies,” so they are

culated the degrees of these shops’ specialty based on view-

shops as a whole targeting emergency supplies. Therefore,

points (see TABLE 2, TABLE 3, TABLE 4). We present

we believe that the P recision values of these shops are rea-

the calculation results in TABLE 5, TABLE 6 and TABLE

sonable.

7, showing Detail, U nif ormity, Cover , Rare, P recision,
C Score and S Score.

Results of calculation by our method shown by TABLE 6
and TABLE 7 denote the same tendency of results shown

TABLE 5 shows that Shop anzenlif and Shop bousaikan

by TABLE 5. Thus, we conﬁrmed that our method to cal-have high degrees of specialty based on the classiﬁcation

culate the degree of specialty of shops based on a classiﬁ-

method (C Score). Because these shops have a relatively de-cation method of Items (C Score) and a main target of atailed category structure of “Emergency Supplies” and deal

shop (P recision) could determine specialty shops. On theeﬀectively with it, these shops are specialty shops in terms

other hand, our method to calculate the degree of specialtyof the classiﬁcation method. Therefore, it is believed thatof shops based on selection of items (S Score) could not de-

the C Score values of these shops are reasonable. In con-

termine specialty shops. However, it may be suspected that
S Score could determine specialty shops if not considering

Table 1 Experimental data: shop data

hard-to-ﬁnd items (Rare).

Viewpoint

The number of shops

Emergency Supplies

50

In this paper, we proposed a method for determining de-Outdoor50

gree of shops’ specialty based on a viewpoint extracted by

Kitchen ＆ Dining

50

5. Conclusion

using category structures of online shops to build a recommender system for specialty shops based on viewpointsof items browsed by users. In addition, to verify the ourmethod, we calculated the degree of specialty of shops in
rakuten.co.jp based on the viewpoint ”Emergency Supplies,”

Table 2 Experimental data: shop data based on “Emergency
Supplies”“Outdoor,” and “Kitchen & Dining”
Shop’s Name

As future work, we intend to repeat the experiment after
we modify a method for determining whether a item in a
shop belongs to other shops in order to verify the method
for calculating Rare. Then, we need to evaluate the usability of the recommendations of specialty shops determined byour method to conﬁrm that it can match user viewpoints.

Acknowledgment
This research was supported in part by a Grant-in-Aid forYoung Scientists (B) 24700098 from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.

References[1] Kato, M., Oshima, H., Oyama, S., Katsumi, T.: Object
Name Search from the Web Based on Relational Similarity.
In: IPSJ Transactions on Databases (TOD), Vol.2, No.2,
pp.110-125, 2009.
[2] Duc, N.T., Bollegala, D. and Ishizuka, M.: Exploiting Relational Similarity between Entity Pairs for Latent Relational
Search. In: IPSJ Transactions on Databases (TOD), Vol.52,
No.4, pp.1-13, 2011.
[3] Seki, S., Nakajima, S. and Zhang, J.: Information Recommendation System Considering Users’ Contexts of Using
Items. In: The 3th Forum on Data Engineering and Information Management. B1-1, (2011) (in Japanese).
[4] Cao, Y. and Li, Y.: An intelligent fuzzy-based recommendation system for consumer electronic products. In: Expert
Systems with Applications Volume 33, Issue 1, July 2007,
Pages 230-240.
[5] Rakuten ichiba, http://www.rakuten.co.jp/.
[6] Zaizen, M., Kitayama, D., Sumiya, K.: An E-commerce
Recommender System Based on Degree of Specialties in
Online Shops. In: The International MultiConference of Engineers and Computer Scientists 2014 (IMECS2014), Hong
Kong, March 12-15, 2014, pp.492-496

The category
The category
The number of item
The number of items based on the viewpoint structure depth structure widthjapan-l

5293

78

2

3

anmakuya

1149

1097

5

108

anzenlife

405

403

5

146

be-kan

714

707

5

173

bousaianshin

13324

13286

5

194

bousaikan

1190

259

5

70

bousaiss

441

441

3

48

ganpon

511

433

5

75

saibou

487

238

4

77

wowsystem

914

108

2

7

bouhanbousai

14

14

2

8

bousai-web

237

237

3

53

gekiyasukaguya

6805

38

2

8

maxshare

608

23

1

1

murauchi-denki

236906123

2

5

onestep

33204

82

2

4

royal3000

1527

18

1

1

smile-hg

4648

133

4

9

ecojiji

975

17

3

4

r-kojima

25291

424

3

5

kaumall

52031

215

2

6

your-shop

2313

113

2

12

auc-tokutoku

1218

38

2

5

good-choice

6633

195

2

5

mitsuyoshi

4425

958

5

25

matsukiyo

14879

28

1

1

otakaratuuhan

3508

12

1

1

santecdirect

28939

205

1

1

the-eikoh

936

26

1

1

tsuruha

17413

96

2

1

ouchimawari

1370

11

1

1

mercurys

1873

15

1

1

ecdenxin

14943

12

1

1

exsmile

2419

39

1

1

ssn

2325

4

1

1

okuwa

2199

39

2

4

colecole

615

73

1

1

ksheep1

9530

32

1

1

k-yokohama

2402

35

2

1

wellness-web

18639

101

2

1

ichibankanshop

16058

24

1

1

wide02

4660

143

2

9

neotec-bafa

31789

168

1

1

yamalab

475

37

1

1

e-kitchen

18046

65

1

1

brico

6498

108

2

10

bungu-kaumall

48158

209

2

6

sekichu

12109

89

1

1

varietyshop

2914

22

1

1

sanbless

8914

49

1

1

Table 3 Experimental data: shop data based on “Outdoor”
Shop’s Namekaumall

52031

316

3

12

your-shop

2313

70

2

6

gmart

1508

16

1

1

good-choice

6633

704

3

22

ecdenxin

14943

752

1

1

k-yokohama

2402

151

1

1

ichibankanshop

16058

281

2

4

comroad

41174

324

2

11

wide02

4660

181

2

7

atmall-pumpkin

2541

131

1

1

neotec-bafa

31789

347

2

6

yamalab

475

283

4

27

hikers

212

211

5

56

e-kitchen

18046

82

3

5

echigoya-online-shop

6246

332

1

1

brico

6498

63

2

11

bungu-kaumall

48158

304

4

11

sekichu

12109

162

2

12

narukiya

3726

81

1

1

varietyshop

2914

65

1

1

p-tano

18464

73

1

1

userlife

155

46

4

10

item-land

9120

7

2

1

fieldlife

499

497

7

123

angelcitiz

10090

54

1

1

genkimart

140

1

1

1

nadeshico-ga

7928

234

2

4

sanbless

8914

15

1

1

sunruck-direct

4810

191

2

4

ripistation

51151

280

3

11

tokyo-hyakkaten

707

5

1

1

magimaguu

1647

39

1

1

livinza

4298

2

1

1

japan-l

5293

103

2

3

auc-bigmo

835

98

3

10

cocoroco

3290

153

1

1

basecamp

1492

1485

6

171

toolex

1723

353

2

2

netonya

4434

245

4

22

auc-pleasure0905

1970

51

1

1

fashion-kosen

32912

966

2

8

hab

8519

6288

4

123

marumoto

3698

328

2

11

hows

43469

343

2

11

ap-zakka

41220

340

2

11

nst21

4622

76

1

1

venusta

374

3

1

1

pedalmania

2967

16

1

1

emishop

884

29

1

1

auc-power

Table 4 Experimental data: shop data based on “Kitchen ＆ Dining”

The category
The category
The number of item
The number of items based on the viewpoint structure depth structure width

724

8

2

1

Shop’s Name

The category
The category
The number of item
The number of items based on the viewpoint structure depth structure width

r-kojima

25291

69

1

ecojiji

975

36

2

1
4

kaumall

52031

547

3

18
7

plywood

4711

219

3

auc-tokutoku

1218

97

2

7

good-choice

6633

4339

4

139

matsukiyo

14879

396

1

1

otakaratuuhan

3508

25

2

2

santecdirect

28939

183

2

4

ouchimawari

1370

24

3

4

mercurys

1873

15

1

1

ecdenxin

14943

875

2

2

exsmile

2419

545

3

8

ksheep1

9530

369

3

39

k-yokohama

2402

323

3

13

wellness-web

18639

827

3

19

ichibankanshop

16058

14

2

1

comroad

41174

1102

3

18

wide02

4660

747

4

43

neotec-bafa

31789

185

3

22

echigoya-online-shop

6246

707

3

29

brico

6498

103

2

14

sekichu

12109

329

2

10

narukiya

3726

449

3

10

neoleaf

60945

9118

4

35

varietyshop

2914

77

2

2

hb-navi

9361

4

1

1

nadeshico-ga

7928

861

5

29

sanbless

8914

603

3

21

richell

2822

299

2

9

livingearth

518

62

4

15

sunruck-direct

4810

285

3

20

katuhara

7994

52

2

5

aires

2525

394

4

13

ozshop

4516

1

1

1

ripistation

51151

531

3

17
38

raison-store

1056

343

4

tokyo-hyakkaten

707

2

1

1

joyfullmart

1041

669

5

29

magimaguu

1647

37

2

1

livinza

4298

443

4

30

aandi

845

426

2

2

zakkashop

2714

330

3

32

cherrybell

966

104

3

12

zaka-mmc

4992

951

5

70

dan-dan

1217

97

1

1

rgbw

3701

194

4

26

tubasa55

935

105

3

20

shopmarna

472

102

3

9

japan-l

5293

298

3

9

Table 5 Experimental results:

Degree of specialty based on“Emergency Supplies”

Table 6 Experimental results: Degree of specialty based on “Outdoor”

Detail U nif ormity

Cover

Detail U nif ormity

Coverjapan-l0.00520.44500.0041 0.0054

0.50810.0006-0.0008

kaumall

0.0341

0.06120.0195 0.0529

0.53000.00050.0000anmakuya0.55620.03260.0123 0.0222

0.99340.0045-0.0001

your-shop

0.01070.12400.0040 0.0103

0.54850.0003-0.0016anzenlife

0.75230.09180.0089 0.0119

0.99920.0173-0.0004

gmart

0.00001.00000.0010 0.0027

0.37880.0000-0.0023

be-kan0.8916

0.03490.0079 0.0141

0.99850.0078-0.0004

good-choice

0.06340.04060.0442 0.1264

0.74510.00060.0069bousaianshin

1.00000.0062

0.6642 1.0000

0.99970.00151.3570ecdenxin

0.00001.00000.0430 0.1210

0.68900.00000.0060

bousaikan

0.36020.20640.0107 0.0153

0.78470.0186-0.0003k-yokohama

0.00001.00000.0094 0.0266

0.64460.0000-0.0005

bousaiss

0.1476

0.05870.0222 0.0277

1.00000.00220.0000ichibankanshop

0.00680.14790.0174 0.0441

0.58220.00030.0000

ganpon0.38600.07070.0170 0.0244

0.97340.00680.0000comroad0.02050.00130.0203 0.0251

0.54400.0000-0.0005

Shop’s nameRare

P recision C Score S Score

Shop’s nameRare

P recision C Score S Score

saibou0.31680.10940.0099 0.0126

0.88430.0087-0.0004

wide020.01270.07440.0107 0.0302

0.61540.0002-0.0004

wowsystem

0.01340.07040.0060 0.0106

0.68670.0002-0.0006

atmall-pumpkin

0.00001.00000.0082 0.0109

0.62180.0000-0.0014bouhanbousai

0.01550.46170.0007 0.0006

1.00000.0018-0.0012

neotec-bafa

0.01070.06080.0202 0.0514

0.56420.00020.0000

bousai-web

0.16310.03430.0110 0.0153

1.00000.0014-0.0003

yamalab

0.10440.08690.0103 0.0286

0.91600.0023-0.0004

gekiyasukaguya

0.01550.16280.0021 0.0036

0.41220.0006-0.0010hikers

0.27220.12750.0020 0.0055

0.99910.0087-0.0020

maxshare0.00001.00000.0013 0.0020

0.48910.0000-0.0011

e-kitchen

0.01370.44150.0052 0.0146

0.44960.0015-0.0012murauchi-denki

0.00930.06610.0066 0.0095

0.38890.0002-0.0006

echigoya-online-shop

0.00001.00000.0132 0.0375

0.66420.0000-0.0001

onestep

0.00720.05140.0045 0.0055

0.42330.0001-0.0008brico

0.02050.23230.0038 0.0107

0.47190.0012-0.0016

royal3000

0.00001.00000.0010 0.0013

0.39430.0000-0.0012bungu-kaumall

0.04200.05910.0187 0.0497

0.53020.00060.0000smile-hg

0.0361

0.09450.0074 0.0129

0.57910.0009-0.0004

sekichu

0.02240.08850.0101 0.0277

0.54110.0005-0.0005

ecojiji0.01140.23800.0006 0.0008

0.41170.0007-0.0012

narukiya

0.00001.00000.0051 0.0119

0.53440.0000-0.0014

r-kojima

0.01440.03400.0213 0.0348

0.59670.00010.0000varietyshop

0.00001.00000.0038 0.0098

0.52330.0000-0.0016

kaumall

0.01140.05730.0104 0.0105

0.49460.0002-0.0005

p-tano0.00001.00000.0046 0.0129

0.43680.0000-0.0014your-shop

0.02370.16000.0055 0.0086

0.61030.0009-0.0006

userlife

0.03800.21820.0007 0.0019

0.75910.0021-0.0024

auc-tokutoku

0.00930.19360.0020 0.0033

0.51200.0004-0.0010item-land

0.00101.00000.0002 0.0005

0.21340.0002-0.0025

good-choice0.00930.02460.0108 0.0194

0.59920.0001-0.0002

fieldlife

0.83900.04010.0056 0.0144

0.99940.0084-0.0012

mitsuyoshi

0.12800.02830.0520 0.0802

0.81770.00090.0036angelcitiz

0.00001.00000.0034 0.0084

0.43270.0000-0.0017

matsukiyo

0.00001.00000.0015 0.0011

0.34680.0000-0.0011

genkimart

0.00001.00000.0001 0.0000

0.00000.0000-0.0026

otakaratuuhan

0.00001.00000.0007 0.0009

0.30440.0000-0.0012nadeshico-ga

0.00680.04410.0147 0.0413

0.60760.0001-0.0001

santecdirect

0.00001.00000.0106 0.0188

0.51820.0000-0.0002sanbless

0.00001.00000.0009 0.0022

0.29770.0000-0.0023the-eikoh

0.00001.00000.0010 0.0015

0.47620.0000-0.0011

sunruck-direct

0.00680.16270.0119 0.0306

0.61950.0003-0.0003tsuruha0.00101.00000.0054 0.0095

0.46740.0003-0.0006

ripistation

0.0312

0.05360.0173 0.0455

0.51970.00040.0000ouchimawari

0.00001.00000.0005 0.0005

0.33200.0000-0.0012

tokyo-hyakkaten

0.00001.00000.0003 0.0007

0.24530.0000-0.0025

mercurys0.00001.00000.0008 0.0011

0.35940.0000-0.0012magimaguu

0.00001.00000.0020 0.0056

0.49460.0000-0.0020

ecdenxin

0.00001.00000.0003 0.0002

0.25850.0000-0.0012livinza

0.00001.00000.0001 0.0002

0.08290.0000-0.0026

exsmile0.00001.00000.0021 0.0033

0.47020.0000-0.0010japan-l0.00490.03070.0063 0.0168

0.54050.0000-0.0011ssn

0.00001.00000.0002 0.0000

0.17880.0000-0.0013

auc-bigmo

0.02830.14060.0036 0.0080

0.68150.0010-0.0018

okuwa

0.00720.10080.0022 0.0017

0.47600.0002-0.0011

cocoroco

0.00001.00000.0096 0.0274

0.62110.0000-0.0005

colecole

0.00001.00000.0037 0.0063

0.66810.0000-0.0008

basecamp

1.00000.0382

0.0614 0.1464

0.99940.00960.0117ksheep1

0.00001.00000.0018 0.0024

0.37830.0000-0.0011

toolex0.00290.15380.0222 0.0631

0.78730.00010.0003k-yokohama

0.00101.00000.0020 0.0031

0.45670.0003-0.0010

netonya

0.08490.11700.0080 0.0227

0.65510.0025-0.0007

wellness-web

0.00101.00000.0056 0.0100

0.46930.0003-0.0006

auc-pleasure0905

0.00001.00000.0032 0.0088

0.51830.0000-0.0017ichibankanshop

0.00001.00000.0013 0.0021

0.32820.0000-0.0011

fashion-kosen

0.01460.01320.0303 0.0870

0.66080.00000.0017

wide020.01750.05690.0074 0.0120

0.58750.0002-0.0005hab

0.47900.00820.3715 1.0000

0.96640.00101.0322neotec-bafa

0.00001.00000.0086 0.0125

0.49430.0000-0.0004

marumoto

0.02050.08280.0204 0.0557

0.70510.00040.0001yamalab

0.00001.00000.0020 0.0032

0.58590.0000-0.0010hows

0.02050.00170.0215 0.0263

0.54660.0000-0.0004

e-kitchen

0.00001.00000.0036 0.0050

0.42590.0000-0.0009

ap-zakka

0.02050.00300.0213 0.0264

0.54850.0000-0.0004brico

0.01960.12790.0058 0.0101

0.53330.0006-0.0006

nst21

0.00001.00000.0046 0.0103

0.51320.0000-0.0016

bungu-kaumall

0.01140.06550.0103 0.0101

0.49550.0002-0.0005

venusta

0.00001.00000.0002 0.0002

0.18540.0000-0.0026

sekichu

0.00001.00000.0049 0.0083

0.47740.0000-0.0007

pedalmania

0.00001.00000.0010 0.0027

0.34680.0000-0.0023

varietyshop

0.00001.00000.0010 0.0012

0.38750.0000-0.0012emishop

0.00001.00000.0016 0.0040

0.49630.0000-0.0022

sanbless

0.00001.00000.0025 0.0038

0.42790.0000-0.0010auc-power

0.00101.00000.0005 0.0013

0.31580.0002-0.0024

Table 7 Experimental results:

Degree of specialty based on“Kitchen & Dining”
Shop’s name

Detail U nif ormity

Cover

r-kojima

0.00001.00000.0027 0.0101

Rare

P recision C Score S Score
0.41760.0000-0.0044

ecojiji

0.01260.19070.0014 0.0058

0.52070.0006-0.0050

kaumall

0.09550.01010.0197 0.0782

0.58050.00020.0000

plywood

0.03600.02710.0059 0.0254

0.63720.0002-0.0026

auc-tokutoku

0.02340.03080.0026 0.0109

0.64390.0002-0.0043

good-choice

1.00000.03470.1691 0.7362

0.95180.00870.4608matsukiyo

0.00001.00000.0153 0.0611

0.62260.0000-0.0002

otakaratuuhan

0.00540.40000.0005 0.0020

0.39430.0005-0.0056

santecdirect

0.01260.02850.0072 0.0296

0.50710.0001-0.0021ouchimawari

0.01980.32660.0003 0.0010

0.44000.0016-0.0057

mercurys0.00001.00000.0006 0.0024

0.35940.0000-0.0055

ecdenxin

0.00541.00000.0325 0.1336

0.70480.00140.0037

exsmile0.04140.02860.0071 0.0308

0.80870.0003-0.0020ksheep1

0.20900.06630.0133 0.0513

0.64510.0035-0.0006

k-yokohama

0.06850.06830.0117 0.0464

0.74220.0012-0.0008

wellness-web

0.10090.03230.0324 0.1418

0.68320.00080.0047ichibankanshop

0.00181.00000.0005 0.0021

0.27250.0005-0.0055

comroad

0.09550.00960.0428 0.1754

0.65920.00020.0108

wide020.3081

0.04330.0134 0.0560

0.78330.0033-0.0004-0.0027

neotec-bafa

0.1171

0.05330.0065 0.0235

0.50360.0016echigoya-online-shop

0.15500.04360.0173 0.0739

0.75070.00170.0000brico

0.04860.10730.0040 0.0172

0.52790.0013-0.0035

sekichu

0.03420.03740.0129 0.0544

0.61650.0003-0.0004

narukiya

0.05230.04360.0149 0.0561

0.74270.0006-0.0003

neoleaf

0.25050.00420.2954 1.0000

0.82760.00030.9334varietyshop

0.00540.14290.0027 0.0104

0.54450.0002-0.0043

hb-navi

0.00001.00000.0002 0.0004

0.15160.0000-0.0058

nadeshico-ga

0.25950.05720.0202 0.0848

0.75270.00370.0001sanbless

0.11170.02090.0202 0.0856

0.70390.00060.0001richell

0.03060.03200.0113 0.0444

0.71750.0002-0.0009

livingearth

0.10630.28570.0012 0.0047

0.66030.0076-0.0051

sunruck-direct

0.10630.05820.0100 0.0403

0.66670.0015-0.0012katuhara

0.01620.12610.0020 0.0067

0.43970.0005-0.0049

aires

0.09190.09350.0139 0.0539

0.76290.0021-0.0004

ozshop0.00001.00000.0000 0.0000

0.00000.0000-0.0059

ripistation

0.09010.00960.0188 0.0745

0.57870.00020.0000

raison-store

0.2721

0.06900.0073 0.0300

0.83850.0047-0.0021

tokyo-hyakkaten

0.00001.00000.0001 0.0002

0.10560.0000-0.0058

joyfullmart

0.25950.03220.0101 0.0439

0.93640.0021-0.0010

magimaguu

0.00181.00000.0014 0.0057

0.48750.0005-0.0050

livinza

0.21440.04950.0170 0.0699

0.72840.00270.0000aandi

0.00540.00570.0165 0.0704

0.89840.00000.0000zakkashop

0.17120.05750.0043 0.0187

0.73350.0025-0.0033-0.0042cherrybell

0.06310.16260.0028 0.0118

0.67570.0026zaka-mmc

0.62880.06920.0329 0.1212

0.80530.01090.0024dan-dan

0.00001.00000.0038 0.0165

0.64390.0000-0.0036rgbw

0.18560.14360.0057 0.0229

0.64110.0067-0.0028

tubasa55

0.10630.14550.0032 0.0122

0.68030.0039-0.0041

shopmarna

0.04680.06900.0032 0.0134

0.75120.0008-0.0039

japan-l

0.04680.01110.0108 0.0434

0.66450.0001-0.0010