1. A Visualized Product Recommendation System using Fisheye Views and Data Adjacency

2. Informs Hong Kong 2006 1/12 IntroductionIntroduction Recommendation System Recommendation System For web stores to gain customer loyalty, sales, and advertisement profit For web stores to gain customer loyalty, sales, and advertisement profit For consumers to search products effectively For consumers to search products effectively Research Purpose Research Purpose We present a visualized product recommendation system based on data adjacency theory and fisheye views. We present a visualized product recommendation system based on data adjacency theory and fisheye views.

3. Informs Hong Kong 2006 2/12 Literature Review Web mining Web mining Web structure mining: summary of web structure. Web structure mining: summary of web structure. Web content mining: extract information of meaning and details in the web. Web content mining: extract information of meaning and details in the web. Web usage mining: web page reconstruction, discrimination, and finding navigation patterns. Web usage mining: web page reconstruction, discrimination, and finding navigation patterns. Personalized Recommendation Systems Personalized Recommendation Systems designed for a specific consumer or group designed for a specific consumer or group gives products or related information for a customer based on demographical data, transaction data, and web log data gives products or related information for a customer based on demographical data, transaction data, and web log data contend based filtering, collaborative filtering, rule based filtering, and web usage mining contend based filtering, collaborative filtering, rule based filtering, and web usage mining

4. Informs Hong Kong 2006 3/12 Literature Review(cont’d) Visualization Visualization A technique that represents the information in a visual form A technique that represents the information in a visual form provides users with easy understanding of the data in a short time provides users with easy understanding of the data in a short time “Fisheye Views” represents “local detail” and “global context” differently. “Fisheye Views” represents “local detail” and “global context” differently. Data Adjacency and Adjacency Matrix Data Adjacency and Adjacency Matrix It shows that whether item i and item j are co-purchased, or the purchase of item i results in that of item j. It shows that whether item i and item j are co-purchased, or the purchase of item i results in that of item j. 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, in an adjacency matrix. 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, in an adjacency matrix.

5. Informs Hong Kong 2006 4/12 Literature Review(cont’d) Fisheye Views Fisheye Views DOI fisheye (x, y) = API(x) – D(x,y) DOI fisheye (x, y) = API(x) – D(x,y) Fisheye Views Calendar

6. Informs Hong Kong 2006 5/12 Fisheye Views and Data Adjacency Data Acquisition and Transformation Data Acquisition and Transformation The data set is collected from an internet shopping site which sells computers and computer-related items. The data set is collected from an internet shopping site which sells computers and computer-related items. As web log data has lot of information including date, IP address, server name, and time, it is important to refine the dataset on proper purposes. As web log data has lot of information including date, IP address, server name, and time, it is important to refine the dataset on proper purposes. All products in the company are assigned new serial numbers All products in the company are assigned new serial numbers ex. P1, P2, …, Pn ex. P1, P2, …, Pn

7. Informs Hong Kong 2006 6/12 Fisheye Views and Data Adjacency(cont’d) A graph consists of two components, vertex and arc. A graph consists of two components, vertex and arc. An adjacency matrix An adjacency matrixABCDEA01111 B10110 C11001 D11001 E10110

8. Informs Hong Kong 2006 7/12 Fisheye Views and Data Adjacency(Cont’d) CFM(connection frequency matrix) of 7 products CFM(connection frequency matrix) of 7 products This shows that customers moved from P1 to P2 45 times. However, it also shows that customers moved from P2 to P1 only three times This shows that customers moved from P1 to P2 45 times. However, it also shows that customers moved from P2 to P1 only three times P1P1 P2P2 P3P3 P4P4 P5P5 P6P6 P7P7 Sum P1P1 -454625911127 P2P2 3-15247451149 P3P3 2445-3822687222 P4P4 479144-1063344365 P5P5 57653740-3712248 P6P6 4127453735-9194 P7P7 545116454-134 Sum2263241891962241261541439

9. Informs Hong Kong 2006 8/12 Fisheye Views and Data Adjacency(Cont’d) A View on Treemap A View on Treemap P1P1 P2P2 P3P3 P4P4 P5P5 P6P6 P7P7 Sum P1P1 -0.350.360.20.070.01 1 P2P2 0.02-0.010.350.320.30.011 P3P3 0.110.2-0.170.10.030.391 P4P4 0.130.250.12-0.290.090.121 P5P5 0.230.260.150.16-0.150.051 P6P6 0.210.140.230.190.18-0.051 P7P7 0.40.380.120.030.040.03-1 Sum1.11.590.991.10.990.610.627

10. Informs Hong Kong 2006 9/12 Fisheye Views and Data Adjacency(Cont’d) For example, when a user is viewing P3, the weight on P4 can be calculated as For example, when a user is viewing P3, the weight on P4 can be calculated as W(P34) = {38 / (24+45+38+22+6+87)} = 38/222 = 0.17 W(P34) = {38 / (24+45+38+22+6+87)} = 38/222 = 0.17

11. Informs Hong Kong 2006 10/12 Fisheye Views and Data Adjacency(Cont’d) Share change by the product In a web page

12. Informs Hong Kong 2006 11/12 TestTest Comparison with the method by association rules Comparison with the method by association rules Association rules Association rules Rule No. Support (%) Confidence (%) LiftRule and its content 119622 P479  P477 (Samsung Laptop  LG-IBM Laptop) 216632 P479  P439 (Sony Laptop  Dell Desktop) 311572 P479  P446 (USB HDD  Samsung Laptop) 410641.5 P479  P102 (MP3 Player  Digital Camera) 510511.5 P479  P465 (Digital Camera  Cell Phone) ……………

13. Informs Hong Kong 2006 12/12 Test(cont’d)Test(cont’d) Group A: Web Page based on Association Rules Group A: Web Page based on Association Rules

14. Informs Hong Kong 2006 13/12 Test(cont’d)Test(cont’d) Group B: Web Page based on CFM Group B: Web Page based on CFM

15. Informs Hong Kong 2006 14/12 Test(cont’d)Test(cont’d) Results Results Measure Subjects Groups No. of Sample Mean Std. Dev. ANOVA (F-value) Sig. Loyalty User Satisfaction A1603.210.56 24.362 *.000 B1603.580.38 Total3203.35 Intention to Re-visit A1603.400.75 12.822 *.000 B1603.670.54 Total3203.54 Intention to Purchase A1603.200.77 38.636 *.000 B1603.680.63 Total3203.44 * Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

16. Informs Hong Kong 2006 15/12 Test(cont’d)Test(cont’d) Results Results Measure Subjects Groups No. of Sample Mean Std. Dev. ANOVA (F-value) Sig. Web Usability System Quality A1603.660.58 18.033 *.000 B1603.870.64 Total3203.77 Information Quality A1603.270.63 22.874 *.000 B1603.550.60 Total3203.41 Service Quality A1603.520.62 11.276 *.004 B1603.670.67 Total3203.60 * Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

17. Informs Hong Kong 2006 16/12 ConclusionsConclusions Based on data adjacency and fisheye views, it is compared with a recommendation system based on association rules. Based on data adjacency and fisheye views, it is compared with a recommendation system 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 loyalty and web usability compared to the other system. Analysis from the tests confirms that it has greater loyalty and web usability compared to the other system. Limitations Limitations Our method should be compared with more diverse methods in the literature of product recommendation systems. Our method should be compared with more diverse methods in the literature of product recommendation systems.