2010, 28, 45-60

99 3 17 99 6 30

1 2

(association rules)

1 2

46

Using Association Rules to Construct Website Recommendations

of Query Keywords

Chui-cheng Chen* Tsung-yi Chen**

Abstract

The website search engine is one of the most popular services in electronic commerce business models. This paper uses browsing data as the source data of mining, and a browsing data contains query keywords and browse websites in search engine. The association rule is used to find the adaptive website recommendations of query keywords from two aspects. One is to propose a fast algorithm to mine association rules between query keywords and browse websites. The other is to use some query keywords as the target of mining, and to modify the previous algorithm to mine association rules as those antecedents are the query keywords. The adaptive website recommendations with rank by the confidence of the association rules can be provided for the query keywords in search engine. A mining system for adaptive website recommendations of query keywords is designed and constructed according to both algorithms, and the performances of both algorithms are evaluated.

Keywords: electronic commerce, association rules, query keywords, search engine, website

recommendations

* Associate Professor, Department of Information Management, Southern Taiwan University. ** Assistant Professor, Department of Electronic Commerce Management, Nanhua University.

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(Internet)(World Wide Web)(search engine) Yahoo(http://www.yahoo.com) Google (http://www.google.com)

(data mining)

(Han & Kamber, 2006)

(association rules)

1.

2.

(sequence)(clustering)(classification)(forecasting)(Han & Kamber, 2006Chen, Han & Yu, 1996)

48

(2007)(2002)

(2002)(2002)(2002)(2002)(2007)Hosseini Abolhassani(2007)Huang, Lee Lin(2001)

Li, ChenYang(2002)Fonseca, Golgher, de Moura, Ziviani(2003)Li, Y. Li, G. Y.(2007) (Ontology)Nettleton, Calderon-Benavides Baeza-ates(2006)

Agrawal, Imielinski Swami (1993)

(itemsets)(items) XY XY XY= X (antecedent)Y(consequent) XY (support)(XY)(confidence)(XY) X (minimum support)(minimum confidence) XY

k k1 k-(frequent temsets) k k- frequentk ABC ABC Agrawal Srikant(1994)Han, Pei, Yin Mao(2004)Holt Chung(2000)Li, He Lei(2005)Tsay Chang- Chien(2004)

Apriori (Agrawal & Srikant, 1994) Apriori

1. frequentk-1k>1

49

2. (1) k-2 frequentk-1 itemsetk 3. (2) itemsetk itemsetk-1 (1) itemsetk

4. (3) itemsetk frequentk

5. frequentk 6. (1) frequentk+1 (4) itemsetk

frequentkTsay Chang-Chien(2004) Apriori itemsetkk1 k CDAR

(1) I={i1, i2, , ia} a (2) J={j1, j2, , jb} b (3) T={T1, T2, , Tk, Tm} m Tk

k 1km (4) Tk Tk=[X, Y]XIX

YJY X Y X1 or X2 oror Xd d Y [X1, Y], [X2, Y],, [Xd, Y] d

andor A and B or CABC A and B C

andand A and B AB

50

Apriori

Apriori AKW(mining association rules between keywords and websites)

AKW 1. D1 frequent1 2. (1) frequent1 frequent1 itemset2 D1 itemset2 frequent2 D1 itemset2 3 D2(3)

3. frequentk-1k>2 Dk-1 4. (3) k-2 frequentk-1 itemsetk 5. (4) itemsetk itemsetk-1 (3)() itemsetk

6. Dk-1(5) itemsetk frequentk Dk-1 itemsetk k+1 Dk

7. frequentk XYXY=frequentkXIYJ

8. (3) frequentk+1 XY

X Y XY X Y n Yi1in Y1Y2Yn

Apriori itemsetkk>1 frequentk itemsetk k+1

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itemsetk+1 frequentk+1 AKW Apriori

1 5 {T1, T2, T3, T4, T5} I={Q, R, S, T, U, V, W, X, Y, Z }J={A, B, C, D, E, F, G, H, I. J} 60%( 3) 60%

1

T1 V, W, X, Y, Z A, B, C, G

T2 S, Q, R, Z A, C, E, F

T3 Q, S, T, X H, I, J

T4 R, U, W, X, Y, Z C, D, F, H, I

T5 Q, T, V, Z A, B, C, F, G, J

frequent1

itemset1 itemset1 itemset1 frequent1

A Q A 3 Q 3 A 3

B R B 2 R 2 C 4

C S C 4 S 2 F 3

D T D 1 T 2 Q 3

E U

D1 E 1 U 1

3

X 3

F V F 3 V 2 Z 4

G W G 2 W 2

H X H 2 X 3

I Y I 2 Y 2

J Z J 2 Z 4

52

D2 itemset2 frequent2

AQ 2 AZ 3 T1 V, W, X, Y, Z A, B, C, G

AX 1 CX 3 T2 S, Q, R, Z A, C, E, F

AZ 3 CZ 4 T4 R, U, W, X, Y, Z C, D, F, H, I

CQ 2

3

FZ 3 T5 Q, T, V, Z A, B, C, F, G, J

CX 3

CZ 4

itemset2

FQ 2

FX 1

FZ 3 D3

itemset3 frequent2

ACZ 3 ACZ 3 T1 V, W, X, Y, Z A, B, C, G

AFZ 2 CFZ 3 T2 S, Q, R, Z A, C, E, F

CXZ 2 T4 R, U, W, X, Y, Z C, D, F, H, I

CFZ 3

3

itemset3

T5 Q, T, V, Z A, B, C, F, G, J

itemset4 ACFZ(5) ACZ- ZAC 3/4=75%

Z A C

andorX and Y or ZX, Y, Z X and YZ

andandX and YXY

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XX

XYXIYJXY

X Y Y X ii1 AKW AAK (mining association rules those antecedents are the query keywords) X

1. D1 frequent1 X D2

2. (1) X frequent1 itemseti+1 D2itemseti+1 frequenti+1 D2 itemseti+1 i+2 D3

3. frequenti+k-1k>1 Dk+1 4. (3) i+k-2 frequenti+k-1 itemseti+k 5. (4) itemseti+k itemseti+k-1

(3)( X) itemseti+k 6. Dk+1(5) itemseti+k frequentk Dk+1 itemseti+k i+k+1 Dk+2

7. frequentk XYXY=frequentkYJ

8. (3) frequenti+k+1 XY

X Y XY X Y n Yj1jn Y1Y2Yn

X i X frequent1 X itemseti+kk>1 frequenti+k itemseti+k i+k+1 AAK

54

X

1 Z 60%( 3)60%Z

itemset1 itemset1 frequent1

A A 3 A 3

B B 2 C 4

C C 4 F 3

D D 1

E

D1

E 1

3

F F 3

G G 2

H H 2

I I 2

J J 2

D 2

itemset2

T1 V, W, X , Y, Z A, B , C, G AZ 3

T2 S, Q, R, Z A, C, E, F CZ 3

T4 R, U , W, X , Y, Z C, D, F, H , I FZ 3

Z

T5 Q, T, V, Z A, B , C, F, G, J

D 3

frequent2

AZ 3 T1 V, W, X, Y, Z A, B, C, G

CZ 3 T2 S, Q, R, Z A, C, E, F

FZ 3 T4 R, U, W, X, Y, Z C, D, F, H, I

3

itemset3

T5 Q, T, V, Z A, B, C, F, G, J

D4 itemset3 frequent3

ACZ 3 ACZ 3 T1 V, W, X, Y, Z A, B, C, G

AFZ 2 CFZ 3 T2 S, Q, R, Z A, C, E, F

CFZ 3 T4 R, U, W, X, Y, Z C, D, F, H, I

3

itemset3

T5 Q, T, V, Z A, B, C, F, G, J

itemset4 ACFZ(5)

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ACZ- ZAC 3/4=75% Z A C X

1

(1) (2)

(2)

(1)

1

C# IBM Data Mining (http://www.almaden.ibm.com/) 50000 D1 D2 D1 D2 2n ntran np tl pl D1 D2 50000 D3

2

n ntran np tl pl

D 1 1000 50000 10000 10 4

D 2 1000 50000 10000 10 4

D 3 D 1 D 2 50000

D1 k1, k2, k3, , k1000 D2

56

w1, w2, w3,, w1000 Access 2003 D3

2

( 1.5%)( 70%)

2

3 ( k1 and k5 )

3

2 3

3

3

CPU CPU-Pentium IV 2.4G Hz

Main memory 256 Mbytes

Windows XP Professional

C#

Access 2003

70% Apriori

57

CDAR AKW 4 4 AKW Apriori CDAR

:50000

0

20

40

60

80

100

120

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

AKW

CDAR

Apriori

4 Apriori CDAR AKW

70% AKW AAK () 10 5 5 AAK AKW

:50000

0

10

20

30

40

50

60

70

80

90

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

0

2

4

6

8

10

12

14

16

18

AKW

AAK

5 AKW AAK

58

4 Apriori CDAR Apriori CDAR

4 Apriori CDAR AKW AAK

itemsetk

k>1

k itemsetkk>1

itemsetk

k>1 itemsetk

k+1

itemseti+k i

k>1

itemseti+k i+k+1

1. 2. Huang, Lee Lin(2001)(feedback)

3. 4.

59

(2002)

(2007)

(2002)

(2002)

(2002)

(2002)

(2007)

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