A Visualized Product Recommendation A Visualized Product Recommendation System using Fisheye Views and Data System using Fisheye Views and Data

AdjacencyAdjacency

A Visualized Product Recommendation A Visualized Product Recommendation System using Fisheye Views and Data System using Fisheye Views and Data

AdjacencyAdjacency

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IntroductionIntroductionIntroductionIntroduction

Recommendation SystemRecommendation System For web stores to gain customer loyalty, For web stores to gain customer loyalty,

sales, and advertisement profitsales, and advertisement profit For consumers to search products For consumers to search products

effectivelyeffectively

Research PurposeResearch Purpose We present a visualized product We present a visualized product

recommendation system based on data recommendation system based on data adjacency theory and fisheye views. adjacency theory and fisheye views.

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Literature ReviewLiterature ReviewLiterature ReviewLiterature Review

Web miningWeb mining Web structure mining: summary of web structure. Web structure mining: summary of web structure. Web content mining: extract information of meaning and Web content mining: extract information of meaning and

details in the web. details in the web. Web usage mining: web page reconstruction, discrimination, Web usage mining: web page reconstruction, discrimination,

and finding navigation patterns. and finding navigation patterns. Personalized Recommendation Systems Personalized Recommendation Systems

designed for a specific consumer or groupdesigned for a specific consumer or group gives products or related information for a customer based on gives products or related information for a customer based on

demographical data, transaction data, and web log datademographical data, transaction data, and web log data contend based filtering, collaborative filtering, rule based contend based filtering, collaborative filtering, rule based

filtering, and web usage miningfiltering, and web usage mining

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Literature Review(cont’d)Literature Review(cont’d)Literature Review(cont’d)Literature Review(cont’d)

VisualizationVisualization A technique that represents the information in a visual formA technique that represents the information in a visual form provides users with easy understanding of the data in a short provides users with easy understanding of the data in a short

timetime ““Fisheye Views” represents “local detail” and “global Fisheye Views” represents “local detail” and “global

context” differently. context” differently.

Data Adjacency and Adjacency MatrixData Adjacency and Adjacency Matrix It shows that whether itemIt shows that whether item i i and item and item jj are co-purchased, or are co-purchased, or

the purchase of item the purchase of item ii results in that of item results in that of item jj.. Based on graph theory, if two points are linked by a line, the Based on graph theory, if two points are linked by a line, the

relationship is represented by 1, and 0 if they are not linked, relationship is represented by 1, and 0 if they are not linked, in an adjacency matrix.in an adjacency matrix.

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Literature Review(cont’d)Literature Review(cont’d)Literature Review(cont’d)Literature Review(cont’d)

Fisheye ViewsFisheye Views DOIDOIfisheyefisheye(x, y) = API(x) – D(x,y)(x, y) = API(x) – D(x,y)

FisheyeViews

Calendar

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Fisheye Views and Data AdjacencyFisheye Views and Data AdjacencyFisheye Views and Data AdjacencyFisheye Views and Data Adjacency

Data Acquisition and TransformationData Acquisition and Transformation The data set is collected from an internet shopping The data set is collected from an internet shopping

site which sells computers and computer-related site which sells computers and computer-related items.items.

As web log data has lot of information including As web log data has lot of information including date, IP address, server name, and time, it is date, IP address, server name, and time, it is important to refine the dataset on proper purposes.important to refine the dataset on proper purposes.

All products in the company are assigned new All products in the company are assigned new serial numbersserial numbers

ex. P1, P2, … , Pnex. P1, P2, … , Pn

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Fisheye Views and Data Fisheye Views and Data Adjacency(cont’d)Adjacency(cont’d)Fisheye Views and Data Fisheye Views and Data Adjacency(cont’d)Adjacency(cont’d) A graph consists of A graph consists of

two components, two components, vertex and arc.vertex and arc.

An adjacency matrixAn adjacency matrix

AA BB CC DD EE

AA 00 11 11 11 11

BB 11 00 11 11 00

CC 11 11 00 00 11

DD 11 11 00 00 11

EE 11 00 11 11 00

A

B C

D E

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Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d)Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d) CFM(connection frequency matrix) of 7 productsCFM(connection frequency matrix) of 7 products ThisThis shows that customers moved from P1 to P2 45 shows that customers moved from P1 to P2 45

times. However, it also shows that customers times. However, it also shows that customers moved from P2 to P1 only three timesmoved from P2 to P1 only three times

P1 P2 P3 P4 P5 P6 P7 Sum

P1 - 45 46 25 9 1 1 127

P2 3 - 1 52 47 45 1 149

P3 24 45 - 38 22 6 87 222

P4 47 91 44 - 106 33 44 365

P5 57 65 37 40 - 37 12 248

P6 41 27 45 37 35 - 9 194

P7 54 51 16 4 5 4 - 134

Sum 226 324 189 196 224 126 154 1439

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Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d)Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d) A View on TreemapA View on Treemap

n

k

Pik

PijPijW

1

)(

P1 P2 P3 P4 P5 P6 P7 Sum

P1 - 0.35 0.36 0.2 0.07 0.01 0.01 1

P2 0.02 - 0.01 0.35 0.32 0.3 0.01 1

P3 0.11 0.2 - 0.17 0.1 0.03 0.39 1

P4 0.13 0.25 0.12 - 0.29 0.09 0.12 1

P5 0.23 0.26 0.15 0.16 - 0.15 0.05 1

P6 0.21 0.14 0.23 0.19 0.18 - 0.05 1

P7 0.4 0.38 0.12 0.03 0.04 0.03 - 1

Sum 1.1 1.59 0.99 1.1 0.99 0.61 0.62 7

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Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d)Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d) For example, when a user is viewing P3, the weight For example, when a user is viewing P3, the weight

on P4 can be calculated as on P4 can be calculated as WW(P34) = {38 / (24+45+38+22+6+87)} = 38/222 = 0.17(P34) = {38 / (24+45+38+22+6+87)} = 38/222 = 0.17

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Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d)Fisheye Views and Data Fisheye Views and Data Adjacency(Cont’d)Adjacency(Cont’d)

Share change

by the productIn a web page

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TestTestTestTest

Comparison with the method by Comparison with the method by association rulesassociation rules Association rulesAssociation rulesRuleNo.

Support(%)

Confidence(%)

Lift Rule and its content

1 19 62 2P479 P477(Samsung Laptop LG-IBM

Laptop)

2 16 63 2P479 P439(Sony Laptop Dell Desktop)

3 11 57 2P479 P446(USB HDD Samsung Laptop)

4 10 64 1.5P479 P102(MP3 Player Digital

Camera)

5 10 51 1.5P479 P465(Digital Camera Cell Phone)

… … … … …

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Test(cont’d)Test(cont’d)Test(cont’d)Test(cont’d)

Group A: Group A: Web Page based on Association RulesWeb Page based on Association Rules

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Test(cont’d)Test(cont’d)Test(cont’d)Test(cont’d)

Group B: Group B: Web Page based on CFMWeb Page based on CFM

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Test(cont’d)Test(cont’d)Test(cont’d)Test(cont’d)

ResultsResultsMeasure

Subjects

Groups

No. ofSampl

eMean

Std.Dev.

ANOVA(F-value)

Sig.

Loyalty

UserSatisfaction

A 160 3.21 0.56

24.362* .000B 160 3.58 0.38

Total 320 3.35

Intentionto Re-visit

A 160 3.40 0.75

12.822* .000B 160 3.67 0.54

Total 320 3.54

Intentionto

Purchase

A 160 3.20 0.77

38.636* .000B 160 3.68 0.63

Total 320 3.44

* Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

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Test(cont’d)Test(cont’d)Test(cont’d)Test(cont’d)

ResultsResultsMeasure

Subjects

Groups

No. ofSampl

eMean

Std.Dev.

ANOVA(F-value)

Sig.

WebUsabilit

y

SystemQuality

A 160 3.66 0.58

18.033* .000B 160 3.87 0.64

Total 320 3.77

Information

Quality

A 160 3.27 0.63

22.874* .000B 160 3.55 0.60

Total 320 3.41

ServiceQuality

A 160 3.52 0.62

11.276* .004B 160 3.67 0.67

Total 320 3.60

* Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

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ConclusionsConclusionsConclusionsConclusions

Based on data adjacency and fisheye views, it Based on data adjacency and fisheye views, it is compared with a recommendation system is compared with a recommendation system based on association rules. based on association rules. The suggested method has high performance. The suggested method has high performance. Analysis from the tests confirms that it has greater Analysis from the tests confirms that it has greater

loyalty and web usability compared to the other loyalty and web usability compared to the other system.system.

LimitationsLimitations Our method should be compared with more diverse Our method should be compared with more diverse

methods in the literature of product methods in the literature of product recommendation systems.recommendation systems.